Observations and Modeling of Incoming Longwave Radiation to Snow Beneath Forest Canopies in the West Tianshan Mountains, China

Forest canopy reduces shortwave radiation and increases the incoming longwave radiation to snowpacks beneath forest canopies. Furthermore, the effect of forest canopy may be changed by complex topography. In this paper, we measured and simulated the incoming longwave radiation to snow beneath forest at different canopy openness in the west Tianshan Mountains, China(43°16'N, 84°24'E) during spring 2013. A sensitivity study was conducted to explore the way that terrain influenced the incoming longwave radiation to snow beneath forest canopies. In the simulation model, measurement datasets, including air temperature, incoming shortwave radiation above canopy, and longwave radiation enhanced by adjacent terrain, were applied to calculate the incoming longwave radiation to snow beneath forest canopy. The simulation results were consistent with the measurements on hourly scale and daily scale. The effect of longwave radiation enhanced by terrain was important than that of shortwave radiation above forest canopy with different openness except the 20% canopy openness. The longwave radiation enhanced due to adjacent terrain increases with the slope increase and temperature rise. When air temperature(or slope) is relatively low, thelongwave radiation enhanced by adjacent terrain is not sensitive to slope(or air temperature), but the sensitivity increases with the decrease of snow cover area on sunny slope. The effect of longwave radiation is especially sensitive when the snow cover on sunny slope melts completely. The effect of incoming shortwave radiation reflected by adjacent terrain on incoming longwave radiation to snow beneath forest canopies is more slight than that of the enhanced longwave radiation.

Modeling of forest soil and litter health using disturbance and landscape heterogeneity indicators in northern Iran

This paper focuses on the indicators of soil and litter health, disturbance, and landscape heterogeneity as a tool for prediction of ecosystem sustainability in the northern forests of Iran. The study area was divided into spatial homogenous sites using slope, aspect, and soil humidity classes. Then a range of sites along the disturbance gradient was selected for sampling. Chemical and physical indicators of soil and litter health were measured at random points within these sites. Structural equation modeling(SEM) was applied to link six constructs of landscape heterogeneity, three constructs of disturbance(harvest, livestock, and human accessibility), and soil and litter health. The results showed that with decreasing accessibility, the total N and organic matter content of soil increased and effective bulk density decreased. Harvesting activities increased soil organic matter. Therefore, it is concluded that disturbances through harvesting and accessibility inversely affect the soil health. Unexpectedly, it was found that the litter total C and C:N ratio improved with an increase in the harvest and accessibility disturbances, whereas litter bulk density decreased. Investigation of tree composition revealed that in the climax communities, which are normally affected more by harvesting activities, some species like Fagus orientalis Lipsky with low decomposition rate are dominant. The research results showed that changes in disturbance intensity are reflected in litter and soil indicators, whereas the SEM indicated that landscape heterogeneity has a moderator effect on the disturbance to both litter and soil paths.

Identification of Mixtures of Two Types of Body Fluids Using the Multiplex Methylation System and Random Forest Models

Objective Body fluid mixtures are complex biological samples that frequently occur in crime scenes,and can provide important clues for criminal case analysis.DNA methylation assay has been applied in the identification of human body fluids,and has exhibited excellent performance in predicting single-source body fluids.The present study aims to develop a methylation SNaPshot multiplex system for body fluid identification,and accurately predict the mixture samples.In addition,the value of DNA methylation in the prediction of body fluid mixtures was further explored.Methods In the present study,420 samples of body fluid mixtures and 250 samples of single body fluids were tested using an optimized multiplex methylation system.Each kind of body fluid sample presented the specific methylation profiles of the 10 markers.Results Significant differences in methylation levels were observed between the mixtures and single body fluids.For all kinds of mixtures,the Spearman’s correlation analysis revealed a significantly strong correlation between the methylation levels and component proportions(1:20,1:10,1:5,1:1,5:1,10:1 and 20:1).Two random forest classification models were trained for the prediction of mixture types and the prediction of the mixture proportion of 2 components,based on the methylation levels of 10 markers.For the mixture prediction,Model-1 presented outstanding prediction accuracy,which reached up to 99.3%in 427 training samples,and had a remarkable accuracy of 100%in 243 independent test samples.For the mixture proportion prediction,Model-2 demonstrated an excellent accuracy of 98.8%in 252 training samples,and 98.2%in 168 independent test samples.The total prediction accuracy reached 99.3%for body fluid mixtures and 98.6%for the mixture proportions.Conclusion These results indicate the excellent capability and powerful value of the multiplex methylation system in the identification of forensic body fluid mixtures.

Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer:A significant leap forward

In a recent paper,Hong et al developed an artificial intelligence(AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients.They demonstrated that integrating AI with random forest models significantly improved the preoperative prediction and patient outcome management accuracy.By incorporating data from multiple centers,their model ensures standardization,reliability,and broad applicability,distinguishing it from the prior models.The present study highlights AI's potential in clinical decision support,aiding in the preoperative and postoperative management of gastric cancer patients.Our findings may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.

Estimating prognosis of gastric neuroendocrine neoplasms using machine learning:A step towards precision medicine

Survival rates following radical surgery for gastric neuroendocrine neoplasms(g-NENs)are low,with high recurrence rates.This fact impacts patient prognosis and complicates postoperative management.Traditional prognostic models,including the Cox proportional hazards(CoxPH)model,have shown limited predictive power for postoperative survival in gastrointestinal neuroectodermal tumor patients.Machine learning methods offer a unique opportunity to analyze complex relationships within datasets,providing tools and methodologies to assess large volumes of high-dimensional,multimodal data generated by biological sciences.These methods show promise in predicting outcomes across various medical disciplines.In the context of g-NENs,utilizing machine learning to predict survival outcomes holds potential for personalized postoperative management strategies.This editorial reviews a study exploring the advantages and effectiveness of the random survival forest(RSF)model,using the lymph node ratio(LNR),in predicting disease-specific survival(DSS)in postoperative g-NEN patients stratified into low-risk and high-risk groups.The findings demonstrate that the RSF model,incorporating LNR,outperformed the CoxPH model in predicting DSS and constitutes an important step towards precision medicine.

Carbon Dynamics in Woody Biomass of Forest Ecosystem in China with Forest Management Practices under Future Climate Change and Rising CO_2 Concentration

It is critical to study how different forest management practices affect forest carbon sequestration under global climate change regime. Previous researches focused on the stand-level forest carbon sequestration with rare investigation of forest carbon stocks influ- enced by forest management practices and climate change at regional scale. In this study, a general integrative approach was used to simulate spatial and temporal variations of woody biomass and harvested biomass of forest in China during the 21st century under dif- ferent scenarios of climate and CO2 concentration changes and management tasks by coupling Integrated Terrestrial Ecosystem Carbon budget （InTEC） model with Global Forest Model （G4M）. The results showed that forest management practices have more predominant effects on forest stem stocking biomass than climate and CO2 concentration change. Meanwhile, the concurrent future changes in cli- mate and CO2 concentration will enhance the amounts of stem stocking biomass in forests of China by 12%-23% during 2001-2100 relative to that with climate change only. The task for maximizing stem stocking biomass will dramatically enhance the stem stocking biomass from 2001~100, while the task for maximum average increment will result in an increment of stem stocking biomass before 2050 then decline. The difference of woody biomass responding to forest management tasks was owing to the current age structure of forests in China. Meanwhile, the sensitivity of long-term woody biomass to management practices for different forest types （coniferous forest, mixed forest and deciduous forest） under changing climate and CO2 concentration was also analyzed. In addition, longer rotation length under future climate change and rising CO2 concentration scenario will dramatically increase the woody biomass of China during 2001~100. Therefore, our estimation indicated that taking the role of forest management in the carbon cycle into the consideration at regional or national level is very important to project the forest carbon sequestration under future climate change and rising atmospheric CO2 concentration.

Succession Features and Dynamic Simulation of Subalpine Forest in the Gongga Mountain, China

The Gongga Mountain of eastern Tibet Plateau is a representative of the alpine regions with high peaks and deep valleys. Climate change over the last thousand years has controlled the dynamics of glacier and debris flow occurrence, which resulted in substantial changes in the mountainous environment. The authors surveyed the community structure of primary forests in Gongga Mountain and forest succession processes in woodland plots. The changing features in the subalpine environment are discussed in this paper. Tree species and sizes between the glacier shrinking areas and debris flow fans in Hailuogou Valley are compared. The pioneer species that settle in debris flow fans and the glacier shrinking areas are Salix spp. and Populus purdomii. Abies fabri and Picea brachytyla are the climax tree species. The succession process of primary vegetation in Hailuogou (2700 ~ 3200 m) can be divided into four stages: Slash surface (20~ 200 yr) Salix-Populus seeding community (10 ~ 30 yr) Populus-Salix sapling community (30 ~ 100 yr) Populus-Abies mixed community (50 ~ 100 yr) Abies-Picea climax In a natural and undisturbed environment, trees compete for light, water and nutrients. Disaster disturbance in mountains is a very important driving factor for regeneration of woody plants. Repeated destruction of forests by glacier movement or debris flows generated additional forest gaps that allow young plants to grow. In this study the Gongga Forest Succession Model (GFSM) was developed for simulation of forest community succession processes on different scales in Gongga Mountain. A soil succession module was added to the GFSM model to simulate soil formation and chemical element change of woodland. In order to represent major features of forests in Southwestern China, many field works has been done to identify ecological parameters of various trees in the subalpine region. On the basis of simulation of tree life history, the GFSM combines forest succession with soil change in both material components and nutrition content. The Monte-Carlo method was applied to simulate random weather fluctuation and the uncertainty of tree death. These modeled processes agreed with the field investigation results in this region. The elevation distribution of different tree species was also simulated; and the results are consistent with field observations of ecological features of tree species. The modeling approach reflects well the succession dynamics of primary forests in Southwestern China. These results are very useful for improving the management policies and prediction technology for restoration and conservation of primary forests in Southwestern China.

Rebirth after death: forest succession dynamics in response to climate change on Gongga Mountain, Southwest China

Global climate change is having long-term impacts on the geographic distribution of forest species. However, the response of vertical belts of mountain forests to climate change is still little known. The vertical distribution of forest vegetation(vertical vegetation belt) on Gongga Mountain in Southwest China has been monitored for 30 years. The forest alternation of the vertical vegetation belt under different climate conditions was simulated by using a mathematical model GFSM(the Gongga Forest Succession Model). Three possible Intergovernmental Panel on Climate Change(IPCC) climate scenarios(increase of air temperature and precipitation by 1.8℃/5%, 2.8℃/10% and 3.4℃/15% for B_1, A_1B and A_2 scenarios, respectively) were chosen to reflect lower, medium and higher changes of global climate. The vertical belts of mountainous vegetation will shift upward by approximately 300 m, 500 m and 600 m in the B_1, A_1B and A_2 scenarios, respectively, according to the simulated results. Thus, the alpine tree-line will move to a higher altitude. The simulation also demonstrated that, in a changing climate, the shift in the vegetation community will be a slow and extended process characterized by two main phases. During the initial phase, trees of the forest community degrade or die, owing to an inability to adapt to a warmer climate. This results in modest environment for the introduction of opportunistic species, consequently, the vegetation with new dominant tree species becomes predominant in the space vacated by the dead trees at the expense of previously dominated original trees as the succession succeed and climate change advance. Hence, the global climate change would dramatically change forest communities and tree species in mountainous regions because that the new forest community can grow only through the death of the original tree. Results indicated that climate change will cause the change of distribution and composition of forest communities on Gongga Mountain, and this change may enhance as the intensity of climate change increases. As a result, the alternation of death and rebirth would finally result in intensive landscape changes, and may strongly affect the eco-environment of mountainous regions.

The Altitudinal Belts of Subalpine Virgin Forest on Mt.Gongga Simulated by a Succession Model

How to accurately simulate the distribution of forest species based upon their biological attributes has been a traditional biogeographical issue.Forest gap models are very useful tools for examining the dynamics of forest succession and revealing the species structure of vegetation.In the present study,the GFSM(Gongga Forest Succession Model) was developed and applied to simulate the distribution,composition and succession process of forests in 100 m elevation intervals.The results indicate that the simulated results of the tree species,quantities of the different types of trees,tree age and differences in DBH(diameter at breast height) composition were in line with the actual situation from 1400 to 3700 MASL(meters above sea level) on the eastern slope of Mt.Gongga.Moreover,the dominant species in the simulated results were the same as those in the surveyed database.Thus,the GFSM model can best simulate the features of forest dynamics and structure in the natural conditions of Mt.Gongga.The work provides a new approach to studying the structure and distribution characteristics of mountain ecosystems in varied elevations.Moreover,the results of this study suggest that the biogeochemistry mechanism model should be combined with the forestsuccession model to facilitate the ecological model in simulating the physical and chemical processes involved.

Unsuspected implications arising from assumptions in simulations: insights from recasting a forest growth model in system dynamics

Background:Familiarity with a simulation platform can seduce modellers into accepting untested assumptions for convenience of implementation.These assumptions may have consequences greater than commonly suspected,and it is important that modellers remain mindful of assumptions and remain diligent with sensitivity testing.Methods:Familiarity with a technique can lead to complacency,and alternative approaches and software can reveal untested assumptions.Visual modelling environments based on system dynamics may help to make critical assumptions more evident by offering an accessible visual overview and empowering a focus on representational rather than computational efficiency.This capacity is illustrated using a cohort-based forest growth model developed for mixed species forest.Results:The alternative model implementation revealed that untested assumptions in the original model could have substantial influence on simulated outcomes.Conclusions:An important implication is that modellers should remain conscious of all assumptions,consider alternative implementations that reveal assumptions more clearly,and conduct sensitivity tests to inform decisions.

Modelling potential distribution of a pine bark beetle in Mexican temperate forests using forecast data and spatial analysis tools

Accurate and reliable predictions of pest species distributions in forest ecosystems are urgently needed by forest managers to develop management plans and monitor new areas of potential establishment.Presence-only species distribution models are commonly used in these evaluations.The maximum entropy algorithm(MaxEnt)has gained popularity for modelling species distribution.Here,MaxEnt was used to model the spatial distribution of the Mexican pine bark beetle(Dendroctonus mexicanus)in a daily fashion by using forecast data from the Weather Research and Forecasting model.This study aimed to exploit freely available geographic and environmental data and software and thus provide a pathway to overcome the lack of costly data and technical guidance that are a challenge to implementing national monitoring and management strategies in developing countries.Our results showed overall agreement values between 60 and 87%.The results of this research can be used for D.mexicanus monitoring and management and may aid as a model to monitor similar species.

Desertification status mapping in MuttumaWatershed by using Random Forest Model

Potential of the Random Forest Model on mapping of different desertification processes was studied in Muttuma watershed of mid-Murrumbidgee river region of New South Wales,Australia.Desertification vulnerability index was developed using climate,terrain,vegetation,soil and land quality indices to identify environmentally sensitive areas for desertification.Random Forest Model(RFM)was used to predict the different desertification processes such as soil erosion,salinization and waterlogging in the watershed and the information needed to train classification algorithms was obtained from satellite imagery interpretation and ground truth data.Climatic factors(evaporation,rainfall,temperature),terrain factors(aspect,slope,slope length,steepness,and wetness index),soil properties(pH,organic carbon,clay and sand content)and vulnerability indices were used as an explanatory variable.Classification accuracy and kappa index were calculated for training and testing datasets.We recorded an overall accuracy rate of 87.7%and 72.1%for training and testing sites,respectively.We found larger discrepancies between overall accuracy rate and kappa index for testing datasets(72.2%and 27.5%,respectively)suggesting that all the classes are not predicted well.The prediction of soil erosion and no desertification process was good and poor for salinization and water-logging process.Overall,the results observed give a new idea of using the knowledge of desertification process in training areas that can be used to predict the desertification processes at unvisited areas.

New Development in Study of the Faustmann Optimal Forest Harvesting

Chinese forest resources have become very scarce in the face of rapid economic growth demand, while the reform of collective forest right system is in full swing across the country. It will directly affect the regeneration level of forest resources and the diverse ecological value functions. In this article, the mainstream model paradigm of forest economics, that is, the basic framework of the Faustmann model and its evolution process are made in a more detailed explanation, especially the extended model including considered silvicultural effort, tax subsidies, risk dynamic management, and forest regeneration factors are made in more detailed explanations. This article concludes with the future further research directions of forest economics, including the design of dynamic models that includes considered uneven-aged forest management, non-timber goods and services, dynamic forest models. The research reflects the general trend of interdisciplinary and cross-border.

Estimating the carbon fluxes of forests with an individual-based forest model

Background： Capturing the response of forest ecosystems to inter-annual climate variability is a great challenge.In this study, we tested the capability of an individual-based forest gap model to display carbon fluxes at yearly and daily time scales.The forest model was applied to a spruce forest to simulate the gross primary production（GPP）, respiration and net ecosystem exchange（NEE）.We analyzed how the variability in climate affected simulated carbon fluxes at the scale of the forest model.Results： Six years were simulated at a daily time scale and compared to the observed eddy covariance（EC） data.In general, the seasonal cycle of the individual carbon fluxes was correctly described by the forest model.However, the estimated GPP differed from the observed data on the days of extreme climatic conditions.Two new parameterizations were developed： one resulting from a numerical calibration, and the other resulting from a filtering method.We suggest new parameter values and even a new function for the temperature limitation of photosynthesis.Conclusions： The forest model reproduced the observed carbon fluxes of a forest ecosystem quite wel.Of the three parameterizations, the calibrated model version performed best.However, the filtering approach showed that calibrated parameter values do not necessarily correctly display the individual functional relations.The concept of simulating forest dynamics at the individual base is a valuable tool for simulating the NEE, GPP and respiration of forest ecosystems.

The carbon fluxes in different successional stages:modelling the dynamics of tropical montane forests in South Ecuador

Background： Tropical forests play an important role in the global carbon（C） cycle.However, tropical montane forests have been studied less than tropical lowland forests, and their role in carbon storage is not well understood.Montane forests are highly endangered due to logging, land-use and climate change.Our objective was to analyse how the carbon balance changes during forest succession.Methods： In this study, we used a method to estimate local carbon balances that combined forest inventory data with process-based forest models.We utilised such a forest model to study the carbon balance of a tropical montane forest in South Ecuador, comparing two topographical slope positions（ravines and lower slopes vs upper slopes and ridges）.Results： The simulation results showed that the forest acts as a carbon sink with a maximum net ecosystem exchange（NEE） of 9.3 Mg C？（ha？yr）-1during its early successional stage（0–100 years）.In the late successional stage, the simulated NEE fluctuated around zero and had a variation of 0.77 Mg C？（ha？yr）–1.The simulated variability of the NEE was within the range of the field data.We discovered several forest attributes（e.g., basal area or the relative amount of pioneer trees） that can serve as predictors for NEE for young forest stands（0–100 years） but not for those in the late successional stage（500–1,000 years）.In case of young forest stands these correlations are high, especially between stand basal area and NEE.Conclusion： In this study, we used an Ecuadorian study site as an example of how to successfully link a forest model with forest inventory data, for estimating stem-diameter distributions, biomass and aboveground net primary productivity.To conclude, this study shows that process-based forest models can be used to investigate the carbon balance of tropical montane forests.With this model it is possible to find hidden relationships between forest attributes and forest carbon fluxes.These relationships promote a better understanding of the role of tropical montane forests in the context of global carbon cycle, which in future will become more relevant to a society under global change.

Optimal nitrogen fertilization of boreal conifer forest

Background： Forest fertilization offers a means to increase the production of renewable resources.Nitrogen is the most common fertilizer in boreal upland forests.There is plenty of research on the effect of nitrogen fertilization on volume growth, but less research on the optimal timing of fertilization and optimal management of fertilized stands.Methods： This study used simulation and optimization to analyze the profitability of fertilization, optimal management of fertilized stands and the effects of fertilization on cash flows and timber yields.The management of 100 stands representing the most common growing sites of Scots pine and Norway spruce was optimized.Results： Fertilization improved profitability in most of the analyzed stands.Profitability improved most in spruce stands growing on mesic site.Improving stem quality increased the economic benefit of fertilization.The timber yields of medium-aged conifer stands can be increased by almost 1 m^3·ha^（-1）·a^（-1）（15%） in sub-xeric pine and mesic spruce sites and about 0.5 m^3·ha^（-1）·a^（-1）（5%） in mesic pine and herb-rich spruce sites when the recommended nitrogen dose（150kg·ha^（-1）） is applied once in 30 years.Conclusions： Nitrogen fertilization of boreal conifer forest should be used mainly in spruce-dominated stands growing on medium sites.The gains are the highest in stands where the mean tree diameter is 16–20 cm and stand basal area is 14–20 m^2·ha^（-1）.

Stable Solution of Nonlinear Age-structured Forest Evolution System

This paper studies the dynamical behavior of a class of total area dependent nonlinear age-structured forest evolution model. We give the problem of equal value for the forest system, and discuss the stable solution of system. We obtained the necessary and sufficient conditions for there exists the stable solution.

Impact of regional human capital and urban sprawl on forest——based on a forest conservation model

Metropolitan cities in China have become a major economic hubs with an unprecedented increase of land use and decline of environmental resources. Based on a simple and abstract forest conservation model, this paper attempts to explain changes of forest resources caused by urban sprawl. Through the research, it is found that high level of regional human capital is beneficial to curb urban sprawl. In this vein the model presents the urban forest conservation cost strategy at the Nash equilibrium of varied discount factor and parameter control.

The Mathematical Model of Short-Term Forest Fire Spread

In this paper, we establish a mathematical model of the forest fire spread process based on a partial differential equation. We describe the distribution of time field and velocity field in the whole two-dimensional space by vector field theory. And we obtain a continuous algorithm to predict the dynamic behavior of forest fire spread in a short time. We use the algorithm to interpolate the fire boundary by cubic non-uniform rational B-spline closed curve. The fire boundary curve at any time can be simulated by solving the Eikonal equation. The model is tested in theory and in practice. The results show that the model has good accuracy and stability, and it’s compatible with most of the existing models, such as the elliptic model and the cellular automata model.

Empirical Analysis of Forest Pest Control Efficiency from 2003 to 2014 in China

Three indexes including forest pest occurrence area,control area and input fund of 31 provinces from 2003 to 2014 were selected from Forestry Statistical Yearbook,to establish dynamic interaction index evaluation system with clustering robust regression model and Stata 13. 0 software. Total forest pest control efficiency in China was determined according to the computing result of entropy method. Suggestions such as improving forest pest control efficiency,increasing service efficiency and input amount of forest pest control input funds were put forward. It will provide empirical basis for target management evaluation of forest pest control work and accountability system.