Browsing by Autor "Ervan Rutishauser"

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Author response: Carbon recovery dynamics following disturbance by selective logging in Amazonian forests
(2016) Camille Piponiot; Plínio Sist; Lucas Mazzei; Marielos Peña‐Claros; Francis E. Putz; Ervan Rutishauser; Alexander Shenkin; Nataly Ascarrunz; C. P. de Azevedo; Christopher Baraloto
Full text Figures and data Side by side Abstract eLife digest Introduction Results Discussion Materials and methods Appendix Data availability References Decision letter Author response Article and author information Metrics Abstract When 2 Mha of Amazonian forests are disturbed by selective logging each year, more than 90 Tg of carbon (C) is emitted to the atmosphere. Emissions are then counterbalanced by forest regrowth. With an original modelling approach, calibrated on a network of 133 permanent forest plots (175 ha total) across Amazonia, we link regional differences in climate, soil and initial biomass with survivors’ and recruits’ C fluxes to provide Amazon-wide predictions of post-logging C recovery. We show that net aboveground C recovery over 10 years is higher in the Guiana Shield and in the west (21 ±3 Mg C ha-1) than in the south (12 ±3 Mg C ha-1) where environmental stress is high (low rainfall, high seasonality). We highlight the key role of survivors in the forest regrowth and elaborate a comprehensive map of post-disturbance C recovery potential in Amazonia. https://doi.org/10.7554/eLife.21394.001 eLife digest The Amazon rainforest in South America is the largest tropical forest in the world. Along with being home to a huge variety of plants and wildlife, rainforests also play an important role in storing an element called carbon, which is a core component of all life on Earth. Certain forms of carbon, such as the gas carbon dioxide, contribute to climate change so researchers want to understand what factors affect how much carbon is stored in rainforests. Trees and other plants absorb carbon dioxide from the atmosphere and then incorporate the carbon into carbohydrates and other biological molecules. The Amazon rainforest alone holds around 30% of the total carbon stored in land-based ecosystems. Humans selectively harvest certain species of tree that produce wood with commercial value from the Amazon rainforest. This “selective logging” results in the loss of stored carbon from the rainforest, but the loss can be compensated for in the medium to long term if the forest is left to regrow. New trees and trees that survived the logging grow to fill the gaps left by the felled trees. However, it is not clear how differences in the forest (for example, forest maturity), environmental factors (such as climate or soil) and the degree of the disturbance caused by the logging affect the ability of the forest ecosystem to recover the lost carbon. Piponiot et al. used computer modeling to analyze data from over a hundred different forest plots across the Amazon rainforest. The models show that the forest’s ability to recover carbon after selective logging greatly differs between regions. For example, the overall amount of carbon recovered in the first ten years is predicted to be higher in a region in the north known as the Guiana Shield than in the south of the Amazonian basin where the climate is less favorable. The findings of Piponiot et al. highlight the key role the trees that survive selective logging play in carbon recovery. The next step would be to couple this model to historical maps of logging to estimate how the areas of the rainforest that are managed by selective logging shape the overall carbon balance of the Amazon rainforest. https://doi.org/10.7554/eLife.21394.002 Introduction With on-going climate change, attention is increasingly drawn to the impacts of human activities on carbon (C) cycles (Griggs and Noguer, 2002), and in particular to the 2.1 ± 1.1 Pg C yr-1 of C loss caused by various forms and intensities of anthropogenic disturbances in tropical forests (Grace et al., 2014). Among those disturbances, selective logging, i.e. the selective harvest of a few merchantable tree species, is particularly widespread: in the Brazilian Amazon alone, about 2 Mha yr−1 were logged in 1999–2002 (Asner et al., 2005). The extent of selective logging in the Brasilian Amazon was equivalent to annual deforestation in the same period, and resulted in C emissions of 90 Tg C yr-1 (Huang and Asner, 2010) which increased anthropogenic C emissions by almost 25% over deforestation alone (Asner et al., 2005). In contrast to deforested areas that are used for agriculture and grazing, most selectively logged forests remain as forested areas (Asner et al., 2006) and may recover C stocks (West et al., 2014). Previously logged Amazonian forests may thus accumulate large amounts of C (Pan et al., 2011), but this C uptake is difficult to accurately estimate, because while detecting selective logging from space is increasingly feasible (Frolking et al., 2009) (even if very few of the IPCC models effectively account for logging), directly quantifying forest recovery remains challenging (Asner et al., 2009; Houghton et al., 2012; Goetz et al., 2015). Studies based on field measurements (e.g. Sist and Ferreira, 2007; Blanc et al., 2009; West et al., 2014; Vidal et al., 2016), sometimes coupled with modeling approaches (e.g. Gourlet-Fleury et al., 2005; Valle et al., 2007) or airborne light detection and ranging (LiDAR) measurements (e.g. Andersen et al., 2014) have assessed post-logging dynamics at particular sites. Nonetheless, to our knowledge no spatially-explicit investigation of post-logging C dynamics at the Amazon biome scale is available. C losses from selective logging are determined by harvest intensity (i.e. number of trees felled or volume of wood extracted) plus the care with which harvest operations are conducted, which affects the amount of collateral damage. After logging, C losses continue for several years due to elevated mortality rates of trees injured during harvesting operations (Shenkin et al., 2015). Logged forests may recover their aboveground carbon stocks (ACS) via enhanced growth of survivors and recruited trees (Blanc et al., 2009). Full recovery of pre-disturbance ACS in logged stands reportedly requires up to 125 years, depending primarily on disturbance intensity (Rutishauser et al., 2015). The underlying recovery processes (i.e. tree mortality, growth and recruitment) are likely to vary with the clear geographical patterns in forest structure and dynamics across the Amazon Basin and Guiana Shield. In particular, northeast-southwest gradients have been reported for ACS (Malhi and Wright, 2004), net primary productivity (Aragão et al., 2009), wood density (Baker et al., 2004), and floristic composition (ter Steege et al., 2006). Such gradients coincide with climate and edaphic conditions that range from nearly a seasonal nutrient-limited in the northeast to seasonally dry and nutrient-rich in the southwest (Quesada et al., 2012). These regional differences in biotic and abiotic conditions largely constrain demographic processes that ultimately shape forest C balances. Here we partition the contributions to post-disturbance ACS gain (from growth and recruitment of trees ≥20 cm DBH) and ACS loss (from mortality) of survivors and recruited trees to detect the main drivers and patterns of ACS recovery in forests disturbed by selective logging across Amazonia sensu lato (that includes the Amazon Basin and the Guiana Shield). Based on long-term (8–30 year) inventory data from 13 experimentally-disturbed sites (Sist et al., 2015) across Amazonia (Figure 1—figure supplement 1), 133 permanent forest plots (175 ha in total) that cover a large gradient of disturbance intensities (ACS losses ranging from 1% to 71%) were used to model the trajectory of those post-disturbance ACS changes (Figure 1) in a comprehensive Bayesian framework. We quantify the effect of pre-disturbance ecosystem characteristics [the site’s average pre-logging ACS (a⁢c⁢s⁢0) and the relative difference between each plot and a⁢c⁢s⁢0 as a proxy of forest maturity (d⁢a⁢c⁢s)], disturbance intensity [percentage of pre-logging ACS lost (l⁢o⁢s⁢s)], and interactions with the environment [annual precipitation (p⁢r⁢e⁢c), seasonality of precipitation (s⁢e⁢a⁢s), and soil bulk density (b⁢d)] (Figure 2) on the rates at which post-disturbance ACS changes converge to a theoretical steady state (as in Figure 1, see Materials and methods for more details). With global maps of ACS (Avitabile et al., 2016), climatic conditions (Hijmans et al., 2005) and soil bulk density (Nachtergaele et al., 2008), we up-scale our results to Amazonia (sensu lato) and elaborate predictive maps of potential ACS changes over 10 years under the hypothesis of a 40% ACS loss, which is a common disturbance intensity after conventional logging in Amazonia (Blanc et al., 2009; Martin et al., 2015; West et al., 2014). Summing these ACS changes over time gives the net post-disturbance rate of ACS accumulation. Disentangling ACS recovery into demographic processes and cohorts is essential to reveal mechanisms underlying ACS responses to disturbance and to make more robust predictions of ACS recovery compared to an all-in-one approach (see Appendix). Figure 1 with 1 supplement see all Download asset Open asset 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 2 with 1 supplement see all Download asset Open asset 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 (l⁢o⁢s⁢s) , i.e. the proportion of initial ACS loss; mean site’s ACS (a⁢c⁢s⁢0), and relative forest maturity, i.e. pre-logging plot ACS as a % of a⁢c⁢s⁢0 (d⁢a⁢c⁢s); annual precipitation (p⁢r⁢e⁢c); seasonality of precipitation (s⁢e⁢a⁢s), soil bulk density (b⁢d). 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 Download elife-21394-fig2-data1-v2.xlsx Results Local variations of ACS changes At a given site, variations of post-logging ACS changes are explained with the disturbance intensity (l⁢o⁢s⁢s) and the relative forest maturity (d⁢a⁢c⁢s). At high disturbance intensity (positive l⁢o⁢s⁢s) as well as in relatively immature forests (negative d⁢a⁢c⁢s), ACS gain from recruits is high: recruitment decreases slowly (Figure 2b and Figure 3b) and recruits’ growth increases rapidly (Figure 2c and Figure 3c). In the same conditions of high disturbance intensity, survivors’ ACS growth is lower in the first years following logging than for low disturbance intensities, but declines slowly (Figure 2a and Figure 3a). Disturbance intensity and relative forest maturity have a weak effect on ACS loss from both survivors and recruits (Figures 2d,e and 3d,e). Overall, net ACS change stays high longer at high disturbance intensity (Figure 3f). Figure 3 Download asset Open asset 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 l⁢o⁢s⁢s) 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 Regional variations of ACS changes Variations of post-logging ACS changes between sites are explained with the mean ACS of each site (a⁢c⁢s⁢0), climatic conditions [annual precipitation (p⁢r⁢e⁢c), seasonality of precipitation (s⁢e⁢a⁢s)] and the soil bulk density (b⁢d). Contribution of survivors’ growth to ACS recovery declined slowly in sites with low a⁢c⁢s⁢0 and high water stress (low precipitation, high seasonality and high bulk density) (Figure 2a). Survivors’ ACS loss showed the opposite pattern (Figure 2d) except in apparent response to high seasonality of precipitation (s⁢e⁢a⁢s) that slowed the post-disturbance rates of decline of both ACS growth and loss. Despite slower recruits’ ACS growth in sites with high pre-logging ACS (a⁢c⁢s⁢0), no other regional covariate had significant effects on recruits’ ACS changes (Figure 2b,c and e). Prediction maps While no significant environmental effects were detected for recruits’ ACS changes (Figures 2 and 4), the survivors showed a highly structured regional gradient: (i) ACS gain from survivors’ ACS growth is high in the west and in the Guiana Shield, but low in the south (Figure 4a), whereas (ii) survivors’ ACS loss is low in the south and in the Guiana Shield but high in the west (Figure 4d). To illustrate how these regional differences will be critical for future ACS across Amazonia, we developed a map of net ACS recovery over the first 10 years after a 40% ACS loss by integrating the sum of ACS change predictions through time (Figure 5). Across the region, net ACS recovery over the first ten years after a 40% ACS loss is predicted to be 17 ± 7 Mg C ha-1, with higher values in the west and in the Guiana Shield (Figure 5a). The uncertainty in predictions was low to medium (coefficient of variation under 40%) in 82% of the mapped area, and high (coefficient of variation above 50%) in 5% of the mapped area (Figure 5b). Figure 4 Download asset Open asset Predicted cumulative ACS changes (Mg C ha−1) 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 Figure 5 Download asset Open asset 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 Four areas (Figure 5a) were selected to represent four contrasted cases of net ACS recovery in time (Figure 6): two areas, northwestern Amazonia and the Guiana Shield, with high ACS accumulation (21 ± 3 Mg C ha-1 over 10 year), one intermediate area, central Amazonia (15 ± 1 Mg C ha-1 over 10 year) and one area with low ACS accumulation, southern Amazonia (12 ± 3 Mg C ha-1 over 10 year). Survivors’ contribution to the sum of ACS gains (recruitment and growth) over the first 10 years after disturbance was 71 ± 4% in the Guiana Shield, 71 ± 2% in the west; 63 ± 4% in central Amazonia and 55 ± 6% in the south. Predicted net ACS recovery (Figure 5) and survivors’ ACS growth (Figure 4a) are highly correlated: ρ=0.90 (Pearson’s correlation coefficient). Figure 6 Download asset Open asset 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 Discussion Contrasting post-disturbance ACS dynamics were detected among the western Amazon, Guiana Shield, and southern Amazon (Figure 4). (i) In the western Amazon, environmental stress is reduced due to fertile soils and abundant, mostly non-seasonal precipitation, but forests are prone to frequent and sometimes large-scale wind-induced disturbances (Espírito-Santo et al., 2014). Such conditions of low stress and high disturbance tend to favor fast-growing species with rapid life cycles (He et al., 2013), which results in fast ACS gain and loss from survivors even after the logging disturbance (Figures 4a,d and 6). (ii) Forests of the Guiana Shield are generally dense and grow on nutrient-poor soils (Quesada et al., 2012), where wood productivity is highly constrained by competition for key nutrients, especially phosphorus and nitrogen (Santiago, 2015; Mercado et al., 2011). The short duration pulse of nutrients released from readily decomposed stems, twigs and leaves of trees damaged and killed by logging may thus explain the substantial but limited-duration increase in growth of survivors on these nutrient-poor soils (Figure 6). Yet post-disturbance ACS loss from survivors’ mortality decreases slowly in the Guiana Shield (Figure 6). This is consistent with the low mortality rates and the high tree longevity reported in old-growth forests of this region (Phillips et al., 2004). (iii) In the southern Amazon, high seasonal water stress is the main constraint on ACS recovery (Wagner et al., 2016). Stress-tolerant trees are generally poor competitors (He et al., 2013) and this may explain the slow ACS changes of survivors in this region (Figures 4a,d and 6). Finally, Central Amazonia is a transition zone for the main environmental and biotic gradients found in Amazonia: (1) a competition gradient between dense and nutrient-poor northeastern forests and nutrient-rich western forests; (2) an environmental gradient between northern wet forests and southern drier forests (Quesada et al., 2012). Across Amazonia, survivors contribute most to post-disturbance ACS recovery. In regions where survivors’ ACS gain is high (west and northeast), net ACS recovery is also high: annual ACS recovery is between 1 and 3 Mg C ha-1 yr-1 in the first 10 year after logging (Figure 6), lower than in Amazonian secondary forests (3–5 Mg C ha-1 yr−1 in the first year after of et al., for their have very low geographical variations in post-logging ACS 10 years after the disturbance are predicted to amounts of ACS almost in Amazonia. trees with cm have not been for in our and may play an important role in post-logging ACS changes. The cm as much as of total ACS and may be highly dynamic in Amazonian forests et al., 2004). of the slow tree growth rates in Amazonia et al., 2005; et al., trees will not the cm 10 years after the effects of the cm on post-logging ACS changes are likely to be in sites with less than 10 years of measurements (e.g. and be with the in the At the high disturbance intensities survivors’ survivors’ ACS growth is lower (Figure in lower net ACS change during the first 10 years of the recovery period (Figure 3f). disturbance intensities as well as relatively low forest maturity and this is ACS contributions from recruits remain high for longer (Figure in such enhanced growth conditions et al., In the first years after logging, net ACS recovery on disturbance intensity (Figure but recovery is predicted to longer in logged In immature et al., may explain fast ACS losses from survivors’ mortality (Figure In the logging et al., are to collateral to stands and results reveal that lower disturbance intensities, as a of the of increase survivors’ ACS growth and slow their ACS loss. that minimum cycles are year in the Brazilian Amazon et al., 2011), and that commercial species are and et al., 2005; et al., available stocks for the next will be mostly of be to high harvest intensities substantial due to poor harvesting that stocks of even if trees that are fast-growing that are by disturbance but are to water stress et al., 2016) and competition and 2008), and because their is lower than in forests (Rutishauser et al., 2016), have reduced carbon With climate change and increased and intensities of in Amazonia (Malhi et al., 2008), on recruits to C in forests disturbed by selective logging thus be a In this we on one of selective of value and for forest selective logging is a human disturbance in tropical and the data by the network are in of duration and We that our gives on the regional differences in Amazonian forests response to large ACS losses by other disturbances (e.g. that are to increase in with global changes et al., 2016). Materials and methods a includes data from long-term (8–30 year) experimental forest sites in the Amazon Basin and the Guiana Shield (Figure 1—figure supplement the following (i) in tropical forests with mean annual precipitation above (ii) a total area above 1 (iii) at one pre-logging and at two post-logging For each site, we annual precipitation and seasonality of precipitation data from (Hijmans et al., topsoil bulk density data from the Harmonized database (Nachtergaele et al., 2008), and the climatic from et al. et al., in all cases the data available For one of our sites see Figure 1—figure supplement 1), field measurements of precipitation from data in this particular we used the value and the climatic in the et al., 2014) data is available at et al., 2016). ACS a In all at of trees cm were and trees were to the to the species when or to the of trees were not To the wood we the following standardized to all (i) trees to the species were the wood value from the et al., (ii) trees to the were a wood (iii) trees with no or that were not in the were the wood The aboveground biomass was with the from et al. et al., 2014). was to be carbon et al., The ACS of tree was then as (1) where and are the wood density and at of the tree and is the climatic et al., 2014). The ACS changes data that was is available at et al., 2016). The recovery period a After logging, plot ACS decreases rapidly it minimum value a few years This transition the of the recovery was as the minimum ACS in the 4 years following logging our is on post-logging ACS recovery, we not in our plots where the minimum ACS value was not the 4 years after logging, because the logging not affect the plot or because were other of disturbance long after logging ACS changes a For each plot and with the time since the of the recovery period we 5 ACS changes : new recruits’ ACS is the ACS of all trees cm at and ≥20 cm at recruits’ ACS growth is the ACS of recruits between and recruits’ ACS loss is the C in recruits that between and survivors’ ACS growth is the ACS of survivors between and survivors’ ACS loss is the ACS of survivors that between and ACS gains are positive and ACS losses are ACS changes are to variation over because we are less in variations than in long-term ACS we cumulative ACS changes of annual ACS changes. Cumulative ACS changes (Mg C ha-1) were as (2) where is the plot, the time since and is the annual ACS change (Mg C ha-1 recruits’ ACS recruits’ ACS growth recruits’ ACS loss survivors’ ACS growth or survivors’ ACS loss Covariates a To model ACS we covariates : (1) disturbance intensity, i.e. of initial ACS loss; (2) a⁢c⁢s⁢0 mean ACS of the (3) relative ACS of the plot, as a % of (4) annual precipitation topsoil bulk To equivalent to all we centred and standardized in to have a mean of zero and a of one over all The uncertainty with ACS covariates is less than et al., 2014). covariates precipitation and precipitation seasonality were
Can timber provision from Amazonian production forests be sustainable?
(IOP Publishing, 2019) Camille Piponiot; Edna Rödig; Francis E. Putz; Ervan Rutishauser; Plínio Sist; Nataly Ascarrunz; Lilian Blanc; Géraldine Derroire; Laurent Descroix; Marcelino Carneiro Guedes
Abstract Around 30 Mm 3 of sawlogs are extracted annually by selective logging of natural production forests in Amazonia, Earth’s most extensive tropical forest. Decisions concerning the management of these production forests will be of major importance for Amazonian forests’ fate. To date, no regional assessment of selective logging sustainability supports decision-making. Based on data from 3500 ha of forest inventory plots, our modelling results show that the average periodic harvests of 20 m 3 ha −1 will not recover by the end of a standard 30 year cutting cycle. Timber recovery within a cutting cycle is enhanced by commercial acceptance of more species and with the adoption of longer cutting cycles and lower logging intensities. Recovery rates are faster in Western Amazonia than on the Guiana Shield. Our simulations suggest that regardless of cutting cycle duration and logging intensities, selectively logged forests are unlikely to meet timber demands over the long term as timber stocks are predicted to steadily decline. There is thus an urgent need to develop an integrated forest resource management policy that combines active management of production forests with the restoration of degraded and secondary forests for timber production. Without better management, reduced timber harvests and continued timber production declines are unavoidable.
Carbon recovery dynamics following disturbance by selective logging in Amazonian forests
(eLife Sciences Publications Ltd, 2016) Camille Piponiot; Plínio Sist; Lucas Mazzei; Marielos Peña‐Claros; Francis E. Putz; Ervan Rutishauser; Alexander Shenkin; Nataly Ascarrunz; C. P. de Azevedo; Christopher Baraloto
When 2 Mha of Amazonian forests are disturbed by selective logging each year, more than 90 Tg of carbon (C) is emitted to the atmosphere. Emissions are then counterbalanced by forest regrowth. With an original modelling approach, calibrated on a network of 133 permanent forest plots (175 ha total) across Amazonia, we link regional differences in climate, soil and initial biomass with survivors' and recruits' C fluxes to provide Amazon-wide predictions of post-logging C recovery. We show that net aboveground C recovery over 10 years is higher in the Guiana Shield and in the west (21 ±3 Mg C ha-1) than in the south (12 ±3 Mg C ha-1) where environmental stress is high (low rainfall, high seasonality). We highlight the key role of survivors in the forest regrowth and elaborate a comprehensive map of post-disturbance C recovery potential in Amazonia.
Pan‐tropical prediction of forest structure from the largest trees
(Wiley, 2018) Jean‐François Bastin; Ervan Rutishauser; James R. Kellner; Sassan Saatchi; Raphaël Pélissier; Bruno Hérault; Ferry Slik; Jan Bogaert; Charles De Cannière; Andrew R. Marshall
Abstract Aim Large tropical trees form the interface between ground and airborne observations, offering a unique opportunity to capture forest properties remotely and to investigate their variations on broad scales. However, despite rapid development of metrics to characterize the forest canopy from remotely sensed data, a gap remains between aerial and field inventories. To close this gap, we propose a new pan‐tropical model to predict plot‐level forest structure properties and biomass from only the largest trees. Location Pan‐tropical. Time period Early 21st century. Major taxa studied Woody plants. Methods Using a dataset of 867 plots distributed among 118 sites across the tropics, we tested the prediction of the quadratic mean diameter, basal area, Lorey's height, community wood density and aboveground biomass (AGB) from the i th largest trees. Results Measuring the largest trees in tropical forests enables unbiased predictions of plot‐ and site‐level forest structure. The 20 largest trees per hectare predicted quadratic mean diameter, basal area, Lorey's height, community wood density and AGB with 12, 16, 4, 4 and 17.7% of relative error, respectively. Most of the remaining error in biomass prediction is driven by differences in the proportion of total biomass held in medium‐sized trees (50–70 cm diameter at breast height), which shows some continental dependency, with American tropical forests presenting the highest proportion of total biomass in these intermediate‐diameter classes relative to other continents. Main conclusions Our approach provides new information on tropical forest structure and can be used to generate accurate field estimates of tropical forest carbon stocks to support the calibration and validation of current and forthcoming space missions. It will reduce the cost of field inventories and contribute to scientific understanding of tropical forest ecosystems and response to climate change.
Rapid tree carbon stock recovery in managed Amazonian forests
(Elsevier BV, 2015) Ervan Rutishauser; Bruno Hérault; Christopher Baraloto; Lilian Blanc; Laurent Descroix; Eleneide Doff Sotta; Joice Ferreira; Milton Kanashiro; Lucas Mazzei; Marcus Vinício Neves d'Oliveira
The Tropical managed Forests Observatory: a research network addressing the future of tropical logged forests
(Wiley, 2014) Plínio Sist; Ervan Rutishauser; Marielos Peña‐Claros; Alexander Shenkin; Bruno Hérault; Lilian Blanc; Christopher Baraloto; Fidèle Baya; Fabrice Bénédet; Kátia Emídio da Silva
Abstract While attention on logging in the tropics has been increasing, studies on the long‐term effects of silviculture on forest dynamics and ecology remain scare and spatially limited. Indeed, most of our knowledge on tropical forests arises from studies carried out in undisturbed tropical forests. This bias is problematic given that logged and disturbed tropical forests are now covering a larger area than the so‐called primary forests. A new network of permanent sample plots in logged forests, the Tropical managed Forests Observatory (Tm FO ), aims to fill this gap by providing unprecedented opportunities to examine long‐term data on the resilience of logged tropical forests at regional and global scales. Tm FO currently includes 24 experimental sites distributed across three tropical regions, with a total of 490 permanent plots and 921 ha of forest inventories.