Modelling tree mortality across diameter classes using mixedeffects zero-inflated models

The mortality of trees across diameter class model is a useful tool for predicting changes in stand structure.Mortality data commonly contain a large fraction of zeros and general discrete models thus show more errors.Based on the traditional Poisson model and the negative binomial model,different forms of zero-inflated and hurdle models were applied to spruce-fir mixed forests data to simulate the number of dead trees.By comparing the residuals and Vuong test statistics,the zero-inflated negative binomial model performed best.A random effect was added to improve the model accuracy;however,the mixed-effects zero-inflated model did not show increased advantages.According to the model principle,the zeroinflated negative binomial model was the most suitable,indicating that the"0"events in this study,mainly from the sample"0",i.e.,the zero mortality data,are largely due to the limitations of the experimental design and sample selection.These results also show that the number of dead trees in the diameter class is positively correlated with the number of trees in that class and the mean stand diameter,and inversely related to class size,and slope and aspect of the site.     

Application of Poisson Regression Model and Negative Binomial Regression Model in Forest Fire Forecasting

A Poisson regression model and a negative binomial regression model(NB model) are often used in areas such as medicine and economy,but rarely in the domestic forestry sector,especially in the forest fire forecasting.Based on the data of forest fire occurrences in Daxing’anling region in 1980- 2005,this paper profoundly analyzes the application conditions and test methods of the two models.The AIC method was used to check the fitting level of the models and the capability of the models for forecasting forest fires was discussed.This study provided necessary theoretical basis and data support for the application of the two models in the field of forestry in China.     

Modeling frequency distributions of tree height,diameter and crown area by six probability functions for open forests of <Emphasis Type="Italic">Quercus persica</Emphasis> in Iran

The prediction of the distribution of quantitative variables in a forest stand is of great interest to forest managers, for the evaluation of forest resources and scheduling of future silvicultural treatments. The aim of this research was to model the distribution of quantitative variables for Quercus persica in open forests in Iran. To investigate the probability distribution of trees in natural stands, 642 trees were selected for measurement using a systematic random sampling method. Selected trees were measured and data were analyzed. Gamma, beta, normal,lognormal, exponential and Weibull probability distributions were fitted to the height distribution of trees. Variables of distribution functions were estimated using the maximum likelihood estimation method. Actual probability and probability which derived from functions was compared using Kolmogorov–Smirnov and Anderson–Darling tests. Beta, Weibull and Weibull probability distributions explained the distributions of tree height, DBH and crown area.     

Modeling frequency distributions of tree height,diameter and crown area by six probability functions for open forests of Quercus persica in Iran

The prediction of the distribution of quantitative variables in a forest stand is of great interest to forest managers, for the evaluation of forest resources and scheduling of future silvicultural treatments. The aim of this research was to model the distribution of quantitative variables for Quercus persica in open forests in Iran. To investigate the probability distribution of trees in natural stands, 642 trees were selected for measurement using a systematic random sampling method. Selected trees were measured and data were analyzed. Gamma, beta, normal,lognormal, exponential and Weibull probability distributions were fitted to the height distribution of trees. Variables of distribution functions were estimated using the maximum likelihood estimation method. Actual probability and probability which derived from functions was compared using Kolmogorov–Smirnov and Anderson–Darling tests. Beta, Weibull and Weibull probability distributions explained the distributions of tree height, DBH and crown area.     

Influence of climatic conditions,topography and soil attributes on the spatial distribution of site productivity index of the species rich forests of Jalisco,Mexico

This paper presents an approach based on field data to model the spatial distribution of the site productivity index （SPI） of the diverse forest types in Jalisco, Mexico and the response in SPI to site and cli-matic conditions. A linear regression model was constructed to test the hypothesis that site and climate variables can be used to predict the SPI of the major forest types in Jalisco. SPI varied significantly with topog-raphy （elevation, aspect and slope）, soil attributes （pH, sand and silt）, climate （temperature and precipitation zones） and forest type. The most important variable in the model was forest type, which accounted for 35% of the variability in SPI. Temperature and precipitation accounted for 8 to 9% of the variability in SPI while the soil attributes accounted for less than 4% of the variability observed in SPI. No significant differences were detected between the observed and predicted SPI for the individual forest types. The linear regression model was used to develop maps of the spatial variability in predicted SPI for the individual forest types in the state. The spatial site productivity models developed in this study provides a basis for understanding the complex relationship that exists between forest productivity and site and climatic conditions in the state. Findings of this study will assist resource managers in making cost-effective decisions about the management of individual forest types in the state of Jalisco, Mexico.     

Modelling of the spread of a potential invasive pest,the Siberian moth （Dendrolimus sibiricus） in Europe

Background： The Siberian moth （Dendrolimus sibiricus） （SM） defoliates several tree species from the genera Larix, Piceo and Abies in northern Asia, east of the Urals. The SM is a potential invasive forest pest in Europe because Europe has several suitable host species and climatic conditions of central and northern Europe are favourable for the SM.
Methods： This study developed a grid-based spatio-temporal model for simulating the spread of the SM in case it enters Europe from Russia via border stations. The spread rate was modeled as a function of the spatial distribution of host species, climatic suitability of different locations for the SM, human population density, transportation of moth-carrying material, and flying of moths from tree to tree.
Results and conclusions： The simulations showed that the SM is most likely to spread in the forests of northeast Belarus, the Baltic countries, and southern and central Finland. Climatic conditions affected the occurrence of the SM more than human population density and the coverage of suitable host species.     

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.     

Determining the edge effect of pine processionary moth (Thaumetopoea pityocampa) in its horizontal distribution in the stand

Pine processionary moth is one of the most important forest pests in the Mediterranean Basin. There is need to explore its distribution behaviour to undertake effective control and determine the edge effect of its horizontal distribution in the stand. Five trial sites damaged by the insect were selected and traps installed in all trees in the sites. The number of nests and larvae was counted and diameter increment cores were taken. The distance to the edge of the stand revealed that there was no statistical difference in the number of nests and larvae up to 25 m while there was a statistically significant difference(0.5%)at [25 m. There was a 2-fold difference in the density of nests between 0 and 25 m and [25 meters, and a 3.8-fold difference in the number of larvae. The effect of pine processionary moth on tree diameter was not significant between trees at the edge of the stand and those in the stand.     

A New Function for Modelling Diameter Frequency Distribution in the Tropical Rain Forest of Xishuangbanna, Southwest of China

Permanent plots in the montane tropical rain forests in Xishuangbanna, southwest China, were established, and different empirical models, based on observation data of these plots in 1992, were built to model diameter frequency distributions.The focus of this study is on predicting accuracy of stem number in the larger diameter classes, which is much more important than that of the smallcr trees, from the view of forest management, and must be adequately considered in the modelling and estimate.There exist 3 traditional ways of modelling the diameter frequency distribution: the negative exponential function model, limiting line function model, and Weibull distribution model. In this study, a new model, named as the logarithmic J-shape function, together with the others, was experimented and was found as a more suitable model for modelling works in the tropical forests.     

Spatial variability of soil microbial indices in common alder COMMON ALDER(Alnus glutinosa) stands using a geostatistical approach in northern Iran

Microbial indices and their spatial patterns are strongly affected by environmental factors. Spatial variability of soil properties is one of the most important causes of variability in soil microbial indices. This research was conducted in the Caspian forest to assess spatial variabilities and frequency distributions of microbial properties.Ninety soil samples were taken using a grid sampling design 40 9 40 m. Microbial indices, organic carbon,nitrogen and pH were determined. Soil variable distributions showed that microbial indices had abnormal distributions. Logarithmic transformation produced normal distribution. Spatial continuity using geostatistical(variogram) was studied and maps obtained by point kriging.The variograms revealed the presence of spatial autocorrelation. The results indicate that spatial dependence of soil microbial indices was affected by non-intrinsic factors and forest management procedures. The maps show that soil microbial indices and soil properties have spatial variability. The spatial pattern of microbial indices was correlated to organic carbon and nitrogen.     

Anthropogenic disturbances and status of forest and wildlife in the dry deciduous forests of Chhattisgarh state in India

The advent of modern forces and the changes in socioeconomic patterns of forest dwellers have increased the pressures on the forests. In order to mitigate such pressures and also to protect the forests and wildlife the model of protected areas networks has shifted and enhanced such pressures in the unprotected natural forests due to several reasons. Being a low profile category of protected status and continuous human settlements, the present study highlights the case of dry deciduous forests of Sarguja district of Chhattisgarh state of India. The major objectives of this study were to quantify the status of forests and wildlife and also to determine the extent of anthropogenic disturbances faced by the dry deciduous forests of central India. Transect and silent drive count methods were used for sampling wildlife and quadrat method was used for sampling vegetation. Besides, the local uses of various forest produces were also studied in view of understanding the people dependency on forests. The forest vegetation, in the study area, was pre-dominated by Shorea robusta, which had Madhuca indica, Diospyrus melanoxylon and Buchnania lanzan as the major companion species. The forest had either the high girth class mature tree species or the saplings. The low vegetation cover and density were due to the high anthropogenic pressures mainly in the form of heavy livestock grazing and collection of ethnobotanically important species. The study though reveals that the area is not rich in wildlife and the forest is fragmented, the area still supports some important species, which include many rare and endangered plants and animals. The findings of this study have been discussed in view of the management and conservation of the forest and wildlife in the dry deciduous forests.     

Developing conservation strategies for Pinus koraiensis and Eleutherococcus senticosus by using model-based geographic distributions

We described potential changes in the geographic distribution and occurrence probability of Pinus koraiensis Sieb. et Zucc. and Eleutherococcus senticosus(Rupr. et Maxim.) Maxim. in the counties of northeast China. This information was used to identify priority areas for protection and provide protection and management recommendations within each studied county. The two species were mapped in 2884 study plots throughout this region over a 4-year period(38 400N–53 300 N, 115 050E–135 020E). We used the species distribution models(Maxent), systematic conservation planning models(Marxan), and Geographic Information Systems(Arc GIS10.0). The distributions of two species were correlated in the study area, enabling unique and economically viable joint conservation measures to be implemented. Three models were combined to identify feasible priority conservation sites. We used local spatial statistics to assess all identified conservation areas in relation to potential climate change based shifts in the geographic distribution of the two species. Model-based conservation strategies were used to identify effective measures to protect and utilize these two tree species in the study region. This study presents a novel technique for assessing wild plant distributions, in addition to serving as a model for the conservation of other species within the framework of general forest management, ecological construction, and geographical surveying.     

Spatial modeling of the carbon stock of forest trees in Heilongjiang Province,China

Heilongjiang province is the largest forest zone in China and the forest coverage rate is 46%. Forests of Heilongjiang province play an important role in the forest ecosystem of China. In this study we investi- gated the spatial distribution of forest carbon storage in Heilongjiang province using 3083 plots sampled in 2010. We attempted to fit two global models, ordinary least squares model （OLS）, linear mixed model （LMM）, and a local model, geographically weighted regression model （GWR）, to the relationship between forest carbon content and stand, environment, and climate factors. Five predictors significantly affected forest carbon storage and spatial distribution, viz. average diameter of stand （DBH）, number of trees per hectare （TPH）, elevation （Elev）, slope （Slope） and the product of precipitation and temperature （Rain Temp）. The GWR model outperformed the two global models in both model fitting and prediction because it successfully reduced both spatial auto- correlation and heterogeneity in model residuals. More importantly, the GWR model provided localized model coefficients for each location in the study area, which allowed us to evaluate the influences of local stand conditions and topographic features on tree and stand growth, and forest carbon stock. It also helped us to better understand the impacts of silvi- cultural and management activities on the amount and changes of forest carbon storage across the province. The detailed information can be readily incorporated with the mapping ability of GIS software to provide excellent tools for assessing the distribution and dynamics of the for- est-carbon stock in the next few years.     

Estimating oak forest parameters in the western mountains of Iran using satellite-based vegetation indices

Remote sensing is an important tool for studying and modeling forest stands.Vegetation indices obtained from Landsat-8 remotely sensed data were used to estimate the forest parameters in the western mountains of Iran.Thirtyfour sample points were selected on the map of Bayangan County,Kermanshah province,Iran.At each point,a cluster of five rectangular plots of 8100 m2 and 200 m apart was established.Some clusters were primarily sampled to determine the variance of the forest parameters.The coefficient of variation was used as a criterion for sample allocation.Stand density,canopy cover and basal area per hectare were calculated for each plot.Vegetation indices were extracted fromthe Landsat-8 images for each plot.Simple linear and nonlinear regressions were conducted to develop the models.The models were validated using an independent data set.Stability of model parameters was statistically evaluated.The results showed that Normalized difference vegetation index(NDVI)and Transformed normalized difference vegetation index(TNDVI)followed by Simple ratio vegetation index(SRVI)were the best predictors,explaining up to 91%of the variations with high precision.For NDVI,Soil adjusted vegetation index 2(SAVI2)and SRVI,the cubic model was more appropriate than the linear model for predicting the forest parameters.For this model,the R-square value increased while NRMSE decreased significantly.For Infrared percentage vegetation index(IPVI),the quadratic model was better,but,for other vegetation indices,nonlinear models were not superior to linear ones.Finally,it can be concluded that Landsat-8 imagery is suitable for predicting forest parameters in the oak forests of western Iran.Of course,large plots must be selected,and pre-classification is necessary to gain accurate and precise estimations.     

Using a stand-level model to predict light absorption in stands with vertically and horizontally heterogeneous canopies

Background： Forest ecosystem functioning is strongly influenced by the absorption of photosynthetically active radiation （APAR）, and therefore, accurate predictions of APAR are critical for many process-based forest growth models. The Lambert-Beer law can be applied to estimate APAR for simple homogeneous canopies composed of one layer, one species, and no canopy gaps. However, the vertical and horizontal structure of forest canopies is rarely homogeneous. Detailed tree-level models can account for this heterogeneity but these often have high input and computational demands and work on finer temporal and spatial resolutions than required by stand-level growth models. The aim of this study was to test a stand-level light absorption model that can estimate APAR by individual species in mixed-species and multi-layered stands with any degree of canopy openness including open-grown trees to closed canopies. Methods： The stand-level model was compared with a detailed tree-level model that has already been tested in mixed-species stands using empirical data. Both models were parameterised for five different forests, including a wide range of species compositions, species proportions, stand densities, crown architectures and canopy structures. Results： The stand-level model performed well in all stands except in the stand where extinction coefficients were unusually variable and it appears unlikely that APAR could be predicted in such stands using （tree- or stand-level） models that do not allow individuals of a given species to have different extinction coefficients, leaf-area density or analogous parameters. Conclusion： This model is parameterised with species-specific information about extinction coefficients and mean crown length, diameter, height and leaf area. It could be used to examine light dynamics in complex canopies and in stand-level growth models.     

Land use change modeling through an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis(case study: Arasbaran region,Iran)

Temporal land use/land cover (LULC) change information provides a variety of applications for informed management of land resources. The aim of this study was to detect and predict LULC changes in the Arasbaran region using an integrated Multi-Layer Perceptron Neural Network and Markov Chain analysis. At the first step, multi-temporal Landsat images (1990, 2002 and 2014) were processed using ancillary data and were classified into seven LULC categories of high density forest, low-density forest, agriculture, grassland, barren land, water and urban area. Next, LULC changes were detected for three time profiles, 1990–2002, 2002–2014 and 1990–2014. A 2014 LULC map of the study area was further simulated (for model performance evaluation) applying 1990 and 2002 map layers. In addition, a collection of spatial variables was also used for modeling LULC change processes as driving forces. The actual and simulated 2014 LULC change maps were cross-tabulated and compared to ensure model simulation success and the results indicated an overall accuracy and kappa coefficient of 97.79% and 0.992, respectively. Having the model properly validated, LULC change was predicted up to the year 2025. The results demonstrated that 992 and 1592 ha of high and lowdensity forests were degraded during 1990–2014,respectively, while 422 ha were added to the extent of residential areas with a growth rate of 17.58 ha per year. The developed model predicted a considerable degradation trend for the forest categories through 2025, accounting for 489 and 531 ha of loss for high and low-density forests, respectively. By way of contrast, residential area and farmland categories will increase up to 211 and 427 ha, respectively. The integrated prediction model and customary area data can be used for practical management efforts by simulating vegetation dynamics and future LULC change trajectories.     

Comparison of satellite images with different spatial resolutions to estimate stand structural diversity in urban forests

The structural diversity in urban forests is highly important to protect biodiversity. In particular, fruit trees and bush species, cavity-bearing trees and coarse, woody debris provide habitats for animals to feed, nest and hide.Improper silvicultural practices, intensive recreational use and illegal harvesting lead to a decline in the structural diversity in forests within larger metropolitan cities. It is important to monitor the structural diversity at definite time intervals using effective technologies with a view to instituting the necessary conservation measures. The use of satellite images seems to be appropriate to this end. Here we aimed to identify the associations between the textural features derived from the satellite images with different spatial resolutions and the structural diversity indices in urban forest stands(Shannon–Wiener index, complexity index, dominance index and density of wildlife trees).Rapid Eye images with a spatial resolution of 5 m 9 5 m,ASTER images with a spatial resolution of 15 m 9 15 m and Landsat-8 ETM satellite images with a spatial resolution of 30 m 9 30 m were used in this study. The firstorder(standard deviation of gray levels) and second order(GLCM entropy, GLCM contrast and GLCM correlation)textural features were calculated from the satellite images.When associations between textural features in the images and the structural diversity indices were assessed using the Pearson correlation coefficient, very high associations were found between the image textural features and the diversity indices. The highest association was found between the standard deviation of gray levels(SDGLRAP) derived from RVIRAPof Rapid Eye image and the Shannon–Wiener index(Hh) calculated on the basis of tree height(R2= 0.64). The findings revealed that Rapid Eye satellite images with a spatial resolution of 5 m 9 5 m are most suitable for estimating the structural diversity in urban forests.     

‘Relationships between relationships’ in forest stands: intercepts and exponents analyses

Relationships between diameter at breast height(dbh) versus stand density, and tree height versus dbh(height curve) were explored with the aim to find if there were functional links between correspondent parameters of the relationships, exponents and intercepts of their power functions. A geometric model of a forest stand using a conic approximation suggested that there should be interrelations between correspondent exponents and intercepts of the relationships. It is equivalent to a type of ‘relationship between relationships’ that might exist in a forest stand undergoing self-thinning, and means that parameters of one relationship may be predicted from parameters of another. The predictions of the model were tested with data on forest stand structure from published databases that involved a number of trees species and site quality levels. It was found that the correspondent exponents and intercepts may be directly recalculated from one another for the simplest case when the total stem surface area was independent of stand density. For cases where total stem surface area changes with the drop of density, it is possible to develop a generalization of the model in which the interrelationships between correspondent parameters(exponents and intercepts) may be still established.     

Improving cross drain systems to minimize sediment delivery using GIS

A well developed network of roads must exist as a necessary infrastructure system in modern forestry to facilitate forest operations. But forest roads have the potential to disrupt the drainage characteristics of watersheds and lead to negative impacts on the environment with increased erosion and sediment yields. Numerous factors affect surface erosion of roads and sediment production potential; determining and ranking them could be a guide for management decisions to erosion control. In this study, the CULSED model (as an extension of ArcGIS) was used to estimate sediment delivery and the distribution of a road network, given the existing culverts. Using the model, some culverts were added to the road network around places with high sediment delivery in order to minimize it. After a correlation analysis and adjustment between sediment production and the factors, i.e., road width, road gradient, age of road and vegetation cover, the trend of changes in sediment delivery with model changes in the input was investigated with a sensitivity analysis of the model. The results show that adding new culverts to the road resulted in a significant reduction of sediment delivery. The most important factor affecting sediment delivery was road width, followed by road gradient, vegetation cover and age of road. Road width and gradient were positively correlated with sediment delivery, while vegetation cover and age of road were negatively correlated. The best model to show the relation between sediment delivery and road width as well as with road gradient was a linear model, for vegetation cover a cubic equation and for road age a power model. The results of sensitivity analysis showed that sediment delivery had the greatest sensitivity to changes of road width and was least sensitive to changes in the age of the road. This model can help to estimate sediment delivery with its spatial distribution, which can be used for optimization of cross drain systems and strategies of sediment control. Application of the model requires field trials to acquire the necessary input data. The reliability of our results is a function of the accuracy of inputs, especially digital elevation model.     

Effects of gaps on regeneration of woody plants: a meta-analysis

Forest gaps, openings in the canopy caused by death of one or more trees, have a profound effect on forest regeneration and drive the forest growth cycle. It is therefore necessary to understand the effects of forest gaps on regeneration for modern forest management. In order to provide a quantitative assessment of the effects of forest gaps on regen-eration of woody plants, we conducted this review of gap effects on woody plant regeneration on the basis of 527 observations from 42 indi-vidual papers, and reported the results of these data in a meta-analysis. Overall, densities of regenerated woody plants were significantly greater （359%） in forest gaps than on the closed-canopy forest floor. The regen-eration density in gaps of plantation forests was significantly greater （P＆lt;0.05） than that of natural forest because the regeneration in gaps of plan-tation forests was improved by both gap effects and experimental meas-ures. Similarly, in comparison to natural gaps, regeneration was better enhanced in artificial gaps. Regeneration density exhibited a significantly positive correlation with gap size, but a negative correlation with gap age because the gap size decreased with increasing gap age. Shade tolerance of woody plants affected regeneration density in gaps and understory. Average regeneration density of shade-tolerant species exhibited a sig-nificantly positive response to gaps but densities remained lower in total than those of intermediate and shade-intolerant species. Gap effects on regeneration decreased in response to increasing temperature and pre-cipitation because of the limiting effects of lower temperature and moisture on woody plant regeneration. In summary, forest gaps enhance woody plant regeneration, and the effects of gaps varied by forest type, gap characteristics, environmental factors and plant traits. The results of this meta-analysis are useful for better understanding the effects and roles of gaps on forest regeneration and forest management.