Background

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 smallscale 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 longlasting 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 riskoftransitionfromshort-dry-seasonrainforesttolongdry-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 lightdemanding 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.

Thisstudydrawsuponthelong-termdisturbanceexperiment 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 processbased 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 demographicprocesses,hasbeenshowntobeessentialtoreveal mechanisms underlying ACS responses to disturbance and to make more robust predictions of ACS recovery(Piponiot et al.2016b).

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Methods Study site

Climate driversWe considered two main sources of climate stress:soil drought CDROUGHTand 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.CDROUGHTwas 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 isfull of water,defined CWATER.These two covariates were computed between 2 consecutive censuses and then standardized at a yearly time-step.

The two variables chosen to describe the pre-disturbance forest structure,i.e.basal area SBAand stem density SN,have contrasted behaviors.Basically,associated parameters get their highest absolute values for the two growth models,bothSurvivors’andRecruits’(Fig.1aandd),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 SBAvalues and low SNvalues(Fig.1a).Contribution of Recruits’growth to ACS recovery declines slowly with high SNvalues(Fig.1d).Mature forests(high SBA,low SN)thus recovers faster than regenerating ones(Fig.2).The relativeimportanceofthepre-disturbanceforeststructure on the variability of ACS recovery rates is low,with 30 to 50 MgC·ha−1recovered after 30 years(Fig.6).

Input data

Aboveground Carbon Stock(ACS)computationIn 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 woodspecificgravityvaluefromtheGlobalWoodDensity 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 intensityAfter disturbance,the subplot’s ACS decreases rapidly until it reaches its minimum value acsmina few years later.This transition point determines the beginning of the recovery period.The difference between the averaged pre-disturbance ACS acspreand this post-logging minimum value acsminreached at time t=tmindefines the disturbance intensity DIST.In other words,the disturbance intensity is defined as the amount of abovegroundcarbonlostin the forest ecosystemduring the first years during and after the disturbance.

Structure driversThe 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 m2·ha−1)of subplot j at tpre,the year preceding the disturbance experiment.

Environment driversThree 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 ESLOPEand the standard deviation,i.e. the heterogeneity,of the altitudinal distribution EHETE.

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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 twothirds 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 700woodyspeciesattaining2cmDBH(diameteratbreast 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)).

Modeling strategy

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 tkthe 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−1and ≥10 cm DBH at tk;recruits’ACS growth(Rgj,k)is the ACS increment of living recruits between tk−1and tk;recruits’ACS loss(Rlj,k)is the ACS in recruits that die between tk−1and tk;survivors’ACS growth(Sgj,k)is the ACS increment of living survivors between tk−1and tk;survivors’ACS loss(Slj,k)is the ACS of survivors that die between tk−1and 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:

Recruits

where j is the subplot,k is the census number,tkthe 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).

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SurvivorsSurvivors’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:

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

不失一般性,假设最后一个资源余额的分配部门仅在{1,2}中选择且si-≤s≤si+,i=1,2.则应该将余额分给部门i=1，如果

Becauseofournesteddesignwithsubplotsjwithinplots p,we modeled thevalues with a random plot effect of meanand variance

Parameter is the rate at which survivors’ACS growth on plot j at time tkconverges to a finite limit after the disturbance:it reflects the response rapidity of survivors’ACS growth to disturbance.Because we are interestedinpredictingvariationsin,weexpressedthe latter as a function of covariates:

withthe model intercept,the vector of l parameters associated to the covariates Vj,k,lfor which we looked at their effects on the post-logging ratein 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 compositionoftheforestbeforedisturbance(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 growthplus their initial ACS(acsminj)will have been lost thus defined

Composition driversThe 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 TSLAand maximum diameter TDBH95estimated 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.

withthe finite limits of survivors’cumulative ACS growth and ACS loss respectively,and acsminjthe ACS of the subplot j at tmin=t0.Then the cumulative carbon loss is

where j is the subplot,p the plot it belongs to,tkis the time sinceis the finite limit of the cumulative ACS change,the rate at which the cumulative ACS change converges to this limit andσSl2the variance of the model.And with

withthemodelinterceptthevectorofl parameters associated to the covariatesfor which we looked at their effects on the post-logging ratein subplot j at time tk.

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RecruitsWhen 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:

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,theACSoftreereachingthe10cmDBHthreshold)ishigh at t0because of the competition drop induced by logging,but then progressively decreases to reach its asymptotic value.We modeled it with the following function:

where t is the time since disturbance.As stated before,we choseto modelcumulative ACS changesinsteadof 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 t0to tkannual ACS changes:

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 limitedin-times ACS losses from survivors’mortality(Fig.1e).

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:

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

with R being Rg,Rl or Rr depending on the process we were interested in,withthe model intercept,the vectorofl parametersassociatedtothecovariatesfor which we looked at their effects on the post-logging ratein subplot j at time tk.

Model inference

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.

Identifying the key drivers of the post-disturbance system recovery

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,lwere 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 structureThe 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.

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ForestenvironmentTheeffectofthreecontrastedforest environment were compared:predominance of bottomlands(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 compositionThe 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 statureTDBH95=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.Doing so,the variability of the net carbon balance after 30 years reflects the sensitivity of ecosystem recovery to the varying group of covariates.

Climate stressThe effects of a wetter(nor or a few seasonaldroughtCDROUGHT=−2,highsoilwatersaturation 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.

Results

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 Survivors′ACSgrowth and Newrecruits′ACS(Fig.1a,c)and positive ones for the other three cumulative fluxes(Fig.1b,d,e).

Sensitivity analysis

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,l/=DIST,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 fromU(−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.

Pre-disturbance forest structure

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Local environment

Three variables inform on the forest local environment,i.e.altitudinal heterogeneity EHETE,proportion of bottomlands EBOTTOMand the average slope ESLOPE.The latter never significantly contributes to the β variability(Fig.1).EBOTTOMis 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−1recovered after 30 years(Fig.6).

Pre-disturbance forest composition

Four orthogonal functional traits(i.e.specific leaf area TSLA,maximum stature TDBH95,seed mass TSLEEDand 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 TSLAand low TWD(Fig.1a)while losses declines slowly with high TSLAand high TSEED(Fig.1b).For Recruits,the contribution of growth to ACS recovery is higher but declines quicker with high TDBH95and low TSLAand 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−1recovered after 30 years(Fig.6).

Post-disturbance climate

Two variables related to climate stress occurring during the recovery process were tested.The intensity of the dry season CDROUGHTdecelerates 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).

Discussion

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 preand 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 notoverlap,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 trajectoriesinhighlydiversetropicalforestsforwhichprecise 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 commerciallyvaluable 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).

On disturbance intensity

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

Survivors

所谓设定信用额度，即由厂家针对不同交易经销商设定不同的最大赊销金额，凡是不超过该金额的交易，均可以用信用方式进行。经销商不可能全都是可靠，总会有一些不讲诚信的经销商，借故或无故延迟付款，虽然最终还是支付了货款，但是，厂家仍花费了许多不必要的成本来催收账款，而且还损失了几个月的利息。因此，为了追求利润最大化，W农资公司对经销商设置了一定的信用额度。

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).

③临床实践平台：培养质量是住院医师规范化培训制度的生命线，培训基地是住院医师规范化培训的实施载体，也是培养合格临床医师的“孵化器”。强化培训基地建设，加强全过程督导，以质量管理和考核管理为抓手，努力推动各项教学制度的落实，对带教师资、管理人员定期组织培训，提高各培训医院的管理和带教水平；在培训过程中，定期组织专家对培训基地建设情况、教学情况进行检查督导，通过有效的评估与反馈，严把培训质量关。严格统一考核，根据各学科考核要求，建立结业综合考核试题库，制定考核方案，形成综合考核规程。

where j is the subplot,p the plot,tkis the time sincet0,R is the annual ACS change,either Rr,Rg or Rl and(σR)2the 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 thevalues with a random plot effect of meanand variance

通过对实验过程中所有声发射原始波形进行小波包分解，统计两类传感器采集的声发射信号在各频段的能谱系数。图8为两类传感器的声发射信号能谱系数分布图。从图上可以清楚地看出宽频传感器接收的声发射信号除在S3.4、S3.5这两层能谱系数较低之外，其他分解尺度并无明显规律。窄频传感器接收的声发射信号主要集中在其工作频段范围内，在0～125kHz频段内接收的声发射信号能量占比达到80%以上。而宽频声发射传感器信号接收频域广，接收的声发射信号在不同频段内分布较为均匀，波形信号能谱系数分布较为分散，并未出现具有大能量频段。

Endogeneous drivers

Pre-disturbance Forest structureAll 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 shortliving 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 compositionThe 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 wellknown 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 thesespeciesthatundergo,afterdisturbance,heavylosses.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.

Exogeneous drivers

LocalenvironmentThe forestlocalenvironment 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 EBOTTOMin 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 stressTwo 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−1is 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 totalprecipitation 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.

Conclusion

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.

Acknowledgements

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.

Funding

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).

Availability of data and materials

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

个人内在制约因素中，出现最频繁的是健康状况限制、技能缺乏、精力不够、担心犯罪(图 8)。其中，担心犯罪主要影响女性休闲活动。相反，随着休闲日益成为现代人不可或缺的生活方式，没有休闲习惯和不重视休闲、因休闲感到内疚等因素对休闲者的阻力大为减弱。

Authors’contributions

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.

乔化苹果园盛果期大树冬季整形修剪的目的很多，最主要的有两条：一是打开光路，即：脱“裙子”，打开底光；开“窗子”，打开侧光；摘“帽子”，打开顶光。力求枝枝见光。二是平衡树势。实质就是调整果树生长与结果的矛盾，使主干上的主枝之间、主枝上的侧枝之间营养分配基本平衡。

Authors’information

BH is an ecological modeler at Cirad(French research institute specialized in development-oriented research for the tropics).He was the scientific coordinator of the Paracou Research Station from 2012 to 2017.He is currently welcomed by Institut National Polytechnique Félix Houphouët-Boigny,Ivory Coast,to develop researches on forest restoration in West Africa.CP is a recent forest engineer from France.She is a PhD candidate at the Joint Research Unit’Ecology of French Guianan Forests’-Université de Guyane.She is currently working on modeling the recovery of ecosystem services in disturbed amazonian forests under the Tropical Managed Forest Observatory(http://www.tmfo.org)framework.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Author details

1Cirad,UMR EcoFoG(AgroParistech,CNRS,Inra,Université des Antilles,Université de la Guyane),Campus Agronomique,97310 Kourou,French Guiana,France.2INPHB(Institut National Polytechnique Félix Houphouët Boigny),Yamoussoukro,Ivory Coast.3Université de la Guyane,UMR EcoFoG(AgroParistech,Cirad,CNRS,Inra,Université des Antilles),Campus Agronomique,97310 Kourou,French Guiana,France.

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