Processing math: 100%
Editors-in-Chief:  Weilun Yin, Beijing Forestry University, China Klaus v. Gadow, University of Göttingen, Germany
Bruno Hérault, Camille Piponiot. Key drivers of ecosystem recovery after disturbance in a neotropical forest[J]. Forest Ecosystems, 2018, 5(1): 2-2. DOI: 10.1186/s40663-017-0126-7
Citation: Bruno Hérault, Camille Piponiot. Key drivers of ecosystem recovery after disturbance in a neotropical forest[J]. Forest Ecosystems, 2018, 5(1): 2-2. DOI: 10.1186/s40663-017-0126-7

Key drivers of ecosystem recovery after disturbance in a neotropical forest

Funds: This study was funded by (ⅰ) the GFclim project (FEDER 2014–2020, Project GY0006894) and (ⅱ) an Investissement d'avenir grant of the ANR (CEBA: ANR-10-LABEX-0025)
More Information
  • Corresponding author:

    Bruno Hérault, bruno.herault@cirad.fr

  • Received Date: 08 July 2017
  • Accepted Date: 19 December 2017
  • Published Date: 08 February 2018
  •   Background   Natural disturbance is a fundamental component of the functioning of tropical rainforests let to natural dynamics, with tree mortality the driving force of forest renewal. With ongoing global (i.e. land-use and climate) changes, tropical forests are currently facing deep and rapid modifications in disturbance regimes that may hamper their recovering capacity so that developing robust predictive model able to predict ecosystem resilience and recovery becomes of primary importance for decision-making: (ⅰ) Do regenerating forests recover faster than mature forests given the same level of disturbance? (ⅱ) Is the local topography an important predictor of the post-disturbance forest trajectories? (ⅲ) Is the community functional composition, assessed with community weighted-mean functional traits, a good predictor of carbon stock recovery? (ⅳ) How important is the climate stress (seasonal drought and/or soil water saturation) in shaping the recovery trajectory?
      Methods   Paracou is a large scale forest disturbance experiment set up in 1984 with nine 6.25 ha plots spanning on a large disturbance gradient where 15 to 60% of the initial forest ecosystem biomass were removed. More than 70, 000 trees belonging to ca. 700 tree species have then been censused every 2 years up today. Using this unique dataset, we aim at deciphering the endogenous (forest structure and composition) and exogenous (local environment and climate stress) drivers of ecosystem recovery in time. To do so, we disentangle carbon recovery into demographic processes (recruitment, growth, mortality fluxes) and cohorts (recruited trees, survivors).
      Results   Variations in the pre-disturbance forest structure or in local environment do not shape significantly the ecosystem recovery rates. Variations in the pre-disturbance forest composition and in the post-disturbance climate significantly change the forest recovery trajectory. Pioneer-rich forests have slower recovery rates than assemblages of late-successional species. Soil water saturation during the wet season strongly impedes ecosystem recovery but not seasonal drought. From a sensitivity analysis, we highlight the pre-disturbance forest composition and the post-disturbance climate conditions as the primary factors controlling the recovery trajectory.
      Conclusions   Highly-disturbed forests and secondary forests because they are composed of a lot of pioneer species will be less able to cope with new disturbance. In the context of increasing tree mortality due to both (ⅰ) severe droughts imputable to climate change and (ⅱ) human-induced perturbations, tropical forest management should focus on reducing disturbances by developing Reduced Impact Logging techniques.
  • In tropical forests, natural disturbances caused by the death of one or more trees are the dominant forms of forest regeneration as the creation of canopy openings continuously reshapes forest structure (Goulamoussène et al. 2017). The immediate increase in light intensity allows the sunlight to penetrate the understorey (Goulamoussène et al. 2016) and light-demanding trees (Denslow et al. 1998) to establish and grow, thus contributing to the maintenance of biodiversity that shapes forest functionning (Liang et al. 2016). Another effect of canopy gaps is the local modification of the forest nutrient balance due to the large amounts of dead leaves and wood that decompose and mineralize (Brokaw and Busing 2000) and that shapes in turn the small-scale spatial variations in forest carbon balance (Feeley et al. 2007; Guitet et al. 2015; Rutishauser et al. 2010). In this way, the natural disturbance regime is a fundamental component of the functioning of tropical forests (Sheil and Burslem 2003).

    With ongoing global (i.e land-use and climate) changes, tropical forests are currently facing deep and rapid changes in disturbance regimes that may hamper their recovering capacity (Hérault and Gourlet-Fleury 2016; Brienen et al. 2015). Human-induced disturbances may encompass a wide range of perturbations from long-lasting ones such as land-cover changes for industrial agriculture, slash-and-burn agriculture or mining (Dezécache et al. 2017a, b) to more insiduous modifications such as selective logging that may not affect the forest cover but modify forest functioning (Rutishauser et al. 2015). An even more insiduous perturbation is climate change (Hérault and Gourlet-Fleury 2016). Global circulation models have shown high probabilities of significant precipitation decrease for tropical areas with a risk of transition from short-dry-season rainforest to long-dry- season savannah ecosystems (Davidson et al. 2012). For instance, after the intense 2005 drought in Amazonia, the forest suffered an additional mortality, leading to a huge loss of live biomass (Phillips et al. 2009) with similar mortality events observed in Panama (Condit 1995), in China (Tan et al. 2013) or in South-East Asia (Slik 2004).

    To our opinion, the drivers of the post-disturbance system trajectory may first be defined based on their origin: endogeneous and exogeneous. (1) Endogenous drivers refer to the internal properties of the system that may influence its post-disturbance behavior. A significant example for ecological systems is the species composition that partially informs on the immediate potential of the system to recover after disturbance. In that respect, the species identity is far less important than the functional signature of the species assemblage (Kunstler et al. 2016): for example, an assemblage of light-demanding species will respond differently to disturbance from an assemblage of shade-tolerant understorey species (Herault et al. 2010). Structural characteristics of the pre-disturbance species community (stem density, average size, live biomass and so on) may also be of primary importance because they are core indicators of the silvigenetic stage of the forest (Pillet et al. 2017). (2) Exogenous drivers refer to external constraints or forces that limit the possible system trajectories. They can be grouped into two broad categories: drivers that vary in space and those that vary in time. The local environment, i.e. the physical characteristics of the abiotic environment, is here defined in space but not in time. On the contrary, external conditions such as climatic stress are here considered to vary in time but not in space.

    This study draws upon the long-term disturbance experiment of Paracou, French Guiana, to develop a modeling approach in order to mechanistically link the endo- and exogenous ecosystem drivers to the ecosystem recovery trajectory after disturbance. More specifically, we ask the following questions: (ⅰ) Do regenerating forests recover faster than mature forests given the same level of disturbance? (ⅱ) Is the local topography an important predictor of the forest recovery rates? (ⅲ) Is the community functional composition, assessed with community weighted-mean functional traits, a good predictor of carbon stock recovery? (ⅳ) How important is the climate stress (drought and/or soil water saturation) to shape the rate of carbon recovery? To do so, we partition the contributions to post-disturbance ACS (Aboveground Carbon Stock) gain (from growth and recruitment of trees sup 10 cm DBH) and ACS loss (from mortality) of survivors and recruited trees to detect the main drivers and patterns of ACS recovery after disturbance. We model the trajectory of those post-disturbance ACS changes (Piponiot et al. 2016b) in a comprehensive Bayesian framework. We then quantify the effect of (ⅰ) endogeneous (forest structure and composition) and (ⅱ) exogeneous (local environment and climate stress) drivers on the rates at which post-disturbance ACS changes converge to a theoretical steady state. Summing these ACS changes over time gives the net post-disturbance rate of ACS accumulation, an indicator of the ecosystem recovery rate. Disentangling ACS recovery with a demographic process-based approach, i.e. by segregating ACS changes into cohorts (survivors and recruits) and demographic processes (growth, recruitment, mortality), as opposed to an all-in-one model in which only the ecosystem net ACS change is modeled without examination of demographic processes, has been shown to be essential to reveal mechanisms underlying ACS responses to disturbance and to make more robust predictions of ACS recovery (Piponiot et al. 2016b).

    The study was conducted at the Paracou experimental site (5°18'N, 52°55'W), a lowland tropical rain forest near Sinnamary, French Guiana. The site receives nearly two-thirds of the annual 3041 mm of precipitation between mid-March and mid-June, and < 50 mm per month in September and October (Wagner et al. 2011). More than 700 woody species attaining 2 cm DBH (diameter at breast height) have been described at the site, with 150 - 210 species of trees > 10 cm DBH per hectare. The floristic composition is typical of Guianan rainforests with dominant families including Leguminoseae, Chrysobalanaceae, Lecythidaceae, Sapotaceae and Burseraceae (Guitet et al. 2014). In 1984, nine 6.25 ha plots, each one divided into 4 subplots of 1.56 ha each, were established for a complete inventory of all trees > 10 cm DBH. From October 1986 to May 1987, the plots underwent three disturbance treatments (details in Table 1 and in (Blanc et al. 2009)).

    Table  1.  Disturbance treatments (T1, T2, T3) implemented on the Paracou plots in 1986-1987
    Timber logging Fuelwood logging Thinning % ACS loss
    T1 DBH ≥ 50 cm, mean of 10 trees ·ha−1 - - [12−33%]
    T2 DBH ≥ 50 cm, mean of 10 trees ·ha−1 - DBH ≥ 40cm, all non-valuable species, mean of 30 trees ·ha−1 [33−56%]
    T3 DBH ≥ 50 cm, mean of 10 trees ·ha−1 40 cm ≤ DBH ≤ 50 cm, all non-valuable species, mean of 20 trees ·ha−1 DBH ≥ 40cm, all non-valuable species, mean of 15 trees ·ha−1 [35−66%]
    The percentage of Aboveground Carbon Stock loss (% ACS loss) is defined as the difference between the pre-disturbance ACS and its minimum value reached during the 4 years after the disturbance treatments
     | Show Table
    DownLoad: CSV

    Aboveground Carbon Stock (ACS) computation In all plots, diameter at breast height (DBH) of trees > 10 cm DBH were measured every two years from 1982 to 2016 resulting in 18 forest censuses. Trees were identified to the lowest taxonomic level. To get wood density, we applied the following standardized protocol: (ⅰ) tree identified to the species level were assigned the corresponding wood specific gravity value from the Global Wood Density Database (GWDD) (Chave et al. 2009); (ⅱ) trees identified to the genus level were assigned a genus-average wood density and (ⅲ) trees with no botanical identification or that were not in the GWDD were assigned the subplot-average wood density. The aboveground biomass (AGB) was estimated taking all uncertainties into account using the BIOMASS package (Réjou-Méchain et al. 2017). Biomass was assumed to be 47% carbon.

    Disturbance intensity After disturbance, the subplot's ACS decreases rapidly until it reaches its minimum value acsmin a few years later. This transition point determines the beginning of the recovery period. The difference between the averaged pre-disturbance ACS acspre and this post-logging minimum value acsmin reached at time t=tmin defines the disturbance intensity DIST. In other words, the disturbance intensity is defined as the amount of aboveground carbon lost in the forest ecosystem during the first years during and after the disturbance.

    Structure drivers The pre-disturbance forest structure was assessed with three variables: the stem density SN (from 483 to 727 ind ·ha-1) and the basal area SBA (from 27 to 36 m 2·ha-1) of subplot j at tpre, the year preceding the disturbance experiment.

    Environment drivers Three environmental drivers were selected from a preliminary exploratory analysis to represent independent source of variation in the local forest physical conditions: the proportion of bottom-lands EBOTTOM, the average topographical slopes of the plot ESLOPE and the standard deviation, i.e the heterogeneity, of the altitudinal distribution EHETE .

    Composition drivers The pre-disturbance forest composition was assessed in a functional trait space to avoid local taxonomic variations in tree assemblages that are of little importance for forest functioning. The four chosen orthogonal traits FT represent key dimensions of the tree functional strategy (Baraloto et al. 2010): wood density TWD, seed mass TSEED, specific leaf area TSLA and maximum diameter TDBH95 estimated as the 95th percentile of the species DBH distribution in the Guyafor database. The community weighted means of these functional traits were calculated the year preceding the disturbance experiment.

    Climate drivers We considered two main sources of climate stress: soil drought CDROUGHT and soil water saturation CWATER . These variables were quantified using a water balance model, developed and calibrated in Paracou (Wagner et al. 2011), that was run using precipitation and evapotranspiration as inputs over the 1982-2016 time period. CDROUGHT was estimated as the number of days with REW, Relative Extractable Water, below 0.4 while the number of days with REW equal to 1, the soil is full of water, defined CWATER . These two covariates were computed between 2 consecutive censuses and then standardized at a yearly time-step.

    We define two cohorts of trees. First, recruits are all the trees (> 10 cm DBH) that have been recruited since the perturbation. Trees that, for a given census, first went through the 10 cm DBH are called new recruits. Thereafter, they are called, for the following censuses, recruits and may grow or may eventually die between 2 censuses. Second, survivors are trees that were present in the forest before the disturbance and that survived the disturbance event.

    For each subplot j and census k, with tk the time since the beginning of the recovery period, we thus define 5 ACS changes : new recruits' ACS (Rrj, k) is the ACS of all trees < 10 cm DBH at tk-1 and ≥ 10 cm DBH at tk ; recruits' ACS growth (Rgj, k) is the ACS increment of living recruits between tk-1 and tk ; recruits' ACS loss (Rlj, k) is the ACS in recruits that die between tk-1 and tk ; survivors' ACS growth (Sgj, k) is the ACS increment of living survivors between tk-1 and tk ; survivors' ACS loss (Slj, k) is the ACS of survivors that die between tk-1 and tk . ACS changes are subject to large stochastic variation over time: because we are less interested in year-to-year variations than in long-term ACS trajectories, we modeled the cumulative ACS changes over time. Cumulative ACS changes (Mg C ·ha-1) were defined as follows:

    cChangej,k=km=0(Changej,m×(tktk1)) (1)

    where j is the subplot, k is the census number, tk the time since t0 (yr) and Change is the annual ACS change (Mg C ·ha-1·yr-1), either recruits' ACS (Rr), recruits' ACS growth (Rg), recruits' ACS loss (Rl), survivors' ACS growth (Sg), or survivors' ACS loss (Sl).

    Survivors Survivors' cumulative ACS changes are null at t=0 and have a finite limit, attained once survivors have all died. We modeled survivors' cumulative ACS growth cSg as:

    cSgj,p,kN(αSgp×(1exp(βSgj,k×tk)),(σSg)2) (2)

    where j is the subplot, p the plot it belongs to, tk is the time since t0. αSgp is the finite limit of the cumulative ACS change, βSgj,k the rate at which the cumulative ACS change converges to this limit and (σSg)2 the variance of the model. By choosing an exponential kernel, we assume that survivors' ACS growth at tk is proportional to survivors' ACS growth at tk-1.

    Because of our nested design with subplots j within plots p, we modeled the αSgp values with a random plot effect of mean αSg0 and variance (σSgα)2:

    αSgpN(αSg0,(σSgα)2) (3)

    Parameter βSgj,k is the rate at which survivors' ACS growth on plot j at time tk converges to a finite limit after the disturbance: it reflects the response rapidity of survivors' ACS growth to disturbance. Because we are interested in predicting variations in βSgj,k, we expressed the latter as a function of covariates:

    βSgj,k=βSg0+13l=1(λSgl×Vj,tk,l) (4)

    with βSg0 the model intercept, λSgl the vector of l parameters associated to the covariates Vj, k, l for which we looked at their effects on the post-logging rate βSgj,k in subplot j at time tk . The covariates are defined above and related to the disturbance intensity DIST, the structure of the forest before disturbance (SN, SDG, SBA ), the functional trait composition of the forest before disturbance (TSEED, TSLA, TWD, TDBH95), the local environment (EBOTTOM, ESLOPE, EHETE ) and the climate stress (CDROUGHT, CWATER ). Note that values of the two later covariates changed with times. All covariates are centered and standardized before the inference. When all survivors in plot p are dead, all the C gained by their growth (cSgj,p,=αSgp) plus their initial ACS (acsminj ) will have been lost (cSlj,=αSlj). We thus defined

    αSlj=αSgp+acsminj (5)

    with αSgp,αSlj the finite limits of survivors' cumulative ACS growth and ACS loss respectively, and acsminj the ACS of the subplot j at tmin =t0. Then the cumulative carbon loss is

    cSlj,p,kN(αSlj×(1exp(βSlj,t×tk)),(σSl)2) (6)

    where j is the subplot, p the plot it belongs to, tk is the time since t0. αSlj is the finite limit of the cumulative ACS change, βSlj,k the rate at which the cumulative ACS change converges to this limit and (σSl)2 the variance of the model. And with

    βSlj,t=βSl0+6l=1(λSll×Vj,t,l) (7)

    with βSl0 the model intercept, λSll the vector of l parameters associated to the covariates Vj, k, l for which we looked at their effects on the post-logging rate βSlj,k in subplot j at time tk .

    Recruits When survivors are all dead, newcomers or recruits will constitute the new forest. We made the assumption that the recruits' annual ACS changes will converge to constant values, with ACS gains compensating ACS losses. Because there are no recruits yet at t0, recruits' annual ACS growth (Rg) and ACS loss (Rl) are zero, and progressively increase to reach their asymptotic values. Recruits' annual ACS growth and ACS loss can be modeled with the function:

    f(t;α,β)=α×(1exp(β×t)) (8)

    where t the time since the beginning of the recovery period. In the same logic as survivors' cumulative ACS changes, α is the asymptotic value of recruits' annual ACS change (Mg C ·ha-1·yr-1), and β is the rate at which this asymptotic value is reached. Contrary to recruits' annual ACS growth and ACS loss, the ACS of new recruits (Rr, the ACS of tree reaching the 10 cm DBH threshold) is high at t0 because of the competition drop induced by logging, but then progressively decreases to reach its asymptotic value. We modeled it with the following function:

    f(t;α,β)=α×(1+exp(β×t)) (9)

    where t is the time since disturbance. As stated before, we chose to model cumulative ACS changes instead of annual ACS changes. The general model for recruits' cumulative ACS changes (ACS growth Rg, ACS loss Rl and ACS of new recruits Rr) is obtained by mathematical integrating from t0 to tk annual ACS changes:

    cRj,p,kN(αRp×(tk+η×1exp(βRj,k×tk)βRj,k),(σR)2) (10)

    where j is the subplot, p the plot, tk is the time since t0, R is the annual ACS change, either Rr, Rg or Rl and (σR)2 the variance of the model. When R is Rg or Rl, η=-1; when R is Rr, η=1. Because of our nested design with subplots j within plots p, we modeled the αRp values with a random plot effect of mean αR0 and variance (σRα)2:

    αRpN(αR0;(σRα)2) (11)

    When the dynamic equilibrium is reached, annual ACS gain (growth and recruitment) compensates annual ACS loss (mortality). We thus added the following constraint for every plot p:

    αRrp+αRgp+αRlp=0 (12)

    Using the same logic as for survivors, we are interested in predicting variation in βR as follows:

    βRj,kN(βR0+6l=1(λRl×Vj,k,l)(σRβ)2) (13)

    with R being Rg, Rl or Rr depending on the process we were interested in, with βR0 the model intercept, λRl the vector of l parameters associated to the covariates Vj, k, l for which we looked at their effects on the post-logging rate βRj,k in subplot j at time tk .

    Bayesian hierarchical models were inferred through MCMC methods using an adaptive form of the Hamiltonian Monte Carlo sampling (Carpenter et al. 2017). Codes were developed using the R language and the Rstan package (Carpenter et al. 2017). A detailed list of priors is provided in Table 2.

    Table  2.  List of priors used to infer ACS changes in a Bayesian framework
    Model Parameter Prior Justification
    Sg αSgp U(10,200) Around 100 survivors/ha storing 0.1 to 2.0 MgC each
    Sg βSgj,t U(0,0.25) 12<tSg0.95<+
    Sl βSlj,t U(0,βSgj,t) tSg0.95<tSl0.95<+
    Rr αRrp U(0.1,1) TmFO observed values (Piponiot et al. 2016b)
    Rr βRrj,t U(0,0.75) 4<tRr0.95<+
    Rr αRgp U(0.1,3) Amazonian values (Johnson et al. 2016)
    Rr βRgj,t U(0,0.5) 6<tRg0.95<+
    Rr βRlj,t U(0,0.5) 6<tRl0.95<+
    All models M∗∗ λMl U(βMj,t,βMj,t) avoid multicollinearity problems
    Models are : (Sg) survivors’ ACS growth, (Sl) survivors’ ACS loss, (Rr) new recruits’ ACS, (Rg) recruits’ ACS growth, (Rl) recruits’ ACS loss
    * t0.95 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 or Rl
     | Show Table
    DownLoad: CSV

    To assess the importance of the pre- (forest structure, environment and composition) and post- (climate stress) disturbance forest conditions, we simulated different scenarios modifying the covariate values but keeping an averaged (set to 0) disturbance intensity DIST. Note that all model covariates Vj, t, l were standardized before modeling so that, for a given covariate, a - 2, 0 or 2 value respectively refers to a very low, average or very high observed value.

    Forest structure The effects of a regenerating (high stem density SN =1, low basal area SBA =-1), intermediate (medium stem density SN =0, medium basal area SBA =0) and mature (low stem density SN =-1, high basal area SBA =1) pre-disturbance forest structure on ecosystem recovery were compared.

    Forest environment The effect of three contrasted forest environment were compared: predominance of bottom-lands (high proportion of bottom-lands EBOTTOM =2, medium slope values ESLOPE =0, medium altitudinal heterogeneity EHETE =0), predominance of slopes (medium proportion of bottom-lands EBOTTOM =0, high slope values ESLOPE =2, medium altitudinal heterogeneity EHETE =0) and hilly landscapes (medium proportion of bottom-lands EBOTTOM =0, medium slope values ESLOPE =0, high altitudinal heterogeneity EHETE =2).

    Forest composition The effect of pre-logging forest community dominated by conservative tree species (high wood density TWD =2, high seed mass TSEED =2, low specific leaf area TSLA =-2, high maximal stature TDBH95=2), by a disturbed community (low wood density TWD =-2, low seed mass TSEED =-2, high specific leaf area TSLA =2, low maximal stature TDBH95=-2) and by a true pioneer community (very low wood density TWD =-4, very low seed mass TSEED =-4, very high specific leaf area TSLA =4, very low maximal stature TDBH95=-4). The values of the last scenario may appear extreme but note that the model was calibrated with mature forest stands only so that covariate values have to be set out of the calibration range to get a true pioneer community.

    Climate stress The effects of a wetter (nor or a few seasonal drought CDROUGHT =-2, high soil water saturation during the wet season CWET =2), a drier (seasonal droughts CDROUGHT =1, medium soil water saturation during the wet season CWET =0) and a even drier (heavy seasonal droughts CDROUGHT =2, medium soil water saturation during the wet season CWET =0) climate on ecosystem recovery were compared.

    To assess the sensitivity of the ecosystem recovery process to the pre- (forest structure, environment and composition) and post- (climate stress) disturbance forest conditions, we simulated the model for an average disturbance intensity DIST=0 and, for each group of covariates Vj, t, lDIST, varying the values within a group of covariate while setting the other covariates to 0. In a nutshell, for each group of covariates Climate, Composition, Environment and Structure (ⅰ) we independently sampled covariate values from U(2;2) while the covariates from the 3 other groups are set to 0, (ⅱ) we ran the model using the sampled covariate values for a set of 100 parameter values drawn from the posterior chains, (ⅲ) we estimated, after 30 years of simulation, the net carbon balance and (ⅳ) we did the procedure 1000 times per group of covariates. Doing so, the variability of the net carbon balance after 30 years reflects the sensitivity of ecosystem recovery to the varying group of covariates.

    Given that all the covariates were standardized before modeling, the absolute values of their associated parameters give the weight of each variable in shaping the rates β at which the ACS changes reach their asymptotic state. Negative covariate values indicate slowing and positive values indicate accelerating rates. The values of the disturbance intensity DIST parameters always ranked among the highest absolute values, with negative values for SurvivorsACSgrowth and NewrecruitsACS (Fig. 1a, c) and positive ones for the other three cumulative fluxes (Fig. 1b, d, e).

    Figure  1.  Effect of covariates on the rate at which post-disturbance ACS changes converge to a theoretical steady state. Covariates are the disturbance intensity DIST (in green), the structure of the forest before disturbance (the number of individual trees SN, the basal area SBA, in pink), the functional trait composition of the forest before disturbance (seed mass TSEED, specific leaf area TSLA, wood density TWD, maximum size TDBH95, in maroon), the local environment (proportion of bottomlands EBOTTOM, average slope ESLOPE, altitudinal heterogeneity EHETE, in blue) and the climate stress (drought intensity CDROUGHT, days with water saturation CWATER, in red). Covariates are centred and standardized. Posterior distribution (median and 0.95 credibility intervals) are reported. Negative covariate values indicate slowing and positive values indicate accelerating rates. a Survivors' ACS growth. b Survivors' ACS loss. c New recruits' ACS. d Recruits' ACS growth. e Recruits' ACS loss

    The two variables chosen to describe the pre-disturbance forest structure, i.e. basal area SBA and stem density SN, have contrasted behaviors. Basically, associated parameters get their highest absolute values for the two growth models, both Survivors' and Recruits' (Fig. 1a and d), while being close to zero for the other three models (Fig. 1b, c, e). Contribution of Survivors' growth to ACS recovery is higher but declines quicker with high pre-disturbance SBA values and low SN values (Fig. 1a). Contribution of Recruits' growth to ACS recovery declines slowly with high SN values (Fig. 1d). Mature forests (high SBA, low SN ) thus recovers faster than regenerating ones (Fig. 2). The relative importance of the pre-disturbance forest structure on the variability of ACS recovery rates is low, with 30 to 50 MgC ·ha-1 recovered after 30 years (Fig. 6).

    Figure  2.  Predicted contribution of annual ACS changes in three contrasted scenarios of pre-logging forest structure : Regenerating, Mature and Intermediate are defined with standardized covariates SN and SBA respectively set to [1, -1, 0] and to [-1, 1, 0]. The white line is the net annual ACS recovery, i.e. the sum of all annual ACS changes: survivors' ACS growth Sg and loss Sl, new recruits' ACS Rr and recruits' ACS growth Rg and loss Rl. Dotted lines are out of the calibration period (0-30 year). Maximum-likelihood predictions for ACS stocks (bottom-right) are projected within their credibility intervals (areas with higher levels of transparency
    Figure  6.  Estimating the relative importance of the Climate stress, the pre-disturbance forest Composition, Environment and Structure in driving ecosystem recovery 30 years after disturbance. The violin plots represent the variability of the distribution of the net carbon balance when covariates within a given group are independently and randomly drawn from U(2;2) while the covariates from other groups are set to 0 (the longest the boxplot the highest the sensitivity of ecosystem recovery to this given group of covariates)

    Three variables inform on the forest local environment, i.e. altitudinal heterogeneity EHETE, proportion of bottomlands EBOTTOM and the average slope ESLOPE . The latter never significantly contributes to the β variability (Fig. 1). EBOTTOM is important in defining recruits ACS fluxes, with positive parameter values for New recruits' ACS and negative ones for growth and loss (Fig. 1c, d, e). All fluxes together, recovery rates do not differ markedly with environmental conditions (Fig. 3) so that the importance of the local forest environment on the variability of ACS recovery rates is quite low, with 30 to 50 MgC ·ha-1 recovered after 30 years (Fig. 6).

    Figure  3.  Predicted contribution of annual ACS changes in three contrasted scenarios of forest environment : Bottomlands, Slopes and Hilly environment are defined with standardized covariates EBOTTOM, ESLOPE and EHETE respectively set to [2, 0, 0], [0, 2, 0] and [0, 0, 2]. The white line is the net annual ACS recovery, i.e. the sum of all annual ACS changes: survivors' ACS growth Sg and loss Sl, new recruits' ACS Rr and recruits' ACS growth Rg and loss Rl. Dotted lines are out of the calibration period (0-30 year). Maximum-likelihood predictions for ACS stocks (bottom-right) are projected within their credibility intervals (areas with higher levels of transparency

    Four orthogonal functional traits (i.e. specific leaf area TSLA, maximum stature TDBH95, seed mass TSLEED and wood density TWD ) have been retained to summarize differences in pre-disturbance forest composition. All these traits have been found to influence post-disturbance ACS recovery rates (Fig. 1). For Survivors, the contribution of growth to ACS recovery is higher but declines quicker with high TSLA and low TWD (Fig. 1a) while losses declines slowly with high TSLA and high TSEED (Fig. 1b). For Recruits, the contribution of growth to ACS recovery is higher but declines quicker with high TDBH95 and low TSLA and TSEED (Fig. 1d) while losses declines slowly with low TWD (Fig. 1e). Forests, for which the pre-disturbance composition is dominated by conservative ecological strategies (high wood density, seed mass, maximal stature and low specific leaf area) recovers faster than disturbed forests dominated pioneer species (very low wood density, very low seed mass, very high specific leaf area and very low maximal stature) (Fig. 4). The relative importance of the pre-disturbance forest composition on the variability of ACS recovery rates is high, with 0 to 100 MgC ·ha-1 recovered after 30 years (Fig. 6).

    Figure  4.  Predicted contribution of annual ACS changes in three contrasted scenarios of pre-logging forest composition: Conservatists, Pioneers and Pioneers++ communities are defined with standardized covariates TWD, TSEED, TSLA and TDBH95 respectively set to [2, -2, -4], [2, -2, -4], [-2, 2, 4] and [2, -2, -4]. Pioneers++ refer to a true pioneer community (very low wood density, very low seed mass, very high specific leaf area and very low maximal stature). The white line is the net annual ACS recovery, i.e. the sum of all annual ACS changes: survivors' ACS growth Sg and loss Sl, new recruits' ACS Rr and recruits' ACS growth Rg and loss Rl. Dotted lines are out of the calibration period (0-30 year). Maximum-likelihood predictions for ACS stocks (bottom-right) are projected within their credibility intervals (areas with higher levels of transparency

    Two variables related to climate stress occurring during the recovery process were tested. The intensity of the dry season CDROUGHT decelerates the decline of Survivors' ACS changes (Fig. 1a, b) while it accelerates the decline of all the recruits' ACS changes (Fig. 1c, d, e). High soil water saturation during the wet season has a similar effect but with very high β values for recruits' ACS loss (Fig. 1e). The forest recovers faster in the driest climate scenarios and this is mainly due to the important Survivors' ACS losses in the wettest scenarios (Fig. 5). The effect of climate stress on the variability of ACS recovery rates ranks 2nd among the 4 groups of covariates, with 10 to 80 MgC ·ha-1, depending on the post-disturbance climate conditions, recovered after 30 years (Fig. 6).

    Figure  5.  Predicted contribution of annual ACS changes in three contrasted climate scenarios: Wet, Dry and Dry++ climates are defined with standardized covariates CDROUGHT and CWATER respectively set to [-2, 1, 2] and [2, 0, 0]. Dry++ refer to an extremely-dry climate (very high seasonal drought in the dry season, medium soil water saturation in the rain season). The white line is the net annual ACS recovery, i.e. the sum of all annual ACS changes: survivors' ACS growth Sg and loss Sl, new recruits' ACS Rr and recruits' ACS growth Rg and loss Rl. Dotted lines are out of the calibration period (0-30 year). Maximum-likelihood predictions for ACS stocks (bottom-right) are projected within their credibility intervals (areas with higher levels of transparency

    In this study, we modeled the post-disturbance ACS fluxes in a neotropical forest and found that by testing a few variables that are related to the main endogenous (forest structure and composition) and exogeneous (local environment and climate stress) drivers of ecological community dynamics, we could successfully predict ecosystem trajectories in a wide range of pre- and post-disturbance conditions. Modeling separately the surviving and recruited cohorts was confirmed to be an important methodological choice (Piponiot et al. 2016b), given that the highlighted drivers did not overlap, whether for the growth (Fig. 1a and d) or the loss (Fig. 1b and e) processes. This suggests that our methodological approach, deciphering ecosystem fluxes by demographic processes, could be very useful to predict the long-term trajectories in highly diverse tropical forests for which precise demographic data may be lacking, but aggregative forest dynamic censuses are available from forest inventories. In this study, the disturbance intensity gradient was induced by combining logging to thinning operations (Table 1). Because of its economic value and implications for forest management, selective logging experiments were set up very early on, and the data gathered by these experiments are unique in terms of experiment duration and spatial extent. Despite the particular nature of logging operations (focus on large and commercially-valuable trees even though logging damage concerns all DBH classes), we believe that our study gives clues on the key drivers of ecosystem recovery after large ACS losses induced by other disturbances (e.g. droughts, fire) that are expected to increase in intensity with ongoing global changes (Bonal et al. 2016).

    Disturbance intensity DIST remains, by far, the first predictor of the post-disturbance system trajectory (Fig. 1).

    High disturbance intensities obviously reduce the residual survivors' ACS so that ACS changes from survivors' growth is lower at the beginning but, because of the lower competition between survivors, they tend to live longer and reach the asymptotic state slowly (lower β, Fig. 1a). The positive β for Survivors' ACS loss, meaning that survivors tend to die faster after high levels of disturbance, may look surprising because this goes against the growth result. We believe that a high tree mortality, due to the low survival of damaged trees in highly disturbed systems, in the early post-disturbance years may have resulted in increased β values. However, those losses should rapidly decrease after a decade (Thorpe et al. 2008).

    High disturbance intensities alleviate competition, and this is probably why recruits' ACS growth is high just after disturbance in the enhanced growth conditions (Herault et al. 2010) and then quickly decrease (high βs, Fig. 1d and e). In these disturbed forests, intense self-thinning (Feldpausch et al. 2007) may explain the fast but limited-in-times ACS losses from survivors' mortality (Fig. 1e).

    Pre-disturbance Forest structure All else being equal, mature forests (high SBA, low SN ) recover faster than regenerating ones. And this is mainly due to the higher ACS incoming fluxes from Survivors' growth (Fig. 2). Regenerating forests are composed of short-living fast-growing small species. These species are poorly efficient at carbon accumulation because of their limited growth response to canopy openings and competition alleviation. Indeed, it has been shown in the Paracou forests that species with the highest inherent growth rate (in the absence of disturbance) have the lowest growth response when a disturbance occurs (Herault et al. 2010). On the contrary, large mature trees are, despite their low numbers in many forests, key elements of carbon storage (Lindenmayer et al. 2012) and dynamics (Sist et al. 2014). Previously disturbed, logged or secondary forests, for which forest structure is characterized by low SBA, and high SN, may thus be far less resilient to new disturbance than natural undisturbed forests. This also means that post-logging ACS recovery that is currently estimated from the first logging rotation (Rutishauser et al. 2015) may be overestimated for the following logging rotations (Rutishauser et al. 2016). Finally, despite these clear outcomes, the importance of the pre-disturbance forest structure on the variability of ACS recovery rates, as compared to the other covariates (forest composition, environment and climate), remains low (Fig. 6) in the sensitivity analysis. However, we should keep in mind that all the disturbed plots were established in a natural undisturbed forest area so that the model has been parameterized with low pre-disturbance forest structure variability. We thus suggest that, in landscapes with contrasted and tumultuous history, the role of the forest structure in shaping the post-disturbance system trajectory would be much higher.

    Pre-disturbance Forest composition The importance of the pre-disturbance forest composition on the variability of the post-disturbance ACS recovery rates is unexpected, with 0 to 100 MgC ·ha-1, depending on the initial species assemblage, recovered after 30 years (Fig. 6). All the studied functional traits are implied in shaping one or more of the investigated ACS changes. When comparing two typical forest composition, i.e. an assemblage of conservative trees (high wood density, seed mass, maximal stature and low specific leaf area) and an assemblage of pioneer trees (exact opposite trait composition), the pioneer assemblage recovers much slower. This difference is mainly due to highly contrasted survivors' ACS changes. Both ACS growth and loss increase rapidly in surviving pioneers. This result is consistent with the acquisitive strategy of species with a high carbon budget (Sterck et al. 2011), that are well-known to have very fast turn-over rates (Aubry-Kientz et al. 2013; Hérault et al. 2011; Flores et al. 2014). Even if both survivors ACS growth and loss are boosted in pioneer assemblages, it is quite remarkable that the ACS balance in time is mainly under the control of survivors' ACS loss Sl. Survivors' ACS loss can cancel survivors' ACS growth in pioneer-dominated communities, resulting in a null ACS balance (zero is included in the simulations results, see Fig. 6). Why do those pre-disturbance pioneer communities have such high ACS losses in the post-disturbance times? Fast-growing pioneers are generally both poor competitors and poor stress-tolerant trees (He et al. 2013). A first possible explanation is thus that the stress induced by disturbance may be too high for these species that undergo, after disturbance, heavy losses. An alternative explanation is that the higher ACS growth mechanically induces, after a while, higher ACS losses. If pioneers grow faster as a result of growth-stimulating disturbance, they will pass through their natural life span faster, resulting in a transitory gain in carbon storage followed by a massive carbon release when these pioneers get older (Körner 2017). Introducing a time lag for the ACS loss models would be the only way to test the last hypothesis.

    Local environment The forest local environment defined by the altitudinal heterogeneity, the proportion of bottomlands and the average slope, have been found to be of low importance in shaping variability of ACS recovery rates (Fig. 3). This result is quite surprising, given that the local environment is very often referred to as a driver of ecological processes in tropical forests (Grau et al. 2017), from fine pairwise interactions between individual trees (Kraft et al. 2008) to regional variation in community assemblages (Fayad et al. 2016). For instance, in the Paracou forest, the proportion of bottomlands have been found to be of primary importance for forest dynamics: treefall rates are twice as high as on hilltops and tree recruitment and growth rates are higher, leading to a lower basal area and ACS (Ferry et al. 2010). Nearly three fourths of the Paracou taxa are locally distributed as a function of relative elevation, with seasonally inundated bottomlands and well-drained plateaus revealing contrasted species associations (Allié et al. 2015). Despite the relative importance of EBOTTOM in defining recruits' ACS changes, with positive β parameter values for new recruits' ACS and negative ones for recruits' ACS growth and loss (Fig. 1), all in all ACS recovery rates differ very little from hilly or sloppy plots (Fig. 3). On the one hand, the low-stress conditions of bottomlands (no seasonal drought, less wind) should induce a faster ecosystem recovery. On the other hand, the lower final ACS (Ferry et al. 2010) may mechanically lead to lower absolute carbon storage during recovery. All together, the two processes could be canceling each other, explaining why absolute carbon recovery is similar between bottomlands and hilltops. We also should keep in mind that the disturbance experiment was made by logging. During logging operations, bottomlands are avoided and logging is preferentially conducted in easier-to-access hilltop areas, whatever their proportion in the plot. This may have artificially reduced the environmental difference between logged plots and, in turn, the ACS recovery trajectories.

    Post-disturbance climate stress Two seasonal climate stresses were studied: soil water saturation in the wet season and drought intensity in the dry season. The importance of the post-disturbance climate stress on the variability of ACS recovery rates was very high with, depending on the climate scenarios, 10 to 80 MgC ·ha-1 recovered after 30 years (Fig. 6). The 2 driest scenarios recover initial ACS very quickly, i.e. in less than 60 years while the wettest one would reach a new asymptotic values, far below the initial system ACS (Fig. 6). The main difference between the 3 scenarios lies in the absolute values of the Survivors' ACS loss, with very high values for the wet scenario. This result may look strange given that drought has often been identified as one of the main climate drivers of tropical forest dynamics (Bonal et al. 2016; Wagner et al. 2012, 2013, 2014, 2016), with large mortality events among tropical trees during El Nino years for instance (Phillips et al. 2009) that have not only immediate but also long-term and cumulative impacts on the carbon cycle (Doughty et al. 2015). Those large mortality events are associated with tree hydraulic traits, the most susceptible species being those having a low hydraulic safety margin (Anderegg et al. 2016). In this context, why do the most intense dry season generate the lowest carbon losses? In our training dataset, the natural variability of the total rainfall from 2700 to 3100 mm ·yr-1 is quite low and, moreover, is far above the 1500 mm ·yr-1, the evapotranspiration threshold. This means that our experimental forest, located in the Guiana Shield, is not water-limited at all (Stahl et al. 2013), just like the Northern part of the Amazonian basin (Wagner et al. 2017). One may also expect that, because of hydraulic failure, standing death is more frequent during the driest years but, when plotting the tree mode of death registered at Paracou for each dead tree against the drought estimator, no evidence was observed for a potential trend (Aubry-Kientz et al. 2015). Our results thus reinforce the idea that the dominant seasonal climate stress in the Paracou forest is not drought during dry season but water saturation during wet season. This confirms the hypothesis that waterlogged soils in space or in time are risky for trees (Ferry et al. 2010). Moreover, during the rainy season, strong rainfall events often come with strong winds that may reinforce ACS losses (Toledo et al. 2011) and we know, from the Paracou dataset, that the highest total precipitation leads to the highest proportion of tree-fall deaths (Aubry-Kientz et al. 2015). Global climate models converge to simulate, at least for the Amazonian region, a change in precipitation regime over the coming decades (Malhi et al. 2009). Seasonal droughts are expected to become longer and stronger in the future (Joetzjer et al. 2013). Our simulations would suggest that post-disturbance forest recovery would be faster with these new climate conditions. However, we should keep in mind that our simulations were based on a model calibrated with data from a natural, undisturbed forest (Fargeon et al. 2016). With increasing mortality rates due to increasing drought occurrence and severity, the new tree community may be richer in post-disturbance pioneer species. And we have already seen that these new assemblages will slowly recover (Fig. 4) so that recurrent climate-stress in time would not lead to faster recovery rates, but rather to pioneer-rich forest communities with slow recovery rates.

    More than half of the tropical forest area are currently designated by National Forests Services as production forests (Blaser et al. 2011) and they consequently play a key role in the tropical forest carbon balance (Piponiot et al. 2016a; Sist et al. 2015). In the Amazon, forest logging and degradation combined to climate change would render up to 80% of the forest area susceptible to major disturbance events in the coming decades (Asner et al. 2010). We have shown that the pre-disturbance forest composition and the post-disturbance climate conditions are of primary importance to predict the recovery potential of tropical forest ecosystems. From the Paracou long-term experiment, it becomes increasingly clear that highly-disturbed forests, because they contain a lot of pioneer species (Baraloto et al. 2012), will be less able to cope with (ⅰ) new disturbance such as logging and (ⅱ) the drier conditions induced by climate change. In other words, already-disturbed forests are likely to be the most vulnerable systems in the current global change context. Forest managers should thus (ⅰ) encourage the development of Reduced-Impact Logging techniques in order to minimize disturbance intensity and (ⅱ) pay a deep attention when drawing management plans to avoid logging pioneer-rich forest units. In the context of increasing disturbances on tropical forests, the lower capacity of disturbed forests to recover is not good news in our fight against climate change.

    We are in debt with all technicians and colleagues who helped setting up the plots and collecting data over years. Without their precious work, this study would have not been possible and they may be warmly thanked here.

    Datasets supporting the conclusions of this article are available upon request to the scientific director of the Paracou Station (https://paracou.cirad.fr).

    BH and CP drew the concept of the paper, developed the modeling mathematical framework and worked on the parametrization of the model. BH worked on the first draft and both authors improved and approved the final manuscript.

    Not applicable.

    Not applicable.

    The authors declare that they have no competing interests.

  • [1]
    Allié E, Pélissier R, Engel J, Petronelli P, Freycon V, Deblauwe V, Soucémarianadin L, Weigel J, Baraloto C (2015) Pervasive Local-Scale Tree-Soil Habitat Association in a Tropical Forest Community. Plos ONE 10(11):e0141488.
    [2]
    Anderegg WRL, Klein T, Bartlett M, Sack L, Pellegrini AFA, Choat B, Jansen S (2016) Meta-analysis reveals that hydraulic traits explain cross-species patterns of drought-induced tree mortality across the globe. Proc Natl Acad Sci 113(18):201525678.
    [3]
    Asner GP, Loarie SR, Heyder U (2010) Combined effects of climate and land-use change on the future of humid tropical forests. Conserv Lett 3(6):395-403.
    [4]
    Aubry-Kientz M, Hérault B, Ayotte-Trépanier C, Baraloto C, Rossi V (2013) Toward trait-based mortality models for tropical forests. PloS one 8(5):e63678.
    [5]
    Aubry-Kientz M, Rossi V, Wagner F, Hérault B (2015) Identifying climatic drivers of tropical forest dynamics. Biogeosciences 12(19):5583-5596.
    [6]
    Baraloto C, Hérault B, Paine CET, Massot H, Blanc L, Bonal D, Molino J-F, Nicolini Ea, Sabatier D (2012) Contrasting taxonomic and functional responses of a tropical tree community to selective logging. J Appl Ecol 49(4):861-870.
    [7]
    Baraloto C, Timothy Paine CE, Poorter L, Beauchene J, Bonal D, Domenach A-M, Hérault B, Patiño S, Roggy J-C, Chave J (2010) Decoupled leaf and stem economics in rain forest trees. Ecol Lett 13(11):1338-47.
    [8]
    Blanc L, Echard M, Herault B, Bonal D, Marcon E, Chave J, Baraloto C (2009) Dynamics of aboveground carbon stocks in a selectively logged tropical forest. Ecol Appl 19(6):1397-1404.
    [9]
    Blaser J, Sarre A, Poore D, Johnson S (2011) Status of Tropical Forest Management 2011. ITTO Technical Series No 38. International Tropical Timber Organization. Yokohama, Japan.
    [10]
    Bonal D, Burban B, Stahl C, Wagner F, Hérault B (2016) The response of tropical rainforests to drought-lessons from recent research and future prospects. Ann For Sci 73(1):27-44.
    [11]
    Brienen RJW, Phillips OL, Feldpausch TR, et al. (2015) Long-term decline of the Amazon carbon sink. Nature 519(7543):344-348.
    [12]
    Brokaw N, Busing R (2000) Niche versus chance and tree diversity in forest gaps. Trends Ecol Evol 15(5):183-188.
    [13]
    Carpenter B, Gelman A, Hoffman MD, Lee D, Goodrich B, Betancourt M, Brubaker M, Guo J, Li P, Riddell A (2017) Stan : A Probabilistic Programming Language. J Stat Softw 76(1). doi: .
    [14]
    Chave J, Coomes D, Jansen S, Lewis SL, Swenson NG, Zanne AE (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12(4):351-366.
    [15]
    Condit R (1995) Research in large, long-term tropical forest plots. Trends Ecol Evol 10(1):18-22.
    [16]
    Davidson Ea, de Araújo AC, Artaxo P, Balch JK, Brown IF, C Bustamante MM, Coe MT, DeFries RS, Keller M, Longo M, Munger JW, Schroeder W, Soares-Filho BS, Souza CM, Wofsy SC (2012) The Amazon basin in transition. Nature 481(7381):321-8.
    [17]
    Denslow JS, Ellison AM, Sanford RE (1998) Treefall gap size effects on above- and below-ground processes in a tropical wet forest. J Ecol 86(4):597-609.
    [18]
    Dezécache C, Faure E, Gond V, Salles J-M, Vieilledent G, Hérault B (2017a) Gold-Rush in a forested El Dorado: Deforestation leakages and the need for regional cooperation. Environ Res Lett 034013:19.
    [19]
    Dezécache C, Salles JM, Vieilledent G, Hérault B (2017b) Moving forward socio-economically focused models of deforestation. Glob Chang Biol 23(9):3484-3500.
    [20]
    Doughty CE, Metcalfe DB, Girardin CAJ, Amézquita FF, Cabrera DG, Huasco WH, Silva-Espejo JE, Araujo-Murakami A, da Costa MC, Rocha W, Feldpausch TR, Mendoza ALM, da Costa ACL, Meir P, Phillips OL, Malhi Y (2015) Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519(7541):78-82.
    [21]
    Fargeon H, Aubry-kientz M, Brunaux O, Descroix L, Guitet S, Rossi V, Hérault B (2016) Vulnerability of commercial tree species to water stress in logged forests of the Guiana shield. Forests 7(105):1-21.
    [22]
    Fayad I, Baghdadi N, Guitet S, Bailly J-S, Hérault B, Gond V, El Hajj M, Tong Minh DH (2016) Aboveground biomass mapping in French Guiana by combining remote sensing, forest inventories and environmental data. Int J Appl Earth Obs Geoinformation 52(August):502-514.
    [23]
    Feeley KJ, Joseph Wright S, Nur Supardi MN, Kassim AR, Davies SJ (2007) Decelerating growth in tropical forest trees. Ecol Lett 10(6):461-9.
    [24]
    Feldpausch TR, Prates-Clark CdC, Fernandes EC, Riha SJ (2007) Secondary forest growth deviation from chronosequence predictions in central Amazonia. Glob Chang Biol 13(5):967-979.
    [25]
    Ferry B, Bontemps J-d, Blanc L, Freycon V, Nancy E, Nancy F (2010) Higher treefall rates on slopes and waterlogged soils result in lower stand biomass and productivity in a tropical rain forest. J Ecol 98:106-116.
    [26]
    Flores O, Hérault B, Delcamp M, Garnier E, Gourlet-Fleury S (2014) Functional traits help predict post-disturbance demography of tropical trees. PloS one 9(9):e105022.
    [27]
    Goulamoussène Y, Bedeau C, Descroix L, Deblauwe V, Linguet L, Hérault B (2016) Weak Environmental Controls of Tropical Forest Canopy Height in the Guiana Shield. Remote Sens 8:747.
    [28]
    Goulamoussène Y, Bedeau C, Descroix L, Linguet L, Hérault B (2017) Environmental control of natural gap size distribution in tropical forests. Biogeosciences 14(2):353-364.
    [29]
    Grau O, Peñuelas J, Ferry B, Freycon V, Blanc L, Desprez M, Baraloto C, Chave J, Descroix L, Dourdain A, Guitet S, Janssens IA, Sardans J, Hérault B (2017) Nutrient-cycling mechanisms other than the direct absorption from soil may control forest structure and dynamics in poor Amazonian soils. Sci Rep 7(February):45017.
    [30]
    Guitet S, Hérault B, Molto Q, Brunaux O, Couteron P (2015) Spatial Structure of Above-Ground Biomass Limits Accuracy of Carbon Mapping in Rainforest but Large Scale Forest Inventories Can Help to Overcome. PLoS ONE 10(9):e0138456.
    [31]
    Guitet S, Sabatier D, Brunaux O, Hérault B, Aubry-Kientz M, Molino J-F, Baraloto C (2014) Estimating tropical tree diversity indices from forestry surveys: A method to integrate taxonomic uncertainty. For Ecol Manag 328:270-281.
    [32]
    He Q, Bertness MD, Altieri AH (2013) Global shifts towards positive species interactions with increasing environmental stress. Ecol Lett 16(5):695-706.
    [33]
    Hérault B, Bachelot B, Poorter L, Rossi V, Bongers F, Chave J, Paine CET, Wagner F, Baraloto C (2011) Functional traits shape ontogenetic growth trajectories of rain forest tree species. J Ecol 99(6):1431-1440.
    [34]
    Hérault B, Gourlet-Fleury S (2016) Will Tropical Rainforests Survive Climate Change? In: Torquebiau E (ed)Climate Change and Agriculture Worldwide. Editions Q. Versailles, 183-196.. Springer, France. doi: .
    [35]
    Herault B, Ouallet J, Blanc L, Wagner F, Baraloto C (2010) Growth responses of neotropical trees to logging gaps. J Appl Ecol 47(4):821-831.
    [36]
    Joetzjer E, Douville H, Delire C, Ciais P (2013) Present-day and future Amazonian precipitation in global climate models: CMIP5 versus CMIP3. Clim Dyn 41(11-12):2921-2936.
    [37]
    Johnson MO, Galbraith D, Gloor M, De Deurwaerder H, Guimberteau M, Rammig A, Thonicke K, Verbeeck H, von Randow C, Monteagudo A, Phillips OL, Brienen RJ, Feldpausch TR, Lopez Gonzalez G, Fauset S, Quesada CA, Christoffersen B, Ciais P, Sampaio G, Kruijt B, Meir P, Moorcroft P, Zhang K, Alvarez-Davila E, Alves de Oliveira A, Amaral I, Andrade A, Aragao LE, Araujo-Murakami A, Arets EJ, Arroyo L, Aymard GA, Baraloto C, Barroso J, Bonal D, Boot R, Camargo J, Chave J, Cogollo A, Cornejo Valverde F, Lola da Costa AC, Di Fiore A, Ferreira L, Higuchi N, Honorio EN, Killeen TJ, Laurance SG, Laurance WF, Licona J, Lovejoy T, Malhi Y, Marimon B, Marimon BH, Matos DC, Mendoza C, Neill DA, Pardo G, Peña-Claros M, Pitman NC, Poorter L, Prieto A, Ramirez-Angulo H, Roopsind A, Rudas A, Salomao RP, Silveira M, Stropp J, ter Steege H, Terborgh J, Thomas R, Toledo M, Torres-Lezama A, van der Heijden GM, Vasquez R, Guimares Vieirã IC, Vilanova E, Vos VA, Baker TR (2016). Variation in stem mortality rates determines patterns of above-ground biomass in Amazonian forests: implications for dynamic global vegetation models. Glob Chang Biol 22(12):3996-4013.
    [38]
    Körner C (2017) A matter of tree longevity. Science 355(6321):130-131.
    [39]
    Kraft NJB, Valencia R, Ackerly DD (2008) Functional traits and niche-based tree community assembly in an Amazonian forest. Science 322(5901):580-2.
    [40]
    Kunstler G, Falster D, Coomes DA, Hui F, Kooyman RM, Laughlin DC, Poorter L, Vanderwel M, Vieilledent G, Wright SJ, Aiba M, Baraloto C, Caspersen J, Cornelissen JHC, Gourlet-Fleury S, Hanewinkel M, Herault B, Kattge J, Kurokawa H, Onoda Y, Peñuelas J, Poorter H, Uriarte M, Richardson S, Ruiz-Benito P, Sun I-F, Ståhl G, Swenson NG, Thompson J, Westerlund B, Wirth C, Zavala MA, Zeng H, Zimmerman JK, Zimmermann NE, Westoby M (2016) Plant functional traits have globally consistent effects on competition. Nature 529(7585):204-207.
    [41]
    Liang J, Crowther TW, Picard N, Wiser S, Zhou M, Alberti G, Schulze E-D, McGuire AD, Bozzato F, Pretzsch H, De-Miguel S, Paquette A, Herault B, Scherer-Lorenzen M, Barrett CB, Glick HB, Hengeveld GM, Nabuurs G-J, Pfautsch S, Viana H, Vibrans AC, Ammer C, Schall P, Verbyla D, Tchebakova N, Fischer M, Watson JV, Chen HYH, Lei X, Schelhaas M-J, Lu H, Gianelle D, Parfenova EI, Salas C, Lee E, Lee B, Kim HS, Bruelheide H, Coomes DA, Piotto D, Sunderland T, Schmid B, Gourlet-Fleury S, Sonke B, Tavani R, Zhu J, Brandl S, Vayreda J, Kitahara F, Searle EB, Neldner VJ, Ngugi MR, Baraloto C, Frizzera L, Ba azy R, Oleksyn J, Zawi a Nied wiecki T, Bouriaud O, Bussotti F, Finer L, Jaroszewicz B, Jucker T, Valladares F, Jagodzinski AM, Peri PL, Gonmadje C, Marthy W, OBrien T, Martin EH, Marshall AR, Rovero F, Bitariho R, Niklaus PA, Alvarez-Loayza P, Chamuya N, Valencia R, Mortier F, Wortel V, Engone-Obiang NL, Ferreira LV, Odeke DE, Vasquez RM, Lewis SL, Reich PB (2016) Positive biodiversity-productivity relationship predominant in global forests. Science 354(6309):aaf8957-aaf8957.
    [42]
    Lindenmayer DB, Laurance WF, Franklin JF (2012) Ecology. Global decline in large old trees. Science 338(6112):1305-6.
    [43]
    Malhi Y, Aragao LEOC, Galbraith D, Huntingford C, Fisher R, Zelazowski P, Sitch S, McSweeney C, Meir P (2009) Exploring the likelihood and mechanism of a climate-change-induced dieback of the Amazon rainforest. Proc Natl Acad Sci 106(49):20610-20615.
    [44]
    Phillips OL, Aragão LEOC, Lewis SL, Fisher JB, Lloyd J, López-Gonzȧlez G, Malhi Y, Monteagudo A, Peacock J, Quesada CA, van der Heijden G, Almeida S, Amaral I, Arroyo L, Aymard G, Baker TR, Bánki O, Blanc L, Bonal D, Brando P, Chave J, de Oliveira ACA, Cardozo ND, Czimczik CI, Feldpausch TR, Freitas MA, Gloor E, Higuchi N, Jiménez E, Lloyd G, Meir P, Mendoza C, Morel A, Neill DA, Nepstad D, Patiño S, Peñuela MC, Prieto A, Ramirez F, Schwarz M, Silva J, Silveira M, Thomas AS, Steege HT, Stropp J, Vȧsquez R, Zelazowski P, Alvarez Dávila E, Andelman S, Andrade A, Chao K-j, Erwin T, Di Fiore A, Honorio CE, Keeling H, Killeen TJ, Laurance WF, Peña Cruz A, Pitman NCA, Núñez Vargas P, Ramirez-Angulo H, Rudas A, Salamȧo R, Silva N, Terborgh J, Torres-Lezama A (2009) Drought sensitivity of the Amazon rainforest. Sci (New York, NY) 323(5919):1344-1347.
    [45]
    Pillet M, Joetzjer E, Belmin C, Chave J, Ciais P, Dourdain A, Evans M, Hérault B, Luyssaert S, Poulter B (2017) Disentangling competitive vs. climatic drivers of tropical forest mortality. J Ecol 38(1):42-49.
    [46]
    Piponiot C, Cabon A, Descroix L, Dourdain A, Mazzei L, Ouliac B, Rutishauser E, Sist P, Hérault B (2016a) A methodological framework to assess the carbon balance of tropical managed forests. Carbon Balance Manag 11(1):15.
    [47]
    Piponiot C, Sist P, Mazzei L, Peña-Claros M, Putz FE, Rutishauser E, Shenkin A, Ascarrunz N, de Azevedo CP, Baraloto C, França M, Guedes M, Honorio Coronado EN, D'Oliveira MV, Ruschel AR, da Silva KE, Doff Sotta E, de Souza CR, Vidal E, West TA, Hérault B (2016b) Carbon recovery dynamics following disturbance by selective logging in Amazonian forests. eLife 5:e21394.
    [48]
    Réjou-Méchain M, Tanguy A, Piponiot C, Chave J, Hérault B (2017) biomass?: an r package for estimating above-ground biomass and its uncertainty in tropical forests. Methods Ecol Evol 8(9):1163-67. doi: .
    [49]
    Rutishauser E, Hérault B, Baraloto C, Blanc L, Descroix L, Sotta ED, Ferreira J, Kanashiro M, Mazzei L, D'Oliveira MV, de Oliveira LC, Peṅa-Claros M, Putz FE, Ruschel AR, Rodney K, Roopsind A, Shenkin A, da Silva KE, de Souza CR, Toledo M, Vidal E, West TA, Wortel V, Sist P (2015) Rapid tree carbon stock recovery in managed Amazonian forests. Curr Biol 25(18):R787-R788.
    [50]
    Rutishauser E, Hérault B, Petronelli P, Sist P (2016) Tree Height Reduction After Selective Logging in a Tropical Forest. Biotropica 48(3):285-289.
    [51]
    Rutishauser E, Wagner F, Herault B, Nicolini E-A, Blanc L (2010) Contrasting above-ground biomass balance in a Neotropical rain forest. J Veg Sci 21(4):672-82. doi: .
    [52]
    Sheil D, Burslem D (2003) Disturbing hypotheses in tropical forests. Trends Ecol Evol 18(1):5-8.
    [53]
    Sist P, Mazzei L, Blanc L, Rutishauser E (2014) Large trees as key elements of carbon storage and dynamics after selective logging in the Eastern Amazon. For Ecol Manag 318:103-109.
    [54]
    Sist P, Rutishauser E, Peña-Claros M, Shenkin A, Hérault B, Blanc L, Baraloto C, Baya F, Benedet F, da Silva KE, Descroix L, Ferreira JN, Gourlet-Fleury S, Guedes MC, Bin Harun I, Jalonen R, Kanashiro M, Krisnawati H, Kshatriya M, Lincoln P, Mazzei L, Medjibé V, Nasi R, D'Oliveira MVN, de Oliveira LC, Picard N, Pietsch S, Pinard M, Priyadi H, Putz FE, Rodney K, Rossi V, Roopsind A, Ruschel AR, Shari NHZ, Rodrigues de Souza C, Susanty FH, Sotta ED, Toledo M, Vidal E, aP West T, Wortel V, Yamada T (2015) The Tropical managed Forests Observatory: a research network addressing the future of tropical logged forests. Appl Veg Sci 18:171-174.
    [55]
    Slik JWF (2004) El Niño droughts and their effects on tree species composition and diversity in tropical rain forests. Oecologia 141(1):114-120.
    [56]
    Stahl C, Hérault B, Rossi V, Burban B, Bréchet C, Bonal D (2013) Depth of soil water uptake by tropical rainforest trees during dry periods: does tree dimension matter?Oecologia 173(4):1191-201.
    [57]
    Sterck F, Markesteijn L, Schieving F, Poorter L (2011) Functional traits determine trade-offs and niches in a tropical forest community. Proc Natl Acad Sci U S A 108(51):20627-32.
    [58]
    Tan ZH, Cao M, Yu GR, Tang JW, Deng XB, Song QH, Tang Y, Zheng Z, Liu WJ, Feng ZL, Deng Y, Zhang JL, Liang N, Zhang YP (2013) High sensitivity of a tropical rainforest to water variability: Evidence from 10 years of inventory and eddy flux data. J Geophys Res Atmos 118(16):9393-9400.
    [59]
    Thorpe HC, Thomas SC, Caspersen JP (2008) Tree mortality following partial harvests is determned by skidding proximity. Ecol Appl 18(7):1652-1663.
    [60]
    Toledo M, Poorter L, Peña-Claros M, Alarcón A, Balcȧzar J, Leaño C, Licona JC, Llanque O, Vroomans V, Zuidema P, Bongers F (2011) Climate is a stronger driver of tree and forest growth rates than soil and disturbance. J Ecol 99(1):254-264.
    [61]
    Wagner F, Herault B, Rossi V, Hilker T, Maeda E, Sanchez A, Lyapustin A, Galyao L, Wang Y, Aragao L (2017) Climate drivers of the Amazon forest greening. PloS ONE 12:e0180932.
    [62]
    Wagner F, Hérault B, Stahl C, Bonal D, Rossi V (2011) Modeling water availability for trees in tropical forests. Agric For Meteorol 151(9):1202-1213.
    [63]
    Wagner F, Rossi V, Aubry-Kientz M, Bonal D, Dalitz H, Gliniars R, Stahl C, Trabucco A, Hérault B (2014) Pan-tropical analysis of climate effects on seasonal tree growth. PloS ONE 9(3):e92337.
    [64]
    Wagner F, Rossi V, Stahl C, Bonal D, Hérault B (2012) Water availability is the main climate driver of neotropical tree growth. PloS ONE 7(4):e34074.
    [65]
    Wagner F, Rossi V, Stahl C, Bonal D, Hérault B (2013) Asynchronism in leaf and wood production in tropical forests: a study combining satellite and ground-based measurements. Biogeosciences 10(11):7307-7321.
    [66]
    Wagner FH, Hérault B, Bonal D, Stahl C, Anderson LO, Baker TR, Becker GS, Beeckman H, Boanerges Souza D, Botosso PC, Bowman DMJS, Bräuning A, Brede B, Brown FI, Camarero JJ, Camargo PB, Cardoso FCG, Carvalho FA, Castro W, Chagas RK, Chave J, Chidumayo EN, Clark DA, Costa FRC, Couralet C, da Silva Mauricio PH, Dalitz H, de Castro VR, de Freitas Milani JE, de Oliveira EC, de Souza Arruda L, Devineau J-L, Drew DM, Dünisch O, Durigan G, Elifuraha E, Fedele M, Ferreira Fedele L, Figueiredo Filho A, Finger CAG, Franco AC, Freitas Júnior JL, Galvão F, Gebrekirstos A, Gliniars R, Graça PMLdA, Griffiths AD, Grogan J, Guan K, Homeier J, Kanieski MR, Kho LK, Koenig J, Kohler SV, Krepkowski J, Lemos-Filho JP, Lieberman D, Lieberman ME, Lisi CS, Longhi Santos T, López Ayala JL, Maeda EE, Malhi Y, Maria VRB, Marques MCM, Marques R, Maza Chamba H, Mbwambo L, Melgaço KLL, Mendivelso HA, Murphy BP, O'Brien JJ, Oberbauer SF, Okada N, Pélissier R, Prior LD, Roig FA, Ross M, Rossatto DR, Rossi V, Rowland L, Rutishauser E, Santana H, Schulze M, Selhorst D, Silva WR, Silveira M, Spannl S, Swaine MD, Toledo JJ, Toledo MM, Toledo M, Toma T, Tomazello Filho M, Valdez Hernández JI, Verbesselt J, Vieira SA, Vincent G, Volkmer de Castilho C, Volland F, Worbes M, Zanon MLB, Aragão LEOC (2016) Climate seasonality limits leaf carbon assimilation and wood productivity in tropical forests. Biogeosciences 13(8):2537-2562.
  • Related Articles

    [1]Vincenzo Saponaro, Miquel De Cáceres, Daniela Dalmonech, Ettore D'Andrea, Elia Vangi, Alessio Collalti. Assessing the combined effects of forest management and climate change on carbon and water fluxes in European beech forests[J]. Forest Ecosystems, 2025, 12(1): 100290. DOI: 10.1016/j.fecs.2024.100290
    [2]Wenjing Chen, Lei Liu, Daijun Liu, Josep Penuelas, Guoyi Zhou, Zhen Yu, Anchi Wu, Zhurong Wu, Langqin Hua. Droughts and windstorms due to climate change increase variability in species and trait composition of a subtropical monsoon evergreen broadleaf forest in China[J]. Forest Ecosystems, 2025, 12(1): 100253. DOI: 10.1016/j.fecs.2024.100253
    [3]Dong Kook Woo. Potential reduction in carbon fixation capacity under climate change in a Pinus koraiensis forest[J]. Forest Ecosystems, 2024, 11(1): 100183. DOI: 10.1016/j.fecs.2024.100183
    [4]Zhongtong Peng, Qifeng Mo, Liangjun Zhu, Qingao Lu, Jiaqing Cai, Mingming Guo, Kun Xu, Yuandong Zhang. Divergent responses of Picea crassifolia Kom. in different forest patches to climate change in the northeastern Tibetan Plateau[J]. Forest Ecosystems, 2023, 10(1): 100153. DOI: 10.1016/j.fecs.2023.100153
    [5]Lucas Moreau, Evelyne Thiffault, Dominic Cyr, Yan Boulanger, Robert Beauregard. How can the forest sector mitigate climate change in a changing climate? Case studies of boreal and northern temperate forests in eastern Canada[J]. Forest Ecosystems, 2022, 9(1): 100026. DOI: 10.1016/j.fecs.2022.100026
    [6]Kai Xu, Xiangping Wang, Chao Jiang, Jianxi Sun. Assessing the vulnerability of ecosystems to climate change based on climate exposure, vegetation stability and productivity[J]. Forest Ecosystems, 2020, 7(1): 23-23. DOI: 10.1186/s40663-020-00239-y
    [7]Vindhya Prasad Tewari, Raj Kumar Verma, Klaus von Gadow. Climate change effects in the Western Himalayan ecosystems of India: evidence and strategies[J]. Forest Ecosystems, 2017, 4(1): 13-13. DOI: 10.1186/s40663-017-0100-4
    [8]TIAN Xiao-rui, SHU Li-fu, WANG Ming-yu, ZHAO Feng-jun. Forest fire danger ratings in the 2040s for northeastern China[J]. Forest Ecosystems, 2011, 13(2): 85-96. DOI: 10.1007/s11632-011-0204-9
    [9]LI Li-juan, LI Bin, LIANG Li-qiao, LI Jiu-yi, LIU Yu-mei. Effect of climate change and land use on stream flow in the upper and middle reaches of the Taoer River, northeastern China[J]. Forest Ecosystems, 2010, 12(3): 107-115. DOI: 10.1007/s11632-010-0301-1
    [10]Lu Yuanchang, Lei Xiangdong, Jiang Lei. A New Function for Modelling Diameter Frequency Distribution in the Tropical Rain Forest of Xishuangbanna,Southwest of China[J]. Forest Ecosystems, 2003, 5(2): 1-6.
  • Cited by

    Periodical cited type(57)

    1. Nathan G. Kiel, Eileen F. Mavencamp, Monica G. Turner. Sparse subalpine forest recovery pathways, plant communities, and carbon stocks 34 years after stand‐replacing fire. Ecological Monographs, 2025, 95(1) DOI:10.1002/ecm.1644
    2. Soulemane Traoré, Irie Casimir Zo-Bi, Camille Piponiot, et al. Fragmentation is the main driver of residual forest aboveground biomass in West African low forest-high deforestation landscapes. Trees, Forests and People, 2024, 15: 100477. DOI:10.1016/j.tfp.2023.100477
    3. Qiang Zhu, Yanhong Liu, Yingda Wu. Timescale effects on the driving mechanisms of post-fire recruitment in Larix gmelinii population. Forest Ecology and Management, 2024, 554: 121691. DOI:10.1016/j.foreco.2024.121691
    4. Alexa S. Wagner, Katharine L. Stuble. Beta diversity and species turnover in communities of newly recruited seedlings following forest management. Forest Ecology and Management, 2024, 572: 122329. DOI:10.1016/j.foreco.2024.122329
    5. C. Bourgoin, G. Ceccherini, M. Girardello, et al. Human degradation of tropical moist forests is greater than previously estimated. Nature, 2024, 631(8021): 570. DOI:10.1038/s41586-024-07629-0
    6. Qiang Zhu, Yanhong Liu, Yingda Wu, et al. Variation and Driving Mechanisms of Bark Thickness in Larix gmelinii under Surface Fire Regimes. Forests, 2024, 15(1): 96. DOI:10.3390/f15010096
    7. Michael J. Emslie, Murray Logan, Peran Bray, et al. Increasing disturbance frequency undermines coral reef recovery. Ecological Monographs, 2024, 94(3) DOI:10.1002/ecm.1619
    8. Vincyane Badouard, Sylvain Schmitt, Guillaume Salzet, et al. LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data. Ecological Modelling, 2024, 487: 110539. DOI:10.1016/j.ecolmodel.2023.110539
    9. Yawei Hu, Jiongchang Zhao, Yang Li, et al. Biomass and Carbon Stock Capacity of Robinia pseudoacacia Plantations at Different Densities on the Loess Plateau. Forests, 2024, 15(7): 1242. DOI:10.3390/f15071242
    10. Faisal Moola, Yu Zhao Ni, Azim U. Mallik. Clonal response of a recalcitrant understory shrub, Labrador tea (Rhododendron groenlandicum Oeder.) to forest gaps. Canadian Journal of Forest Research, 2024, 54(11): 1296. DOI:10.1139/cjfr-2024-0021
    11. Lisa Mandl, Alba Viana-Soto, Rupert Seidl, et al. Unmixing-based forest recovery indicators for predicting long-term recovery success. Remote Sensing of Environment, 2024, 308: 114194. DOI:10.1016/j.rse.2024.114194
    12. Eliott Maurent, Bruno Hérault, Camille Piponiot, et al. A common framework to model recovery in disturbed tropical forests. Ecological Modelling, 2023, 483: 110418. DOI:10.1016/j.ecolmodel.2023.110418
    13. Qiang Zhu, Yanhong Liu, Yujian Zhang, et al. Population dynamics and individual growth dynamics of Larix gmelinii under non-stand replacing fire. Forest Ecology and Management, 2023, 538: 120951. DOI:10.1016/j.foreco.2023.120951
    14. Aka G.M. Elogne, Camille Piponiot, Irie C. Zo-Bi, et al. Life after fire - Long-term responses of 20 timber species in semi-deciduous forests of West Africa. Forest Ecology and Management, 2023, 538: 120977. DOI:10.1016/j.foreco.2023.120977
    15. Jing Li, Junko Morimoto, Wataru Hotta, et al. The 30-year impact of post-windthrow management on the forest regeneration process in northern Japan. Landscape and Ecological Engineering, 2023, 19(2): 227. DOI:10.1007/s11355-023-00539-9
    16. Nahidul Hoque Samrat, Nahina Islam, Amlan Haque. Forest Land Cover Changes and Its Socio-economic Consequences on South-eastern Part of Bangladesh. Journal of Sustainable Forestry, 2023, 42(5): 487. DOI:10.1080/10549811.2022.2045505
    17. E. Sanial, F. Ruf, D. Louppe, et al. Local farmers shape ecosystem service provisioning in West African cocoa agroforests. Agroforestry Systems, 2023, 97(3): 401. DOI:10.1007/s10457-021-00723-6
    18. Madhab Chandra Behera, Uttam Kumar Sahoo, Tanmay Lalitendu Mohanty, et al. Species Composition and Diversity of Plants along Human-Induced Disturbances in Tropical Moist Sal Forests of Eastern Ghats, India. Forests, 2023, 14(10): 1931. DOI:10.3390/f14101931
    19. Sai S. Nudurupati, Erkan Istanbulluoglu, Gregory E. Tucker, et al. On Transient Semi‐Arid Ecosystem Dynamics Using Landlab: Vegetation Shifts, Topographic Refugia, and Response to Climate. Water Resources Research, 2023, 59(4) DOI:10.1029/2021WR031179
    20. Esteban Pinto, Francisco Cuesta, Antonella Bernardi, et al. Determinants of above-ground carbon stocks and productivity in secondary forests along a 3000-m elevation gradient in the Ecuadorian Andes. Plant Ecology & Diversity, 2023, 16(3-4): 127. DOI:10.1080/17550874.2023.2274844
    21. Camille Girard‐Tercieux, Isabelle Maréchaux, Adam T. Clark, et al. Rethinking the nature of intraspecific variability and its consequences on species coexistence. Ecology and Evolution, 2023, 13(3) DOI:10.1002/ece3.9860
    22. Daniel DeArmond, Fabiano Emmert, Alberto C. M. Pinto, et al. A Systematic Review of Logging Impacts in the Amazon Biome. Forests, 2023, 14(1): 81. DOI:10.3390/f14010081
    23. Marie Ruth Dago, Irie Casimir Zo-Bi, Vincyane Badouard, et al. Concomitant effects of multiple disturbances (logging, fire, biological invasion) on native tree abundances into West Africa's semi-deciduous forests. Biological Conservation, 2023, 285: 110220. DOI:10.1016/j.biocon.2023.110220
    24. Álvaro Cañadas-López, Paul Gamboa-Trujillo, Santiago Buitrón-Garrido, et al. Thinning Levels of Laurel Natural Regeneration to Establish Traditional Agroforestry Systems, Ecuadorian Amazon Upper Basin. Forests, 2023, 14(4): 667. DOI:10.3390/f14040667
    25. Samaria Armenta Montero, Edward A. Ellis. Carbon stock recovery from tree regeneration following selective logging in tropical forest of the Yucatan Peninsula, Mexico. Carbon Management, 2023, 14(1) DOI:10.1080/17583004.2023.2238672
    26. Weerapong Unawong, Siriphong Yaemphum, Anuttara Nathalang, et al. Variations in leaf water status and drought tolerance of dominant tree species growing in multi-aged tropical forests in Thailand. Scientific Reports, 2022, 12(1) DOI:10.1038/s41598-022-10988-1
    27. Dhirender Kumar, C. L. Thakur, D. R. Bhardwaj, et al. Biodiversity conservation and carbon storage of Acacia catechu willd. Dominated northern tropical dry deciduous forest ecosystems in north-western Himalaya: Implications of different forest management regimes. Frontiers in Environmental Science, 2022, 10 DOI:10.3389/fenvs.2022.981608
    28. P A Mandasari, M Adim, St Aisjah, et al. The capacity of agroforestry and food crop system for C stock and sequestration (case study on Saobi Island Madura). IOP Conference Series: Earth and Environmental Science, 2022, 1005(1): 012011. DOI:10.1088/1755-1315/1005/1/012011
    29. Sylvain Schmitt, Niklas Tysklind, Myriam Heuertz, et al. Selection in space and time: Individual tree growth is adapted to tropical forest gap dynamics. Molecular Ecology, 2022. DOI:10.1111/mec.16392
    30. Koyel Sam. Modeling the effectiveness of natural and anthropogenic disturbances on forest health in Buxa Tiger Reserve, India, using fuzzy logic and AHP approach. Modeling Earth Systems and Environment, 2022, 8(2): 2261. DOI:10.1007/s40808-021-01227-z
    31. Bienvenu H K Amani, Anny Estelle N’Guessan, Victor Van der Meersch, et al. Lessons from a regional analysis of forest recovery trajectories in West Africa. Environmental Research Letters, 2022, 17(11): 115005. DOI:10.1088/1748-9326/ac9b4f
    32. Elahe Parvizi, Ludovic Dutoit, Ceridwen I. Fraser, et al. Concordant phylogeographic responses to large‐scale coastal disturbance in intertidal macroalgae and their epibiota. Molecular Ecology, 2022, 31(2): 646. DOI:10.1111/mec.16245
    33. Goualo Y.A. Doua-Bi, Irie C. Zo-Bi, Bienvenu H.K. Amani, et al. Taking advantage of natural regeneration potential in secondary forests to recover commercial tree resources in Côte d’Ivoire. Forest Ecology and Management, 2021, 493: 119240. DOI:10.1016/j.foreco.2021.119240
    34. Victor Van der Meersch, Irie Casimir Zo-Bi, Bienvenu H. K. Amani, et al. Causes and consequences of Cedrela odorata invasion in West African semi-deciduous tropical forests. Biological Invasions, 2021, 23(2): 537. DOI:10.1007/s10530-020-02381-8
    35. Sylvain Schmitt, Niklas Tysklind, Bruno Hérault, et al. Topography drives microgeographic adaptations of closely related species in two tropical tree species complexes. Molecular Ecology, 2021, 30(20): 5080. DOI:10.1111/mec.16116
    36. Bienvenu H.K. Amani, Anny E. N'Guessan, Géraldine Derroire, et al. The potential of secondary forests to restore biodiversity of the lost forests in semi-deciduous West Africa. Biological Conservation, 2021, 259: 109154. DOI:10.1016/j.biocon.2021.109154
    37. Mariana Villagra, Carolina Paola Trentini, Débora di Francescantonio, et al. Disturbance as a driver of trait assembly in liana communities in a semi-deciduous Atlantic Forest. Plant Ecology, 2021, 222(7): 773. DOI:10.1007/s11258-021-01142-4
    38. Sylvain Schmitt, Niklas Tysklind, Géraldine Derroire, et al. Topography shapes the local coexistence of tree species within species complexes of Neotropical forests. Oecologia, 2021, 196(2): 389. DOI:10.1007/s00442-021-04939-2
    39. Mingxin Liu, Gregory J. Jordan, Christopher P. Burridge, et al. Metabarcoding reveals landscape drivers of beetle community composition approximately 50 years after timber harvesting. Forest Ecology and Management, 2021, 488: 119020. DOI:10.1016/j.foreco.2021.119020
    40. Nikolai Knapp, Rico Fischer, Victor Cazcarra-Bes, et al. Structure metrics to generalize biomass estimation from lidar across forest types from different continents. Remote Sensing of Environment, 2020, 237: 111597. DOI:10.1016/j.rse.2019.111597
    41. Marcos Longo, Sassan Saatchi, Michael Keller, et al. Impacts of Degradation on Water, Energy, and Carbon Cycling of the Amazon Tropical Forests. Journal of Geophysical Research: Biogeosciences, 2020, 125(8) DOI:10.1029/2020JG005677
    42. Wanlong Sun, Xuehua Liu. Review on carbon storage estimation of forest ecosystem and applications in China. Forest Ecosystems, 2020, 7(1) DOI:10.1186/s40663-019-0210-2
    43. Sylvain Schmitt, Isabelle Maréchaux, Jerome Chave, et al. Functional diversity improves tropical forest resilience: Insights from a long‐term virtual experiment. Journal of Ecology, 2020, 108(3): 831. DOI:10.1111/1365-2745.13320
    44. Sylvain Schmitt, Bruno Hérault, Émilie Ducouret, et al. Topography consistently drives intra‐ and inter‐specific leaf trait variation within tree species complexes in a Neotropical forest. Oikos, 2020, 129(10): 1521. DOI:10.1111/oik.07488
    45. Lunyan Wang, Wenmin Li, Huimin Li. Decision-making for ecological landslide prevention in tropical rainforests. Natural Hazards, 2020, 103(1): 985. DOI:10.1007/s11069-020-04022-8
    46. A. Mirabel, B. Hérault, E. Marcon. Diverging taxonomic and functional trajectories following disturbance in a Neotropical forest. Science of The Total Environment, 2020, 720: 137397. DOI:10.1016/j.scitotenv.2020.137397
    47. Purva Jain, Raihan Ahmed, Sufia Rehman, et al. Detecting disturbed forest tracts in the Sariska Tiger Reserve, India, using forest canopy density and fragmentation models. Modeling Earth Systems and Environment, 2020, 6(3): 1373. DOI:10.1007/s40808-020-00755-4
    48. Mélaine Aubry-Kientz, Vivien Rossi, Guillaume Cornu, et al. Temperature rising would slow down tropical forest dynamic in the Guiana Shield. Scientific Reports, 2019, 9(1) DOI:10.1038/s41598-019-46597-8
    49. Guillaume Odonne, Martijn van den Bel, Maxime Burst, et al. Long‐term influence of early human occupations on current forests of the Guiana Shield. Ecology, 2019, 100(10) DOI:10.1002/ecy.2806
    50. Anny Estelle N'Guessan, Justin Kassi N'dja, Olivier N. Yao, et al. Drivers of biomass recovery in a secondary forested landscape of West Africa. Forest Ecology and Management, 2019, 433: 325. DOI:10.1016/j.foreco.2018.11.021
    51. Benjamin Yguel, Camille Piponiot, Ariane Mirabel, et al. Beyond species richness and biomass: Impact of selective logging and silvicultural treatments on the functional composition of a neotropical forest. Forest Ecology and Management, 2019, 433: 528. DOI:10.1016/j.foreco.2018.11.022
    52. Camille Piponiot, Géraldine Derroire, Laurent Descroix, et al. Assessing timber volume recovery after disturbance in tropical forests – A new modelling framework. Ecological Modelling, 2018, 384: 353. DOI:10.1016/j.ecolmodel.2018.05.023
    53. Ulrike Hiltner, Andreas Huth, Achim Bräuning, et al. Simulation of succession in a neotropical forest: High selective logging intensities prolong the recovery times of ecosystem functions. Forest Ecology and Management, 2018, 430: 517. DOI:10.1016/j.foreco.2018.08.042
    54. Daniel Magnabosco Marra, Susan E. Trumbore, Niro Higuchi, et al. Windthrows control biomass patterns and functional composition of Amazon forests. Global Change Biology, 2018, 24(12): 5867. DOI:10.1111/gcb.14457
    55. J. Aaron Hogan, Bruno Hérault, Bénédicte Bachelot, et al. Understanding the recruitment response of juvenile Neotropical trees to logging intensity using functional traits. Ecological Applications, 2018, 28(8): 1998. DOI:10.1002/eap.1776
    56. Loutfy El-Juhany, Irfan Ahmad, Mirza Barjees Baig, et al. The Food Security, Biodiversity, and Climate Nexus. DOI:10.1007/978-3-031-12586-7_15
    57. Emilio Vilanova. Silviculture. DOI:10.5772/intechopen.93279

    Other cited types(0)

Catalog

    Figures(6)  /  Tables(2)

    Article Metrics

    Article views (275) PDF downloads (0) Cited by(57)
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return