Forest Entomology and Pathology:Volume 1:Entomology

Released July 1, 2023 by Springer, this 822 page book in English consists of 23 chapters. Chapters 1–8 address basic entomology concepts from morphology and function to general insect ecology, population dynamics, insect and natural enemy interactions, insect-plant interactions, and insect and forest succession.

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。

Estimation of forest volumes by integrating Landsat TM imagery and forest inventory data

Accurate information about forest volumes is essential for forest management planning. The survey interval of the Forest Resource Inventory of China (FRIC) is too long to meet the demand for timely decision-making required for forest protection, management, and utilization. Analysis of satellite imagery provides good potential for more frequent reporting of forest parameters. In this study, we describe an application of the k-nearest neighbors (kNN) method to Landsat TM imagery for improving estimation of forest volumes. Several spectral features were tested and compared in forest volume estimations, including normalized difference vegetation index, environmental vegetation index, and the combination of the spectral features. The combined index resulted in the most accurate volume estimations. The kNN estimator and the combined index were then used in forest volume estimation. The estimation error (RMSE) of the total volume was44.2%, much lower than those for Larix forest (the RMSE was 51.7%) and those for the Korean pine and broadleaved forests (the estimation errors were over 71.7% and 88.19%,respectively). This preliminary study demonstrates the potential of forest volume estimations with remote sensing data to provide useful information for forest management if only limited ground information is available.

Using machine learning algorithms to estimate stand volume growth of Larix and Quercus forests based on national-scale Forest Inventory data in China

Estimating the volume growth of forest ecosystems accurately is important for understanding carbon sequestration and achieving carbon neutrality goals.However,the key environmental factors affecting volume growth differ across various scales and plant functional types.This study was,therefore,conducted to estimate the volume growth of Larix and Quercus forests based on national-scale forestry inventory data in China and its influencing factors using random forest algorithms.The results showed that the model performances of volume growth in natural forests(R^(2)=0.65 for Larix and 0.66 for Quercus,respectively)were better than those in planted forests(R^(2)=0.44 for Larix and 0.40 for Quercus,respectively).In both natural and planted forests,the stand age showed a strong relative importance for volume growth(8.6%–66.2%),while the edaphic and climatic variables had a limited relative importance(<6.0%).The relationship between stand age and volume growth was unimodal in natural forests and linear increase in planted Quercus forests.And the specific locations(i.e.,altitude and aspect)of sampling plots exhibited high relative importance for volume growth in planted forests(4.1%–18.2%).Altitude positively affected volume growth in planted Larix forests but controlled volume growth negatively in planted Quercus forests.Similarly,the effects of other environmental factors on volume growth also differed in both stand origins(planted versus natural)and plant functional types(Larix versus Quercus).These results highlighted that the stand age was the most important predictor for volume growth and there were diverse effects of environmental factors on volume growth among stand origins and plant functional types.Our findings will provide a good framework for site-specific recommendations regarding the management practices necessary to maintain the volume growth in China's forest ecosystems.

Models and form factors for stand volume estimation in natural forest ecosystems: a case study of Katarniaghat Wildlife Sanctuary (KGWS), Bahraich District, India

In view of the difficulties in stand volume estimation in natural forests, we derived real form factors and models for volume estimation in these types of forest ecosystems, using Katarniaghat Wildlife Sanctuary as a case study. Tree growth data were obtained for all trees （dbh 〉10 cm） in 4 plots （25 × 25 m） randomly located in each of three strata selected in the forest. The form factor calculated for the stand was 0.42 and a range of 0.42 0.57 was estimated for selected species （density 〉10）. The parameters of model variables were consistent with general growth trends of trees and each was statistically significant. There was no significant difference （p〉0.05） between the observed and predicted volumes for all models and there was very high correlation between observed and predicted volumes. The output of the performance statistics and the logical signs of the regression coefficients of the models demonstrated that they are useful for volume estimation with minimal error. Plotting the biases with respect to considerable regressor variables showed no meaningful and evident trend of bias values along with the independent variables. This showed that the models did not violate regression assumptions and there were no heteroscedacity or multiculnarity problems. We recommend use of the form factors and models in this ecosystem and in similar ones for stand and tree volume estimation.

Estimation of the Volume of Coarse Woody Debris in Eucalyptus Forest using LiDAR Derived Forest Structure Variables

CWD （coarse woody debris） plays an important role in nutrient cycling, habitat for species and more recently carbon accounting in forest ecosystems. LiDAR （light detection and ranging） technology has demonstrated utility in capturing forest structure information. This paper proposes an indirect method of assessing downed CWD using LiDAR derived forest structure variables. Fieldwork was conducted to measure CWD volume in an Eucalyptus forest in Tasmania. A GLM （generalized linear model） to statistically estimate CWD volume in the Eucalyptus forest was developed using a LiDAR derived FCS （forest characterisation scheme）： the openings above the ground, low and medium vegetation, canopy cover, presence of understorey and mid-storey vegetation and high trees, and the vertical canopy density of high trees. Five structural variables were selected for the best model based on AIC （Akaike＇s Information Criterion） by stepwise selection. The applicability of the model was then compared to the outcome of model using field derived variables such as diameter at breast height of trees. The results show that the model using LiDAR derived variables better estimated the amount of CWD. It is concluded that LiDAR derived forest structural variables has the potential to predict the amount of downed CWD in Eucalyptus forest.

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.

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.

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.

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.

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.

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.

Evaluation of three spatial interpolation methods to estimate forest volume in the municipal forest of the Greek island Skyros

Forest volume is of great interest to forest managers.Plot observations of forest volume are available from planning reports.For managers,however,what is relevant are forest volume surfaces.In this study,three interpolation methods were employed to construct continuous surfaces for the evaluation of forest volume at positions for which no measurements are available.For the Municipal Forest of Skyros Island,we compared spatial predictions derived from Inverse Distance Weighting(IDW),Block Kriging(BK),and Block Co-Kriging(BCK)interpolation methods,as applied to data from 120 sample plots.The existence of spatial autocorrelation in the data was identified using correlograms of Moran’s I index.Spatial outliers were identified and excluded from the analysis using the local Moran’s I index.Only slope,of the examined environmental factors,showed an acceptable correlation with forest volume and was used as an auxiliary variable for BCK.The performance of the three methods was evaluated,using an independent validation set and comparing these indices:mean error(ME)and root mean square error.Additionally,for BK and BCK,the indices,standardized ME,average standard error,and the standardized root-mean-square error were used.For the three interpolation methods,the BK method gave the more accurate results;BCK is the next more accurate method and IDW the third.

Improving wood volume predictions in dry tropical forest in the semi-arid Brazil

The volumetric variability of dry tropical forests in Brazil and the scarcity of studies on the subject show the need for the development of techniques that make it possible to obtain adequate and accurate wood volume estimates.In this study,we analyzed a database of thinning trees from a forest management plan in the Contendas de SincoráNational Forest,southwestern Bahia State,Brazil.The data set included a total of 300 trees with a trunk diameter ranging from 5 to 52 cm.Adjustments,validation and statistical selection of four volumetric models were performed.Due to the difference in height values for the same diameter and the low correlation between both variables,we do not suggest models which only use the diameter at breast height(DBH)variable as a predictor because they accommodate the largest estimation errors.In comparing the best single entry model(Hohenald-Krenn)with the Spurr model(best fit model),it is noted that the exclusion of height as a predictor causes the values of 136.44 and 0.93 for Akaike information criterion(AIC)and adjusted determination coefficient(R2 adj),which are poorer than the second best model(Schumacher-Hall).Regarding the minimum sample size,errors in estimation(root mean square error(RMSE)and bias)of the best model decrease as the sample size increases,especially when a larger number of trees with DBH≥15.0 cm are randomly sampled.Stratified sampling by diameter class produces smaller volume prediction errors than random sampling,especially when considering all trees.In summary,the Spurr and Schumacher-Hall models perform better.These models suggest that the total variance explained in the estimates is not less than 95%,producing reliable forecasts of the total volume with shell.Our estimates indicate that the bias around the average is not greater than 7%.Our results support the decision to use regression methods to build models and estimate their parameters,seeking stratification strategies in diameter classes for the sample trees.Volume estimates with valid confidence intervals can be obtained using the Spurr model for the studied dry forest.Stratified sampling of the data set for model adjustment and selection is necessary,since we find significant results with mean error square root values and bias of up to 70%of the total database.

Characteristics and Growing Stocks Volume of Forest Stand in Dry Temperate Forest of Chilas Gilgit-Baltistan

Chilas forest sub division in Diamer district, of Gilgit-Baltistan is located at northern regions of Pakistan. We estimated tree density, diameter, height and volume of the dominant tree species in four blocks (Thore, Chilas, Thak Niat and Gunar) of Chilas forest sub division. The tree density of deodar was maximum with average 26 tree·ha-1 and minimum was of Chalgoza 4 trees·ha-1. The maximum average height showed by the dominant species (Fir, Kail, Deodar, and Chilgoza) of the study area to be 20.40, 16.06, 12.24 and 12.12 m respectively. Moreover the average maximum volume attained by the Kail, Fir, Deodar and Chalgoza trees was 1.92, 1.57, 0.46 and 0.291 m3·tree-1 respectively. Regression analysis was carried out to determine the relationship between diameter (cm), height (m), tree density (trees·ha-1) and volume (m3·ha-1). The findings of the study will help the future scientific management of the forest for sustained yield. The study also provides information about the unexplored growing stock and structure of the forests. Additionally, this study will help to understand the patterns of tree species composition and diversity in the northern part of Pakistan with dry temperate climate.

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Big Basal Area Factor (Big BAF) and Point-3P are two-stage sampling methods. In the first stage the sampling units, in both methods, are Bitterlich points where the selection of the trees is proportional to their basal area. In the second stage, sampling units are trees which are a subset of the first stage trees. In the Big BAF method, the probability of selecting trees in the second stage is made proportional to the two BAFs’ ratio, with a basal area factor larger than that of the first stage. In the Point-3P method the probability of selecting trees, in the second stage, is based on the height prediction and use of a specific random number table. Estimates of the forest stands’ volume and their sampling errors are based on the theory of the product of two random variables. The increasing error in the second stage is small, but the total cost of measuring the trees is much smaller than simply using the first stage, with all the trees measured. In general, the two sampling methods are modern and cost-effective approaches that can be applied in forest stand inventories for forest management purposes and are receiving the growing interest of researchers in the current decade.

Mixed-Species Allometric Equations to Quantify Stem Volume and Tree Biomass in Dry Afromontane Forest of Ethiopia

Volume and biomass equations are essential tools to determine forest productivity and enable forest managers to make informed decisions. However, volume and biomass estimation equations are scarce for Afromontane forests in Africa in general and Ethiopia in particular. This limits our knowledge of the standing volume of wood, biomass, and carbon stock of the forests therein. In this study, we developed a new mixed-species volume and biomass equations for Afromontane forests and compared them with generic pantropical and local models. A total of 193 sampled trees from seven dominant tree species were used to develop the equations. Various volume and biomass equations were fitted using robust linear and nonlinear regression. Model comparison indicated that the best model to estimate stem volume was ln(v)=-9.909+ 0.954*ln(d2h), whereas the best model to estimate biomass was ln(b)=-2.983+ 0.949*ln(ρd2h) . These equations explained over 85% of the variations in the stem volume and biomass measurements. The mean density and basal area of trees in the forest with d ≥ 2 cm was 631.5 stems·ha-1 and 24.4 m2·ha-1. Based on the newly developed equations, the forest has on average 303.0 m3·ha-1 standing volume of wood and 283.8 Mg·ha-1 biomass stock. The newly developed allometric equations derived from this study can be used to accurately determine the stem volume, biomass, and carbon storage in the Afromontane forests in Ethiopia and elsewhere with similar stand characteristics and ecological conditions. By contrast, the generic pan-tropical and other local models appear to provide biased estimates and are not suitable for dry Afromontane forests in Ethiopia. The estimated stem biomass and carbon stock in the Chilimo forest are comparable with the estimates from various tropical forests and woodlands elsewhere in Africa, indicating the importance of dry Afromontane forest for climate change mitigation.

Spatial Distribution of Soil Moisture Content and Tree Volume Estimation in International Institute of Tropical Agriculture Forest, Ibadan, Nigeria

The role of soil moisture in the survival and growth of trees cannot be over-emphasized and it contributes to the net productivity of the forest. However, information on the spatial distribution of the soil moisture content regarding the tree volume in forest ecosystems especially in Nigeria is limited. Therefore, this study combined spatial and ground data to determine soil moisture distribution and tree volume in the International Institute of Tropical Agriculture (IITA) forest, Ibadan. Satellite images of 1989, 1999, 2009 and 2019 were obtained and processed using topographic and vegetation-based models to examine the soil moisture status of the forest. Satellite-based soil moisture obtained was validated with ground soil moisture data collected in 2019. Tree growth variables were obtained for tree volume computation using Newton’s formular. Forest soil moisture models employed in this study include Topographic Wetness Index (TWI), Temperature Dryness Vegetation Index (TDVI) and Modified Normalized Difference Wetness Index (MNDWI). Relationships between index-based and ground base Soil Moisture Content (SMC), as well as the correlation between soil moisture and tree volume, were examined. The study revealed strong relationships between tree volume and TDVI, SMC, TWI with R2 values of 0.91, 0.85, and 0.75, respectively. The regression values of 0.89 between in-situ soil data and TWI and 0.83 with TDVI ascertain the reliability of satellite data in soil moisture mapping. The decision of which index to apply between TWI and TDVI, therefore, depends on available data since both proved to be reliable. The TWI surface is considered to be a more suitable soil moisture prediction index, while MNDWI exhibited a weak relationship (R2 = 0.03) with ground data. The strong relationships between soil moisture and tree volume suggest tree volume can be predicted based on available soil moisture content. Any slight undesirable change in soil moisture could lead to severe forest conditions.

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.

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.