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 interpo...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.展开更多
Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstruc...Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.展开更多
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 req...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.展开更多
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 ...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.展开更多
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 diff...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.展开更多
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 ...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.展开更多
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...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.展开更多
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, in...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.展开更多
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 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.展开更多
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 basa...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.展开更多
A better understanding of the structure and dynamics of disturbed forests is key for forecasting their future successional trajectories.Despite vulnerability of subalpine forests to warming climate,little is known as ...A better understanding of the structure and dynamics of disturbed forests is key for forecasting their future successional trajectories.Despite vulnerability of subalpine forests to warming climate,little is known as to how their community composition has responded to disturbances and climate warming over decades.Before the 1970s,subalpine forests on the southeastern Qinghai-Tibet Plateau mainly experienced logging and fire,but afterwards they were more impacted by climate warming.Thus,they provide an excellent setting to test whether disturbances and climate warming led to changes in forest structure.Based on the analysis of 3145 forest inventory plots at 4-to 5-year resolution,we found that spruce-fir forests shifted to pine and broadleaved forests since the early 1970s.Such a turnover in species composition mainly occurred in the 1994e1998 period.By strongly altering site conditions,disturbances in concert with climate warming reshuffle community composition to warm-adapted broadleaf-pine species.Thus,moderate disturbances shifted forest composition through a gradual loss of resilience of spruce-fir forests.Shifts in these foundation species will have profound impacts on ecosystem functions and services.In the future,broadleaved forests could expand more rapidly than evergreen needle-leaved forests under moderate warming scenarios.In addition to climate,the effects of anthropogenic disturbances on subalpine forests should be considered in adaptive forest management and in projections of future forest changes.展开更多
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 fore...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(d<sup>2</sup>h), whereas the best model to estimate biomass was ln(b)=-2.983+ 0.949*ln(ρd<sup>2</sup>h) . 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<sup>-1</sup> and 24.4 m<sup>2</sup>·ha<sup>-1</sup>. Based on the newly developed equations, the forest has on average 303.0 m<sup>3</sup>·ha<sup>-1</sup> standing volume of wood and 283.8 Mg·ha<sup>-1</sup> 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.展开更多
The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We r...The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.展开更多
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 c...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 R<sup>2</sup> 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 (R<sup>2</sup> = 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.展开更多
With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threat...With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.展开更多
Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satell...Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.展开更多
Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the dis...Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the distribution areas of Moso bamboo(Phyllostachys edulis)in China to explore the effects of bamboo invasion on forest structural attributes and diameter–height allometries by comparing paired plots of bamboo,mixed bamboo-tree,and non-bamboo forests along the transects.We found that bamboo invasion decreased the mean and maximum diameter at breast height,maximum height,and total basal area,but increased the mean height,stem density,and scaling exponent for stands.Bamboo also had a higher scaling exponent than tree,particularly in mixed forests,suggesting a greater allocation of biomass to height growth.As invasion intensity increased,bamboo allometry became more plastic and decreased significantly,whereas tree allometry was indirectly promoted by increasing stem density.Additionally,a humid climate may favour the scaling exponents for both bamboo and tree,with only minor contributions from topsoil moisture and nitrogen content.The inherent superiority of diameter–height allometry allows bamboo to outcompete tree and contributes to its invasive success.Our findings provide a theoretical basis for understanding the causes and consequences of bamboo invasion.展开更多
Background:The forest musk deer,a rare fauna species found in China,is famous for its musk secretion which is used in selected Traditional Chinese medicines.However,over-hunting has led to musk deer becoming an endang...Background:The forest musk deer,a rare fauna species found in China,is famous for its musk secretion which is used in selected Traditional Chinese medicines.However,over-hunting has led to musk deer becoming an endangered species,and their survival is also greatly challenged by various high incidence and high mortality respiratory and intestinal diseases such as septic pneumonia and enteritis.Accumulating evidence has demonstrated that Akkermannia muciniphila(AKK)is a promising probiotic,and we wondered whether AKK could be used as a food additive in animal breeding pro-grammes to help prevent intestinal diseases.Methods:We isolated one AKK strain from musk deer feces(AKK-D)using an im-proved enrichment medium combined with real-time PCR.After confirmation by 16S rRNA gene sequencing,a series of in vitro tests was conducted to evaluate the probiotic effects of AKK-D by assessing its reproductive capability,simulated gas-trointestinal fluid tolerance,acid and bile salt resistance,self-aggregation ability,hy-drophobicity,antibiotic sensitivity,hemolysis,harmful metabolite production,biofilm formation ability,and bacterial adhesion to gastrointestinal mucosa.Results:The AKK-D strain has a probiotic function similar to that of the standard strain in humans(AKK-H).An in vivo study found that AKK-D significantly amelio-rated symptoms in the enterotoxigenic Escherichia coli(ETEC)-induced murine diar-rhea model.AKK-D improved organ damage,inhibited inflammatory responses,and improved intestinal barrier permeability.Additionally,AKK-D promoted the reconsti-tution and maintenance of the homeostasis of gut microflora,as indicated by the fact that AKK-D-treated mice showed a decrease in Bacteroidetes and an increase in the proportion of other beneficial bacteria like Muribaculaceae,Muribaculum,and unclas-sified f_Lachnospiaceae compared with the diarrhea model mice.Conclusion:Taken together,our data show that this novel AKK-D strain might be a potential probiotic for use in musk deer breeding,although further extensive system-atic research is still needed.展开更多
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...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.展开更多
Over the past decades,the expansion of natu-ral secondary forests has played a crucial role in offsetting the loss of primary forests and combating climate change.Despite this,there is a gap in our understanding of ho...Over the past decades,the expansion of natu-ral secondary forests has played a crucial role in offsetting the loss of primary forests and combating climate change.Despite this,there is a gap in our understanding of how tree species’growth and mortality patterns vary with eleva-tion in these secondary forests.In this study,we analyzed data from two censuses(spanning a five-year interval)conducted in both evergreen broadleaved forests(EBF)and temperate coniferous forests(TCF),which have been recovering for half a century,across elevation gradients in a subtropical mountain region,Mount Wuyi,China.The results indicated that the relative growth rate(RGR)of EBF(0.028±0.001 cm·cm^(-1)·a^(-1))and the mortality rate(MR)(20.03%±1.70%)were 27.3%and 16.4%higher,respec-tively,than those of TCF.Interestingly,the trade-off between RGR and MR in EBF weakened as elevation increased,a trend not observed in TCF.Conversely,TCF consistently showed a stronger trade-off between RGR and MR compared to EBF.Generalized linear mixed models revealed that ele-vation influences RGR both directly and indirectly through its interactions with slope,crown competition index(CCI),and tree canopy height(CH).However,tree mortality did not show a significant correlation with elevation.Additionally,DBH significantly influenced both tree growth and mortal-ity,whereas and CH and CCI had opposite effects on tree growth between EBF and TCF.Our study underscores the importance of elevation in shaping the population dynamics and the biomass carbon sink balance of mountain forests.These insights enhance our understanding of tree species’life strategies,enabling more accurate predictions of forest dynamics and their response to environmental changes.展开更多
文摘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.
基金funded by the Directorate of Research and Community Service,Directorate General of Research and Development,Ministry of Higher Education,Science and Technologyin accordance with the Implementation Contract for the Operational Assistance Program for State Universities,Research Program Number:109/C3/DT.05.00/PL/2025.
文摘Sudden wildfires cause significant global ecological damage.While satellite imagery has advanced early fire detection and mitigation,image-based systems face limitations including high false alarm rates,visual obstructions,and substantial computational demands,especially in complex forest terrains.To address these challenges,this study proposes a novel forest fire detection model utilizing audio classification and machine learning.We developed an audio-based pipeline using real-world environmental sound recordings.Sounds were converted into Mel-spectrograms and classified via a Convolutional Neural Network(CNN),enabling the capture of distinctive fire acoustic signatures(e.g.,crackling,roaring)that are minimally impacted by visual or weather conditions.Internet of Things(IoT)sound sensors were crucial for generating complex environmental parameters to optimize feature extraction.The CNN model achieved high performance in stratified 5-fold cross-validation(92.4%±1.6 accuracy,91.2%±1.8 F1-score)and on test data(94.93%accuracy,93.04%F1-score),with 98.44%precision and 88.32%recall,demonstrating reliability across environmental conditions.These results indicate that the audio-based approach not only improves detection reliability but also markedly reduces computational overhead compared to traditional image-based methods.The findings suggest that acoustic sensing integrated with machine learning offers a powerful,low-cost,and efficient solution for real-time forest fire monitoring in complex,dynamic environments.
基金supported by the National Natural Science Foundation of China(Grant Nos.30470302 and 70373044).
文摘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.
文摘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.
基金supported by the Major Program of the National Natural Science Foundation of China(No.32192434)the Fundamental Research Funds of Chinese Academy of Forestry(No.CAFYBB2019ZD001)the National Key Research and Development Program of China(2016YFD060020602).
文摘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.
文摘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.
基金the National Council for Scientific and Technological Development-CNPq for granting financial support to the project(484260/2013-8).
文摘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.
文摘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.
文摘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.
文摘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.
基金supported by the National Natural Science Foundation of China(42030508)the Second Tibetan Plateau Scientific Expedition and Research(STEP)program(2019QZKK0301)the Key technology research and development projects in Xizang Autonomous Regions(XZ202101ZY0005G).
文摘A better understanding of the structure and dynamics of disturbed forests is key for forecasting their future successional trajectories.Despite vulnerability of subalpine forests to warming climate,little is known as to how their community composition has responded to disturbances and climate warming over decades.Before the 1970s,subalpine forests on the southeastern Qinghai-Tibet Plateau mainly experienced logging and fire,but afterwards they were more impacted by climate warming.Thus,they provide an excellent setting to test whether disturbances and climate warming led to changes in forest structure.Based on the analysis of 3145 forest inventory plots at 4-to 5-year resolution,we found that spruce-fir forests shifted to pine and broadleaved forests since the early 1970s.Such a turnover in species composition mainly occurred in the 1994e1998 period.By strongly altering site conditions,disturbances in concert with climate warming reshuffle community composition to warm-adapted broadleaf-pine species.Thus,moderate disturbances shifted forest composition through a gradual loss of resilience of spruce-fir forests.Shifts in these foundation species will have profound impacts on ecosystem functions and services.In the future,broadleaved forests could expand more rapidly than evergreen needle-leaved forests under moderate warming scenarios.In addition to climate,the effects of anthropogenic disturbances on subalpine forests should be considered in adaptive forest management and in projections of future forest changes.
文摘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(d<sup>2</sup>h), whereas the best model to estimate biomass was ln(b)=-2.983+ 0.949*ln(ρd<sup>2</sup>h) . 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<sup>-1</sup> and 24.4 m<sup>2</sup>·ha<sup>-1</sup>. Based on the newly developed equations, the forest has on average 303.0 m<sup>3</sup>·ha<sup>-1</sup> standing volume of wood and 283.8 Mg·ha<sup>-1</sup> 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.
基金National Key Research and Development Program of China,No.2023YFE0208100,No.2021YFC3000201Natural Science Foundation of Henan Province,No.232300420165。
文摘The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.
文摘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 R<sup>2</sup> 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 (R<sup>2</sup> = 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.
基金supported by National Key Research and Development Program of China(No.2021YFD2200405(S.R.L.))Natural Science Foundation of China(Grant No.31971653).
文摘With the rapid economic development and continuous expansion of human activities,forest degradation—characterized by reduced forest stock within the forest including declining carbon storage—poses significant threats to ecosystem stability.Understanding the current status of forest degradation and assessing potential carbon stocks in China are of strategic importance for making forest restoration efforts and enhancing carbon sequestration capacity.In this study,we used the national forest inventory data from 2009 to 2018 to develop a set of standard measures for assessing degraded forests across China,based on five key indicators:forest accumulation growth rate(FAGR),forest recruitment rate(FRR),tree species reduction rate(TSRR),forest canopy cover reduction rate(FCCRR),and forest disaster level(FDL).Additionally,we estimated standing carbon stock,potential carbon stock,and theoretical space to grow by developing a stand growth model,which accounts for stand density across different site classes,to evaluate the restoration potential of degraded forests.The results indicate that degraded forest area in China is 36.15 million hectares,accounting for 20.10% of a total forest area.Standing carbon stock and potential carbon stock of degraded forests in China are 23.93 million tons and 61.90 million tons,respectively.Overall,degraded forest varies significantly across different regions.The results highlight the important trade-offs among environmental factors,policy decisions,and forest conditions,providing a robust foundation for developing measures to enhance forest quality.
文摘Brazil’s deforestation monitoring integrates accuracy and current monitoring for land use and land cover applications.Regular monitoring of deforestation and non-deforestation requires Sentinel-2 multispectral satellite images of several bands at various frequencies,the mix of high-and low-resolution images that make object classification difficult because of the mixed pixel problem.Accuracy is impacted by the mixed pixel problem,which occurs when pixels belong to different classes and makes detection challenging.To identify mixed pixels,Band Math is used to merge numerous bands to generate a new band NDVI.Thresholding is used to analyze the edges of deforested and non-deforested areas.Segmentation is then used to analyze the pixels which helps to identify the number of mixed pixels to compute the deforested and non-deforested areas.Segmented image pixels are used to categorize the deforestation of the Brazilian Amazon Forest between 2019 and 2023.Verify how many pixels are mixed to improve accuracy and identify mixed pixel issues;compare the mixed and pure pixels of fuzzy clustering with the subtracted morphological image pixels.With the help of segmentation and clustering researchers effectively validate mixed pixels in a specific area.The proposed methodology is easy to analyze and helpful for an appropriate calculation of deforested and non-deforested areas.
基金supported by the National Natural Science Foundation of China(No.31988102)Yunnan Province Major Program for Basic Research Project(No.202101BC070002)+1 种基金Yunnan Province Science and Technology Talents and Platform Program(No.202305AA160014)Yunnan Province Key Research and Development Program of China(No.202303AC100009)。
文摘Forest structure is fundamental in determining ecosystem function,yet the impact of bamboo invasion on these structural characteristics remains unclear.We investigated 219 invasion transects at 41 sites across the distribution areas of Moso bamboo(Phyllostachys edulis)in China to explore the effects of bamboo invasion on forest structural attributes and diameter–height allometries by comparing paired plots of bamboo,mixed bamboo-tree,and non-bamboo forests along the transects.We found that bamboo invasion decreased the mean and maximum diameter at breast height,maximum height,and total basal area,but increased the mean height,stem density,and scaling exponent for stands.Bamboo also had a higher scaling exponent than tree,particularly in mixed forests,suggesting a greater allocation of biomass to height growth.As invasion intensity increased,bamboo allometry became more plastic and decreased significantly,whereas tree allometry was indirectly promoted by increasing stem density.Additionally,a humid climate may favour the scaling exponents for both bamboo and tree,with only minor contributions from topsoil moisture and nitrogen content.The inherent superiority of diameter–height allometry allows bamboo to outcompete tree and contributes to its invasive success.Our findings provide a theoretical basis for understanding the causes and consequences of bamboo invasion.
基金This research was funded by Shaanxi Administration of Traditional Chinese Medicine Projects(2021-QYZL-01,2021-QYPT-001)Key R&D Project in Shaanxi Province(2023-YBSF-463)+1 种基金the Foundation of Science and Technology in Shaanxi Province(2020TD-050)the Fundamental Research Funds for the Central Universities(GK202205010).
文摘Background:The forest musk deer,a rare fauna species found in China,is famous for its musk secretion which is used in selected Traditional Chinese medicines.However,over-hunting has led to musk deer becoming an endangered species,and their survival is also greatly challenged by various high incidence and high mortality respiratory and intestinal diseases such as septic pneumonia and enteritis.Accumulating evidence has demonstrated that Akkermannia muciniphila(AKK)is a promising probiotic,and we wondered whether AKK could be used as a food additive in animal breeding pro-grammes to help prevent intestinal diseases.Methods:We isolated one AKK strain from musk deer feces(AKK-D)using an im-proved enrichment medium combined with real-time PCR.After confirmation by 16S rRNA gene sequencing,a series of in vitro tests was conducted to evaluate the probiotic effects of AKK-D by assessing its reproductive capability,simulated gas-trointestinal fluid tolerance,acid and bile salt resistance,self-aggregation ability,hy-drophobicity,antibiotic sensitivity,hemolysis,harmful metabolite production,biofilm formation ability,and bacterial adhesion to gastrointestinal mucosa.Results:The AKK-D strain has a probiotic function similar to that of the standard strain in humans(AKK-H).An in vivo study found that AKK-D significantly amelio-rated symptoms in the enterotoxigenic Escherichia coli(ETEC)-induced murine diar-rhea model.AKK-D improved organ damage,inhibited inflammatory responses,and improved intestinal barrier permeability.Additionally,AKK-D promoted the reconsti-tution and maintenance of the homeostasis of gut microflora,as indicated by the fact that AKK-D-treated mice showed a decrease in Bacteroidetes and an increase in the proportion of other beneficial bacteria like Muribaculaceae,Muribaculum,and unclas-sified f_Lachnospiaceae compared with the diarrhea model mice.Conclusion:Taken together,our data show that this novel AKK-D strain might be a potential probiotic for use in musk deer breeding,although further extensive system-atic research is still needed.
基金Under the auspices of National Natural Science Foundation of China(No.42201374,42071359)。
文摘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.
基金funded by the National Natural Science Foundation of China(Grant No.32271872).
文摘Over the past decades,the expansion of natu-ral secondary forests has played a crucial role in offsetting the loss of primary forests and combating climate change.Despite this,there is a gap in our understanding of how tree species’growth and mortality patterns vary with eleva-tion in these secondary forests.In this study,we analyzed data from two censuses(spanning a five-year interval)conducted in both evergreen broadleaved forests(EBF)and temperate coniferous forests(TCF),which have been recovering for half a century,across elevation gradients in a subtropical mountain region,Mount Wuyi,China.The results indicated that the relative growth rate(RGR)of EBF(0.028±0.001 cm·cm^(-1)·a^(-1))and the mortality rate(MR)(20.03%±1.70%)were 27.3%and 16.4%higher,respec-tively,than those of TCF.Interestingly,the trade-off between RGR and MR in EBF weakened as elevation increased,a trend not observed in TCF.Conversely,TCF consistently showed a stronger trade-off between RGR and MR compared to EBF.Generalized linear mixed models revealed that ele-vation influences RGR both directly and indirectly through its interactions with slope,crown competition index(CCI),and tree canopy height(CH).However,tree mortality did not show a significant correlation with elevation.Additionally,DBH significantly influenced both tree growth and mortal-ity,whereas and CH and CCI had opposite effects on tree growth between EBF and TCF.Our study underscores the importance of elevation in shaping the population dynamics and the biomass carbon sink balance of mountain forests.These insights enhance our understanding of tree species’life strategies,enabling more accurate predictions of forest dynamics and their response to environmental changes.
