Satellite remote sensing reveals overwhelming recovery of forest from disturbances in Asia

Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of forests.At the same time,detailed accounts of disturbances and forest responses are not yet well quantified in Asia.This study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast Asia.This approach allowed us to detect forest disturbance and quantify the resilience after disturbance.Results showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by disturbances.Specifically,95%of forests had robust resilience and could recover from disturbance within a few decades.The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude.In summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades.The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022.The findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.

Canopy structural heterogeneity drivesαandβspeciesegenetic diversity correlations in a Chinese subtropical forest

Patterns and drivers of species–genetic diversity correlations(SGDCs)have been broadly examined across taxa and ecosystems and greatly deepen our understanding of how biodiversity is maintained.However,few studies have examined the role of canopy structural heterogeneity,which is a defining feature of forests,in shaping SGDCs.Here,we determine what factors contribute toα-andβ-species–genetic diversity correlations(i.e.,α-andβ-SGDCs)in a Chinese subtropical forest.For this purpose,we used neutral molecular markers to assess genetic variation in almost all adult individuals of the dominant tree species,Lithocarpus xylocarpus,across plots in the Ailaoshan National Natural Reserve.We also quantified microhabitat variation by quantifying canopy structure heterogeneity with airborne laser scanning on 201-ha subtropical forest plots.We found that speciesα-diversity was negatively correlated with geneticα-diversity.Canopy structural heterogeneity was positively correlated with speciesα-diversity but negatively correlated with geneticα-diversity.These contrasting effects contributed to the formation of a negativeα-SGDC.Further,we found that canopy structural heterogeneity increases speciesα-diversity and decreases geneticα-diversity by reducing the population size of target species.Speciesβ-diversity,in contrast,was positively correlated with geneticβ-diversity.Differences in canopy structural heterogeneity between plots had non-linear parallel effects on the two levels ofβ-diversity,while geographic distance had a relatively weak effect onβ-SGDC.Our study indicates that canopy structural heterogeneity simultaneously affects plot-level community species diversity and population genetic diversity,and species and genetic turnover across plots,thus drivingα-andβ-SGDCs.

Modeling of Spring Phenology of Boreal Forest by Coupling Machine Learning and Diurnal Temperature Indicators

The roles of diurnal temperature in providing heat accumulation and chilling requirements for vegetation spring phenology differ.Although previous studies have established a stronger correlation between leaf onset and diurnal temperature than between leaf onset and average temperature,current research on modeling spring phenology based on diurnal temperature indicators remains limited.In this study,we confirmed the start of the growing season(SOS)sensitivity to diurnal temperature and average temperature in boreal forest.The estimation of SOS was carried out by employing K-Nearest Neighbor Regression(KNR-TDN)model,Random Forest Regres-sion(RFR-TDN)model,eXtreme Gradient Boosting(XGB-TDN)model and Light Gradient Boosting Machine model(LightGBM-TDN)driven by diurnal temperature indicators during 1982-2015,and the SOS was projected from 2015 to 2100 based on the Coupled Model Intercomparison Project Phase 6(CMIP6)climate scenario datasets.The sensitivity of boreal forest SOS to daytime temperature is greater than that to average temperature and nighttime temperature.The LightGBM-TDN model perform best across all vegetation types,exhibiting the lowest RMSE and bias compared to the KNR-TDN model,RFR-TDN model and XGB-TDN model.By incorporating diurn-al temperature indicators instead of relying only on average temperature indicators to simulate spring phenology,an improvement in the accuracy of the model is achieved.Furthermore,the preseason accumulated daytime temperature,daytime temperature and snow cover end date emerged as significant drivers of the SOS simulation in the study area.The simulation results based on LightGBM-TDN model exhibit a trend of advancing SOS followed by stabilization under future climate scenarios.This study underscores the potential of diurn-al temperature indicators as a viable alternative to average temperature indicators in driving spring phenology models,offering a prom-ising new method for simulating spring phenology.

Heterogeneous occurrence of evergreen broad-leaved forests in East Asia:Evidence from plant fossils

Evergreen broad-leaved forests(EBLFs)are widely distributed in East Asia and play a vital role in ecosystem stability.The occurrence of these forests in East Asia has been a subject of debate across various disciplines.In this study,we explored the occurrence of East Asian EBLFs from a paleobotanical perspective.By collecting plant fossils from four regions in East Asia,we have established the evolutionary history of EBLFs.Through floral similarity analysis and paleoclimatic reconstruction,we have revealed a diverse spatio-temporal pattern for the occurrence of EBLFs in East Asia.The earliest occurrence of EBLFs in southern China can be traced back to the middle Eocene,followed by southwestern China during the late Eoceneeearly Oligocene.Subsequently,EBLFs emerged in Japan during the early Oligocene and eventually appeared in central-eastern China around the Miocene.Paleoclimate simulation results suggest that the precipitation of wettest quarter(PWetQ,mm)exceeding 600 mm is crucial for the occurrence of EBLFs.Furthermore,the heterogeneous occurrence of EBLFs in East Asia is closely associated with the evolution of the Asian Monsoon.This study provides new insights into the occurrence of EBLFs in East Asia.

Latitudinal patterns of tree b-diversity and relevant ecological processes vary across spatial extents in forests of southeastern China

Latitudinal patterns of treeβ-diversity reveal important insights into the biogeographical processes that influence forest ecosystems.Although previous studies have extensively documentedβ-diversity within relatively small spatial extents,the potential drivers ofβ-diversity along latitudinal gradients are still not well understood at larger spatial extents.In this study,we determined whether treeβ-diversity is correlated with latitude in forests of southeastern China,and if so,what ecological processes contribute to these patterns of treeβ-diversity.We specifically aimed to disentangle the relative contributions from interspecific aggregation and environmental filtering across various spatial extents.We delineated regional communities comprising multiple nearby national forest inventory(NFI)plots around random focal plots.The number of NFI plots in a regional community served as a surrogate for spatial extent.We also used a null model to simulate randomly assembled communities and quantify the deviation(β-deviation)between observed and expectedβ-diversity.We found thatβ-diversity decreased along a latitudinal gradient and that this pattern was clearer at larger spatial extents.In addition,latitudinal patterns ofβ-deviation were explained by the degree of species spatial aggregation.We also identified environmental factors that driveβ-deviation in these forests,including precipitation,seasonality,and temperature variation.At larger spatial extents,these environmental variables explained up to 84%of theβ-deviation.Our results reinforce that ecological processes are scale-dependent and collectively contribute to theβ-gradient in subtropical forests.We recommend that conservation efforts maintain diverse forests and heterogeneous environments at multiple spatial extents to mitigate the adverse effects of climate change.

Reconstructing forest and grassland cover changes in China over the past millennium

Historical forest and grassland cover changes not only are critical indicators for quantifying ecological and environmental change processes but also serve as fundamental data for long-term climate change simulations and terrestrial ecosystem carbon emission assessments.However,because of limitations in historical data,quantitative estimations and spatially gridded reconstructions of these changes remain challenging,necessitating further methodological exploration.This study focused on China's present-day land area over the past millennium,objectively capturing the characteristics and drivers of forest and grassland cover changes.On this basis,using the forest transition theory and the space-for-time substitution method,we depicted the historical deforestation process as an inverted“S”curve and developed a model to reconstruct historical forest area changes based on the functional relationship between the forest area and population size dynamics.Subsequently,a gridded forest allocation model was established on the basis of deforestation tendencies.For the grassland cover,we implemented regionspecific methods,such as the cropland area deduction method and the habitat constraint method,to quantitatively reconstruct historical changes.Consequently,we obtained provincial forest and grassland area changes over the past millennium and mapped 10-km-resolution gridded data of forest and grassland cover.The results indicated the following.(1)The methods developed using population data as a proxy objectively reproduced the spatiotemporal evolution of forest and grassland cover in China over the past millennium.These feasible methods offer a novel pathway for the quantitative reconstruction of historical forest and grassland cover changes.(2)The data indicated that China's forest area generally decreased over the past millennium,characterized by a“decrease-then-increase”pattern.The forest area experienced three distinct phases:a slow decline(AD 1000–1650),a rapid decline(AD 1650–1960),and a gradual recovery(AD 1960–2000).The area decreased from 298 million hectares(Mha)in AD 1000 to 89 Mha in AD 1960 before increasing to 153 Mha in AD 2000.Spatially,deforestation began in the middlelower reaches of the Yellow River and gradually expanded to the middle-lower reaches of the Yangtze River,the southern coastal areas of China,southwest China,and northeast China,with the forest cover declining by 27%,40%,58%,55%,and 35%in these regions,respectively.(3)China's grassland area has shown a continuous decline over the past millennium with three phases:stable fluctuation(AD 1000–1600),slow decline(AD 1600–1900),and rapid decline(AD 1900–2000).The grassland area decreased from 305 Mha in AD 1000 to 277 Mha in AD 2000.Notably,zonal grassland areas in Northeast China,Inner Mongolia,Gan-Ning,Qinghai,Xinjiang,and Xizang decreased by 28 Mha over the millennium,whereas nonzonal secondary grassland areas in the hilly and mountainous areas of eastern and southern China increased by 0.3 Mha.

Light Use Efficiency Model Based on Chlorophyll Content Better Captures Seasonal Gross Primary Production Dynamics of Deciduous Broadleaf Forests

Gross primary production(GPP)is a crucial indicator representing the absorption of atmospheric CO_(2) by vegetation.At present,the estimation of GPP by remote sensing is mainly based on leaf-related vegetation indexes and leaf-related biophysical para-meter leaf area index(LAI),which are not completely synchronized in seasonality with GPP.In this study,we proposed chlorophyll content-based light use efficiency model(CC-LUE)to improve GPP estimates,as chlorophyll is the direct site of photosynthesis,and only the light absorbed by chlorophyll is used in the photosynthetic process.The CC-LUE model is constructed by establishing a linear correlation between satellite-derived canopy chlorophyll content(Chlcanopy)and FPAR.This method was calibrated and validated utiliz-ing 7-d averaged in-situ GPP data from 14 eddy covariance flux towers covering deciduous broadleaf forest ecosystems across five dif-ferent climate zones.Results showed a relatively robust seasonal consistency between Chlcanopy with GPP in deciduous broadleaf forests under different climatic conditions.The CC-LUE model explained 88% of the in-situ GPP seasonality for all validation site-year and 56.0% of in-situ GPP variations through the growing season,outperforming the three widely used LUE models(MODIS-GPP algorithm,Vegetation Photosynthesis Model(VPM),and the eddy covariance-light use efficiency model(EC-LUE)).Additionally,the CC-LUE model(RMSE=0.50 g C/(m^(2)·d))significantly improved the underestimation of GPP during the growing season in semi-arid region,re-markably decreasing the root mean square error of averaged growing season GPP simulation and in-situ GPP by 75.4%,73.4%,and 37.5%,compared with MOD17(RMSE=2.03 g C/(m^(2)·d)),VPM(RMSE=1.88 g C/(m^(2)·d)),and EC-LUE(RMSE=0.80 g C/(m^(2)·d))model.The chlorophyll-based method proved superior in capturing the seasonal variations of GPP in forest ecosystems,thereby provid-ing the possibility of a more precise depiction of forest seasonal carbon uptake.

基于Deep Forest算法的对虾急性肝胰腺坏死病(AHPND)预警数学模型构建

为预报池塘养殖凡纳对虾(Penaeus vannamei)急性肝胰腺坏死病(AHPND)的发生,自2020年开始,笔者对凡纳对虾养殖区开展了连续监测工作,包括与疾病发生相关的环境理化因子、微生物因子、虾体自身健康状况等18个候选预警因子指标,通过数据标准化处理后分析病原、宿主与环境之间的相关性,对候选预警因子进行筛选,基于Python语言编程结合Deep Forest、Light GBM、XGBoost算法进行数据建模和预测性能评判,仿真环境为Python2.7,以预警因子指标作为输入样本(即警兆),以对虾是否发病指标作为输出结果(即警情),根据输入样本和输出结果各自建立输入数据矩阵和目标数据矩阵,利用原始数据矩阵对输入样本进行初始化,结合函数方程进行拟合,拟合的源代码能利用已知环境、病原及对虾免疫指标数据对目标警情进行预测。最终建立了基于Deep Forest算法的虾体(肝胰腺内)细菌总数、虾体弧菌(Vibrio)占比、水体细菌总数和盐度的4维向量预警预报模型,准确率达89.00%。本研究将人工智能算法应用到对虾AHPND发生的预测预报,相关研究结果为对虾AHPND疾病预警预报建立了预警数学模型,并为对虾健康养殖和疾病防控提供了技术支撑和有力保障。

Pitfalls in forest carbon sink projection

Global forests are increasingly crucial for achiev-ing net-zero carbon emissions,with a quarter of the miti-gation efforts under the Paris Climate Agreement directed towards forests.In China,forests currently contribute to 13%of the global land’s carbon sink,but their stability and per-sistence remain uncertain.We examined and identified that published studies suffered from oversimplifications of eco-system succession and tree demographic dynamics,as well as poor constraints on land quality.Consequently,substan-tial estimations might have been suffered from underrepre-sented or ignored crucial factors,including tree demographic dynamics,and disturbances and habitat shifts caused by global climate change.We argue that these essential factors should be considered to enhance the reliability and accuracy of assessments of the potential for forest carbon sinks.

Dendroclimatological study of Sabina saltuaria and Abies faxoniana in the mixed forests of the Qionglai Mountains,eastern Tibetan Plateau

Tree-ring chronologies were developed for Sabina saltuaria and Abies faxoniana in mixed forests in the Qionglai Mountains of the eastern Tibetan Plateau.Climate-growth relationship analysis indicated that the two co-exist-ing species reponded similarly to climate factors,although S.saltuaria was more sensitive than A.faxoniana.The strong-est correlation was between S.saltuaria chronology and regional mean temperatures from June to November.Based on this relationship,a regional mean temperature from June to November for the period 1605-2016 was constructed.Reconstruction explained 37.3%of the temperature variance during th period 1961-2016.Six major warm periods and five major cold periods were identified.Spectral analysis detected significant interannual and multi-decadal cycles.Reconstruction also revealed the influence of the Atlantic Multi-decadal Oscillation,confirming its importance on climate change on the eastern Tibetan Plateau.

Implication of community-level ecophysiological parameterization to modelling ecosystem productivity:a case study across nine contrasting forest sites in eastern China

Parameterization is a critical step in modelling ecosystem dynamics.However,assigning parameter values can be a technical challenge for structurally complex natural plant communities;uncertainties in model simulations often arise from inappropriate model parameterization.Here we compared five methods for defining community-level specific leaf area(SLA)and leaf C:N across nine contrasting forest sites along the North-South Transect of Eastern China,including biomass-weighted average for the entire plant community(AP_BW)and four simplified selective sampling(biomass-weighted average over five dominant tree species[5DT_BW],basal area weighted average over five dominant tree species[5DT_AW],biomass-weighted average over all tree species[AT_BW]and basal area weighted average over all tree species[AT_AW]).We found that the default values for SLA and leaf C:N embedded in the Biome-BGC v4.2 were higher than the five computational methods produced across the nine sites,with deviations ranging from 28.0 to 73.3%.In addition,there were only slight deviations(<10%)between the whole plant community sampling(AP_BW)predicted NPP and the four simplified selective sampling methods,and no significant difference between the predictions of AT_BW and AP_BW except the Shennongjia site.The findings in this study highlights the critical importance of computational strategies for community-level parameterization in ecosystem process modelling,and will support the choice of parameterization methods.

Life forms affect beta-diversity patterns of larch forests in China

Beta-diversity reflects the spatial changes in community species composition which helps to understand how communities are assembled and biodiversity is formed and maintained. Larch(Larix) forests, which are coniferous forests widely distributed in the mountainous and plateau areas in North and Southwest China, are critical for maintaining the environmental conditions and species diversity. Few studies of larch forests have examined the beta-diversity and its constituent components(species turnover and nestedness-resultant components). Here, we used 483 larch forest plots to determine the total betadiversity and its components in different life forms(i.e., tree, shrub, and herb) of larch forests in China and to evaluate the main drivers that underlie this beta-diversity. We found that total betadiversity of larch forests was mainly dependent on the species turnover component. In all life forms,total beta-diversity and the species turnover component increased with increasing geographic, elevational, current climatic, and paleoclimatic distances. In contrast, the nestedness-resultant component decreased across these same distances. Geographic and environmental factors explained 20%-25% of total beta-diversity, 18%-27% of species turnover component, and 4%-16% of nestedness-resultant component. Larch forest types significantly affected total beta-diversity and species turnover component. Taken together, our results indicate that life forms affect beta-diversity patterns of larch forests in China, and that beta-diversity is driven by both niche differentiation and dispersal limitation. Our findings help to greatly understand the mechanisms of community assemblies of larch forests in China.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Aboveground carbon sequestration of Cunninghamia lanceolata forests:Magnitude and drivers

Understanding the spatial variation,temporal changes,and their underlying driving forces of carbon sequestration in various forests is of great importance for understanding the carbon cycle and carbon management options.How carbon density and sequestration in various Cunninghamia lanceolata forests,extensively cultivated for timber production in subtropical China,vary with biodiversity,forest structure,environment,and cultural factors remain poorly explored,presenting a critical knowledge gap for realizing carbon sequestration supply potential through management.Based on a large-scale database of 449 permanent forest inventory plots,we quantified the spatial-temporal heterogeneity of aboveground carbon densities and carbon accumulation rates in Cunninghamia lanceolate forests in Hunan Province,China,and attributed the contributions of stand structure,environmental,and management factors to the heterogeneity using quantile age-sequence analysis,partial least squares path modeling(PLS-PM),and hot-spot analysis.The results showed lower values of carbon density and sequestration on average,in comparison with other forests in the same climate zone(i.e.,subtropics),with pronounced spatial and temporal variability.Specifically,quantile regression analysis using carbon accumulation rates along an age sequence showed large differences in carbon sequestration rates among underperformed and outperformed forests(0.50 and 1.80 Mg·ha^(-1)·yr^(-1)).PLS-PM demonstrated that maximum DBH and stand density were the main crucial drivers of aboveground carbon density from young to mature forests.Furthermore,species diversity and geotopographic factors were the significant factors causing the large discrepancy in aboveground carbon density change between low-and high-carbon-bearing forests.Hotspot analysis revealed the importance of culture attributes in shaping the geospatial patterns of carbon sequestration.Our work highlighted that retaining largesized DBH trees and increasing shade-tolerant tree species were important to enhance carbon sequestration in C.lanceolate forests.

Community structure and species diversity dynamics of a subtropical evergreen broad-leaved forest in China:2005 to 2020

Here,we characterize the temporal and spatial dynamics of forest community structure and species diversity in a subtropical evergreen broad-leaved forest in China.We found that community structure in this forest changed over a 15-year period.Specifically,renewal and death of common species was large,with the renewal of individuals mainly concentrated within a few populations,especially those of Aidia canthioides and Cryptocarya concinna.The numbers of individual deaths for common species were concentrated in the small and mid-diameter level.The spatial distribution of community species diversity fluctuated in each monitoring period,showing a more dispersed diversity after the 15-year study period,and the coefficient of variation on quadrats increased.In 2010,the death and renewal of the community and the spatial variation of species diversity were different compared to other survey years.Extreme weather may have affected species regeneration and community stability in our subtropical monsoon evergreen broad-leaved forests.Our findings suggest that strengthening the monitoring and management of the forest community will help better understand the long-and short-term causes of dynamic fluctuations of community structure and species diversity,and reveal the factors that drive changes in community structure.

An Optimized System of Random Forest Model by Global Harmony Search with Generalized Opposition-Based Learning for Forecasting TBM Advance Rate

As massive underground projects have become popular in dense urban cities,a problem has arisen:which model predicts the best for Tunnel Boring Machine(TBM)performance in these tunneling projects?However,performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers.On the other hand,a reliable and accurate prediction of TBM performance is essential to planning an applicable tunnel construction schedule.The performance of TBM is very difficult to estimate due to various geotechnical and geological factors and machine specifications.The previously-proposed intelligent techniques in this field are mostly based on a single or base model with a low level of accuracy.Hence,this study aims to introduce a hybrid randomforest(RF)technique optimized by global harmony search with generalized oppositionbased learning(GOGHS)for forecasting TBM advance rate(AR).Optimizing the RF hyper-parameters in terms of,e.g.,tree number and maximum tree depth is the main objective of using the GOGHS-RF model.In the modelling of this study,a comprehensive databasewith themost influential parameters onTBMtogetherwithTBM AR were used as input and output variables,respectively.To examine the capability and power of the GOGHSRF model,three more hybrid models of particle swarm optimization-RF,genetic algorithm-RF and artificial bee colony-RF were also constructed to forecast TBM AR.Evaluation of the developed models was performed by calculating several performance indices,including determination coefficient(R2),root-mean-square-error(RMSE),and mean-absolute-percentage-error(MAPE).The results showed that theGOGHS-RF is a more accurate technique for estimatingTBMAR compared to the other applied models.The newly-developedGOGHS-RFmodel enjoyed R2=0.9937 and 0.9844,respectively,for train and test stages,which are higher than a pre-developed RF.Also,the importance of the input parameters was interpreted through the SHapley Additive exPlanations(SHAP)method,and it was found that thrust force per cutter is the most important variable on TBMAR.The GOGHS-RF model can be used in mechanized tunnel projects for predicting and checking performance.

一种基于KMeans与Random Forest的异常温升捕捉方法

针对线路老化、线路过载的火灾频发问题,分析了线路老化、线路过载与异常温升之间的关联性,以电流值、线缆温度作为输入,利用KMeans聚类算法划分可能存在异常温升的区间,通过Random Forest算法识别线路过载问题,可以提前通知用户整改线路,预防火灾的发生。

Twenty years of population dynamics in European beech-oak forest at their rear range margin anticipate changes in its structure and composition

There is an increasing interest in restoring degraded forests,which occupy half of the forest areas.Among the forms of restoration,passive restoration,which involves the elimination of degrading factors and the free evolution of natural dynamics by applying minimal or no management,is gaining attention.Natural dynamics is difficult to predict due to the influence of multiple interacting factors such as climatic and edaphic conditions,composition and abundance of species,and the successional character of these species.Here,we study the natural dynamics of a mixed forest located in central Spain,which maintained an open forest structure,due to intensive use,until grazing and cutting were banned in the 1960s.The most frequent woody species in this forest are Fagus sylvatica,Quercus petraea,Quercus pyrenaica,Ilex aquifolium,Sorbus aucuparia,Sorbus aria and Prunus avium,with contrasting shade and drought tolerance.These species are common in temperate European deciduous forest and are found here near their southern distribution limit,except for Q.pyrenaica.In order to analyze forest dynamics and composition,three inventories were carried out in 1994,2005 and 2015.Our results show that,despite the Mediterranean influence,the natural dynamics of this forest has been mainly determined by different levels of shade tolerance.After the abandonment of grazing and cutting,Q.pyrenaica expanded rapidly due to its lower shade tolerance,whereas after canopy closure and forest densification,shade-tolerant species gained ground,particularly F.sylvatica,despite its lower drought and late-frost tolerance.If the current dynamics continue,F.sylvatica will overtake the rest of the species,which will be relegated to sites with shallow soils and steep slopes.Simultaneously,all the multi-centennial beech trees,which are undergoing a rapid mortality and decline process,will disappear.

Assessment of large-scale multiple forest disturbance susceptibilities with AutoML framework: an Izmir Regional Forest Directorate case

Disturbances such as forest fires,intense winds,and insect damage exert strong impacts on forest ecosystems by shaping their structure and growth dynamics,with contributions from climate change.Consequently,there is a need for reliable and operational methods to monitor and map these disturbances for the development of suitable management strategies.While susceptibility assessment using machine learning methods has increased,most studies have focused on a single disturbance.Moreover,there has been limited exploration of the use of“Automated Machine Learning(AutoML)”in the literature.In this study,susceptibility assessment for multiple forest disturbances(fires,insect damage,and wind damage)was conducted using the PyCaret AutoML framework in the Izmir Regional Forest Directorate(RFD)in Turkey.The AutoML framework compared 14 machine learning algorithms and ranked the best models based on AUC(area under the curve)values.The extra tree classifier(ET)algorithm was selected for modeling the susceptibility of each disturbance due to its good performance(AUC values>0.98).The study evaluated susceptibilities for both individual and multiple disturbances,creating a total of four susceptibility maps using fifteen driving factors in the assessment.According to the results,82.5%of forested areas in the Izmir RFD are susceptible to multiple disturbances at high and very high levels.Additionally,a potential forest disturbances map was created,revealing that 15.6%of forested areas in the Izmir RFD may experience no damage from the disturbances considered,while 54.2%could face damage from all three disturbances.The SHAP(Shapley Additive exPlanations)methodology was applied to evaluate the importance of features on prediction and the nonlinear relationship between explanatory features and susceptibility to disturbance.

Evolutionary fingerprint, phylogenetic and forest structure of tropical montane Atlantic cloud forests along an elevation gradient

Environmental conditions can change markedly over geographical distances along elevation gradients,making them natural laboratories to study the processes that structure communities.This work aimed to assess the influences of elevation on Tropical Montane Cloud Forest plant communities in the Brazilian Atlantic Forest,a historically neglected ecoregion.We evaluated the phylogenetic structure,forest structure(tree basal area and tree density)and species richness along an elevation gradient,as well as the evolutionary fingerprints of elevation-success on phylogenetic lineages from the tree communities.To do so,we assessed nine communities along an elevation gradient from 1210 to 2310 m a.s.l.without large elevation gaps.The relationships between elevation and phylogenetic structure,forest structure and species richness were investigated through Linear Models.The occurrence of evolutionary fingerprint on phylogenetic lineages was investigated by quantifying the extent of phylogenetic signal of elevation-success using a genus-level molecular phylogeny.Our results showed decreased species richness at higher elevations and independence between forest structure,phylogenetic structure and elevation.We also verified that there is a phylogenetic signal associated with elevation-success by lineages.We concluded that the elevation is associated with species richness and the occurrence of phylogenetic lineages in the tree communities evaluated in Mantiqueira Range.On the other hand,elevation is not associated with forest structure or phylogenetic structure.Furthermore,closely related taxa tend to have their higher ecological success in similar elevations.Finally,we highlight the fragility of the tropical montane cloud forests in the Mantiqueira Range in face of environmental changes(i.e.global warming)due to the occurrence of exclusive phylogenetic lineages evolutionarily adapted to environmental conditions(i.e.minimum temperature)associated with each elevation range.