Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models

There are typical ecosystems of littoral wetlands in the Yellow River Delta.In order to study the relationships between Tamarix chinensis and environmental variables and to predict T.chinensis potential distribution in the Yellow River Delta,641 vegetation samples and 964 soil samples were collected in the area in October of 2004,2005,2006 and 2007.The contents of soil organic matter,total phosphorus,salt,and soluble potassium were determined.Then,the analyzed data were interpolated into spatial raster data by Kriging interpolation method.Meanwhile,the digital elevation model,soil type map and landform unit map of the Yellow River Delta were also collected.Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T.chinensis.The results indicated that the distribution of T.chinensis was mainly limited by soil salt content,total soil phosphorus content,soluble potassium content,soil type,landform unit,and elevation.The distribution probability of T.chinensis was produced with a lookup table generated by Grasp Module(based on GAMs) in software ArcView GIS 3.2.The AUC(Area Under Curve) value of validation and cross-validation of ROC(Receive Operating Characteristic) were both higher than 0.8,which suggested that the established model had a high precision for predicting species distribution.

Probabilistic Precipitation Forecasting Based on Ensemble Output Using Generalized Additive Models and Bayesian Model Averaging

A probabilistic precipitation forecasting model using generalized additive models （GAMs） and Bayesian model averaging （BMA） was proposed in this paper. GAMs were used to fit the spatial-temporal precipitation models to individual ensemble member forecasts. The distributions of the precipitation occurrence and the cumulative precipitation amount were represented simultaneously by a single Tweedie distribution. BMA was then used as a post-processing method to combine the individual models to form a more skillful probabilistic forecasting model. The mixing weights were estimated using the expectation-maximization algorithm. The residual diagnostics was used to examine if the fitted BMA forecasting model had fully captured the spatial and temporal variations of precipitation. The proposed method was applied to daily observations at the Yishusi River basin for July 2007 using the National Centers for Environmental Prediction ensemble forecasts. By applying scoring rules, the BMA forecasts were verified and showed better performances compared with the empirical probabilistic ensemble forecasts, particularly for extreme precipitation. Finally, possible improvements and a^plication of this method to the downscaling of climate change scenarios were discussed.

Inference Procedures on the Generalized Poisson Distribution from Multiple Samples: Comparisons with Nonparametric Models for Analysis of Covariance (ANCOVA) of Count Data

Count data that exhibit over dispersion (variance of counts is larger than its mean) are commonly analyzed using discrete distributions such as negative binomial, Poisson inverse Gaussian and other models. The Poisson is characterized by the equality of mean and variance whereas the Negative Binomial and the Poisson inverse Gaussian have variance larger than the mean and therefore are more appropriate to model over-dispersed count data. As an alternative to these two models, we shall use the generalized Poisson distribution for group comparisons in the presence of multiple covariates. This problem is known as the ANCOVA and is solved for continuous data. Our objectives were to develop ANCOVA using the generalized Poisson distribution, and compare its goodness of fit to that of the nonparametric Generalized Additive Models. We used real life data to show that the model performs quite satisfactorily when compared to the nonparametric Generalized Additive Models.

Grouping tree species to estimate basal area increment in temperate multispecies forests in Durango,Mexico

Multispecies forests have received increased scientific attention,driven by the hypothesis that biodiversity improves ecological resilience.However,a greater species diversity presents challenges for forest management and research.Our study aims to develop basal area growth models for tree species cohorts.The analysis is based on a dataset of 423 permanent plots(2,500 m^(2))located in temperate forests in Durango,Mexico.First,we define tree species cohorts based on individual and neighborhood-based variables using a combination of principal component and cluster analyses.Then,we estimate the basal area increment of each cohort through the generalized additive model to describe the effect of tree size,competition,stand density and site quality.The principal component and cluster analyses assign a total of 37 tree species to eight cohorts that differed primarily with regard to the distribution of tree size and vertical position within the community.The generalized additive models provide satisfactory estimates of tree growth for the species cohorts,explaining between 19 and 53 percent of the total variation of basal area increment,and highlight the following results:i)most cohorts show a"rise-and-fall"effect of tree size on tree growth;ii)surprisingly,the competition index"basal area of larger trees"had showed a positive effect in four of the eight cohorts;iii)stand density had a negative effect on basal area increment,though the effect was minor in medium-and high-density stands,and iv)basal area growth was positively correlated with site quality except for an oak cohort.The developed species cohorts and growth models provide insight into their particular ecological features and growth patterns that may support the development of sustainable management strategies for temperate multispecies forests.

Analysis of the Influence of Heat Wave on Death among the Elderly in Nanjing City

To obtain the influence of heat waves on death in the elderly, the influence of the heat waves in Nanjing in the summers (from June to August) of 2005-2008 on death among the elderly was analyzed by using statistical methods including generalized additive models. The results showed that the death toll over these four summers in Nanjing tended to increase;on an average 10.76% more males died than females, and the mortality rate of old people aged ≥65 accounted for 73.21% of all deaths. The mortality rate of older people rose with increasing maximum temperature. Furthermore, the average excess mortality rate caused by heat wave weather processes was 15.91%, while it was less affected by the duration of the heat wave. The death toll of the elderly increased with the increase in humidity, dropping of atmospheric pressure, and decrease of wind speed for 1°C increase of maximum temperature. Under the same humidity condition, atmospheric pressure, and wind speed, the death toll during heat wave days was higher than that occurring on other days, and heat waves increased the risk of death among the elderly by 26.6% (95% CI: 1.100 - 1.154). Daily mortality was mainly affected by the daily maximum temperature 1, 4, or 6 days later, particularly 4 days later. Heat wave was one of the principal factors, which caused the rise in death tolls in summer, and the elderly were most affected.

Growth and form of Quercus robur and Fraxinus excelsior respond distinctly different to initial growing space: results from 24-year-old Nelder experiments

Initial growing space is of critical importance to growth and quality development of individual trees. We investigated how mortality, growth （diameter at breast height, total height）, natural pruning （height to first dead and first live branch and branchiness） and stem and crown form of 24-year-old pedunculate oak （Quercus robur [L.]） and European ash （Fraxinus excelsior [L.]） were affected by initial spacing. Data were recorded from two replicate single-species Nelder wheels located in southern Germany with eight initial stocking regimes varying from 1,020 to 30,780 seedlings·ha？1. Mortality substantially decreased with increasing initial growing space but significantly differed among the two species, averaging 59% and 15% for oak and ash plots, respectively. In contrast to oak, the low self-thinning rate found in the ash plots over the investigated study period resulted in a high number of smaller intermediate or suppressed trees, eventually retarding individual tree as well as overall stand development. As a result, oak gained greater stem dimensions throughout all initial spacing regimes and the average height of ash significantly increased with initial growing space. The survival of lower crown class ashes also appeared to accelerate self-pruning dynamics. In comparison to oak, we observed less dead and live primary branches as well as a smaller number of epicormic shoots along the first 6 m of the lower stem of dominant and co-dominant ashes in all spacing regimes. Whereas stem form of both species was hardly affected by initial growing space, the percentage of brushy crowns significantly increased with initial spacing in oak and ash. Our findings suggest that initial stockings of ca. 12,000 seedlings per hectare in oak and 2,500 seedlings per hectare in ash will guarantee a sufficient number of at least 300 potential crop trees per hectare in pure oak and ash plantations at the end of the self-thinning phase, respectively. If the problem of epicormic shoots and inadequate self-pruning can be controlled with trainer species, the initial stocking may be reduced significantly in oak.

Optimization of environmental variables in habitat suitability modeling for mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent waters

Habitat suitability index(HSI)models have been widely used to analyze the relationship between species abundance and environmental factors,and ultimately inform management of marine species.The response of species abundance to each environmental variable is different and habitat requirements may change over life history stages and seasons.Therefore,it is necessary to determine the optimal combination of environmental variables in HSI modelling.In this study,generalized additive models(GAMs)were used to determine which environmental variables to be included in the HSI models.Significant variables were retained and weighted in the HSI model according to their relative contribution(%)to the total deviation explained by the boosted regression tree(BRT).The HSI models were applied to evaluate the habitat suitability of mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent areas in 2011 and 2013–2017.Ontogenetic and seasonal variations in HSI models of mantis shrimp were also examined.Among the four models(non-optimized model,BRT informed HSI model,GAM informed HSI model,and both BRT and GAM informed HSI model),both BRT and GAM informed HSI model showed the best performance.Four environmental variables(bottom temperature,depth,distance offshore and sediment type)were selected in the HSI models for four groups(spring-juvenile,spring-adult,falljuvenile and fall-adult)of mantis shrimp.The distribution of habitat suitability showed similar patterns between juveniles and adults,but obvious seasonal variations were observed.This study suggests that the process of optimizing environmental variables in HSI models improves the performance of HSI models,and this optimization strategy could be extended to other marine organisms to enhance the understanding of the habitat suitability of target species.

National-scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data

China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.

The bacterial abundance and production in the East China Sea: seasonal variations and relationships with the phytoplankton biomass and production

The East China Sea is a productive marginal sea with a wide continental shelf and plays an important role in absorbing atmospheric carbon dioxide and transferring terrigenous organic matter to the open ocean. To investigate the roles of heterotrophic bacteria in the biogeochemical dynamics in the East China Sea, bacterial biomasses （BB） and productions （BP） were measured in four cruises. The spatial distributions of the BB and the BP were highly season-dependent. Affected by the Changjiang River discharge, the BB and the BP were high in shelf waters （bottom depth not deeper than 50 m） and generally decreased offshore in August 2009. In December 2009 to lanuary 2010, and November to December 2010, the BB and the BP were high in waters with medium bottom depth. The onshore-offshore decreasing trends of the BB and the BP also existed in May-June 2011, when the BB was significantly higher than in other cruises in shelf break waters （bottom depth deeper than 50 m but not deeper than 200 m）. The results of generalized additive models （GAM） suggest that the BB increased with the temperature at a range of 8-20~C, increased with the chlorophyll concentration at a range of 0.02-3.00 mg/m3 and then declining, and decreased with the salinity from 28 to 35. The relationship between the temperature and the log-transformed bacterial specific growth rate （SGR） was linear. The estimated temperature coefficient （Q10） of the SGR was similar with that of the phytoplankton growth. The SGR also increased with the chlorophyll concentration. The ratio of the bacterial to phytoplankton production ranged from less than 0.01 to 0.40, being significantly higher in November December 2010 than in May-June 2011. Calculated from the bacterial production and growth efficiency, the bacterial respiration consumed, on average, 59%, 72% and 23% of the primary production in August 2009, November-December 2010, and May-/une 2011, respectively.

Effect of climate factors on the incidence of hand, foot, and mouth disease in Malaysia: A generalized additive mixed model

Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus pandemic.HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s.The outbreaks may result from a combination of rapid population growth,climate change,socioeconomic changes,and other lifestyle changes.However,the modeling of climate variability and HFMD remains unclear,particularly in statistical theory development.The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling.When dealing with time-series data with clustered variables such as HFMD with clustered states,the independence principle of both modeling approaches may be violated.Thus,a Generalized Additive Mixed Model(GAMM)is used to investigate the relationship between HFMD and climate factors in Malaysia.The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect.This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation.The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia.The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm.Besides,a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances.The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon.The study's outcomes can be used by public health officials and the general public to raise awareness,and thus,implement effective preventive measures.

The Potential Vertical Distribution of Bigeye Tuna (Thunnus obesus) and Its Influence on the Spatial Distribution of CPUEs in the Tropical Atlantic Ocean

Understanding the potential vertical distribution of bigeye tuna(Thunnus obesus) is necessary to understand the catch rate fluctuations and the stock assessment of bigeye tuna. To characterize the potential vertical distribution of this fish while foraging and determine the influences of the distribution on longline efficiency in the tropical Atlantic Ocean, the catch per unit effort(CPUE) data were compiled from the International Commission for the Conservation of Atlantic Tunas and the Argo buoy data were downloaded from the Argo data center. The raw Argo buoy data were processed by data mining methods. The CPUE was standardized by support vector machine before analysis. We assumed the depths with the upper and lower limits of the optimum water temperatures of 15℃ and 9℃ as the preferred swimming depth, while the lower limit of the temperature(12℃) associated with the highest hooking rate as the preferred foraging depth(D12) of bigeye tuna during the daytime in the Atlantic Ocean. The preferred swimming depth and foraging depth range in the daytime were assessed by plotting the isobath based on Argo buoy data. The preferred swimming depth and vertical structure of the water column were identified to investigate the spatial effects on the CPUE by using a generalized additive model(GAM). The empirical cumulative distribution function was used to assess the relationship between the spatial distribution of CPUE and the depth of 12℃ isolines and thermocline. The results indicate that 1) the preferred swimming depth of bigeye tuna in the tropical Atlantic is from 100 m to 400 m and displays spatial variation;2) the preferred foraging depth of bigeye tuna is between 190 and 300 m and below the thermocline;3) the number of CPUEs peaks at a relative depth of 30 –50 m(difference between the 12℃ isolines and the lower boundary of the thermocline);and 4) most CPUEs are within the lower depth boundary of the thermocline levels(LDBT) which is from 160 m to 230 m. GAM analysis indicates that the general relationship between the nominal CPUE and LDBT is characterized by a dome shape and peaks at approximately 190 m. The oceanographic features influence the habitat of tropical pelagic fish and fisheries. Argo buoy data can be an important tool to describe the habitat of oceanic fish. Our results provide new insights into how oceanographic features influence the habitat of tropical pelagic fish and fisheries and how fisheries exploit these fish using a new tool(Argo profile data).

Hierarchical responses of soil organic and inorganic carbon dynamics to soil acidification in a dryland agroecosystem,China

Soil acidification is a major global issue of sustainable development for ecosystems. The increasing soil acidity induced by excessive nitrogen （N） fertilization in farmlands has profoundly impacted the soil carbon dynamics. However, the way in which changes in soil pH regulating the soil carbon dynamics in a deep soil profile is still not well elucidated. In this study, through a 12-year field N fertilization experiment with three N fertilizer treatments （0, 120, and 240 kg N/（hm-2·a）） in a dryland agroecosystem of China, we explored the soil pH changes over a soil profile up to a depth of 200 cm and determined the responses of soil organic carbon （SOC） and soil inorganic carbon （SIC） to the changed soil pH. Using a generalized additive model, we identified the soil depth intervals with the most powerful statistical relationships between changes in soil pH and soil carbon dynamics. Hierarchical responses of SOC and SIC dynamics to soil acidification were found. The results indicate that the changes in soil pH explained the SOC dynamics well by using a non-linear relationship at the soil depth of 0-80 cm （P=0.006）, whereas the changes in soil pH were significantly linearly correlated with SIC dynamics at the 100-180 cm soil depth （P=0.015）. After a long-term N fertilization in the experimental field, the soil pH value decreased in all three N fertilizer treatments. Furthermore, the declines in soil pH in the deep soil layer （100-200 cm） were significantly greater （P=0.035） than those in the upper soil layer （0-80 cm）. These results indicate that soil acidification in the upper soil layer can transfer excess protons to the deep soil layer, and subsequently, the structural heterogeneous responses of SOC and SIC to soil acidification were identified because of different buffer capacities for the SOC and SIC. To better estimate the effects of soil acidification on soil carbon dynamics, we suggest that future investigations for soil acidification should be extended to a deeper soil depth, e.g., 200 cm.

Spatio-temporal distribution of Konosirus punctatus spawning and nursing ground in the South Yellow Sea

In recent years,Konosirus punctatus has accounted for a large portion in catch composition and become important economic species in the South Yellow Sea.However,the distribution of K.punctatus early life stages is still poorly understood.In this study,generalized additive models with Tweedie distribution were used to analyze the relationships between K.punctatus ichthyoplankton and environmental factors(longitude and latitude,sea surface temperature(SST),sea surface salinity(SSS)and depth),and predict distribution K.punctatus spawning ground and nursing ground,based on samplings collected in 6 months during 2014–2017.The results showed that K.punctatus’spawning ground were mainly distributed in central and north study area(from 33.0°N to 37.0°N).By comparison,the nursing ground shifted southward,which were approximately located along central and south coast of study area(from 31.7°N to 35.5°N).The optimal models identified that suitable SST,SSS and depth for eggs were 19–26℃,25–30 and 9–23 m,respectively.The suitable SSS for larvae were 29–31.The K.punctatus spawning habit might have changed in the past decades,which was a response to increasing SST and fishing pressure.That needs to be proved in further study.The study provides references of conservation and exploitation for K.punctatus.

Evaluating the eff ect of input variables on quantifying the spatial distribution of croaker Johnius belangerii in Haizhou Bay,China

A habitat model has been widely used to manage marine species and analyze relationship between species distribution and environmental factors.The predictive skill in habitat model depends on whether the models include appropriate explanatory variables.Due to limited habitat range,low density,and low detection rate,the number of zero catches could be very large even in favorable habitats.Excessive zeroes will increase the bias and uncertainty in estimation of habitat.Therefore,appropriate explanatory variables need to be chosen first to prevent underestimate or overestimate species abundance in habitat models.In addition,biotic variables such as prey data and spatial autocovariate(SAC)of target species are often ignored in species distribution models.Therefore,we evaluated the eff ects of input variables on the performance of generalized additive models(GAMs)under excessive zero catch(>70%).Five types of input variables were selected,i.e.,(1)abiotic variables,(2)abiotic and biotic variables,(3)abiotic variables and SAC,(4)abiotic,biotic variables and SAC,and(5)principal component analysis(PCA)based abiotic and biotic variables and SAC.Belanger’s croaker Johnius belangerii is one of the dominant demersal fish in Haizhou Bay,with a large number of zero catches,thus was used for the case study.Results show that the PCA-based GAM incorporated with abiotic and biotic variables and SAC was the most appropriate model to quantify the spatial distribution of the croaker.Biotic variables and SAC were important and should be incorporated as one of the drivers to predict species distribution.Our study suggests that the process of input variables is critical to habitat modelling,which could improve the performance of habitat models and enhance our understanding of the habitat suitability of target species.

Interannual variability of dimethylsulfide in the Yellow Sea

The 22-year(1998-2019)surface seawater dimethylsulfi de(DMS)concentrations in the Yellow Sea(YS)were hindcasted based on satellite sea surface temperature(SST)and chlorophyll-a(Chl-a)data using a generalized additive mixed model(GAMM).A continuous monthly dataset of DMS concentration in the YS was obtained after using the data interpolation empirical orthogonal function(DINEOF)to reconstruct missing information in the dataset.Then,the interannual DMS variability in the YS was analyzed.The results indicated that the monthly climatological DMS concentration in the YS was 3.61 nmol/L.DMS concentrations in the spring and summer were signifi cantly higher than those in the autumn and winter.DMS concentrations were highest in coastal YS waters and lowest primarily in off shore YS waters.Interannual DMS variability between 1998 and 2019 was subdivided into two inverse phases:with the exception of the central YS,DMS increased before the turning point and decreased after.The turning point in interannual DMS variation was earlier in the inshore YS as compared to the central YS.Spectrum analysis identifi ed some signifi cant patterns of interannual variation in the DMS anomaly in the YS.Chl a appeared to be the main factor infl uencing interannual trends in DMS in the YS.Interannual DMS variability was under the joint control of Chl a and SST.However,short-term interannual DMS variation(2-3 years)was primarily related to SST,while longer term interannual DMS variation(6-8 years)was signifi cantly correlated with Chl a and SST.

Linearity extensions of the market model:a case of the top 10 cryptocurrency prices during the pre‑COVID‑19 and COVID‑19 periods

This research investigates the appropriateness of the linear specification of the market model for modeling and forecasting the cryptocurrency prices during the pre-COVID-19 and COVID-19 periods.Two extensions are offered to compare the performance of the linear specification of the market model(LMM),which allows for the measurement of the cryptocurrency price beta risk.The first is the generalized additive model,which permits flexibility in the rigid shape of the linearity of the LMM.The second is the time-varying linearity specification of the LMM(Tv-LMM),which is based on the state space model form via the Kalman filter,allowing for the measurement of the time-varying beta risk of the cryptocurrency price.The analysis is performed using daily data from both time periods on the top 10 cryptocurrencies by adjusted market capitalization,using the Crypto Currency Index 30(CCI30)as a market proxy and 1-day and 7-day forward predictions.Such a comparison of cryptocurrency prices has yet to be undertaken in the literature.The empirical findings favor the Tv-LMM,which outperforms the others in terms of modeling and forecasting performance.This result suggests that the relationship between each cryptocurrency price and the CCI30 index should be locally instead of globally linear,especially during the COVID-19 period.

Modeling deformation resistance for hot rolling based on generalized additive model

A model of deformation resistance during hot strip rolling was established based on generalized additive model.Firstly,a data modeling method based on generalized additive model was given.It included the selection of dependent variable and independent variables of the model,the link function of dependent variable and smoothing functional form of each independent variable,estimating process of the link function and smooth functions,and the last model modification.Then,the practical modeling test was carried out based on a large amount of hot rolling process data.An integrated variable was proposed to reflect the effects of different chemical compositions such as carbon,silicon,manganese,nickel,chromium,niobium,etc.The integrated chemical composition,strain,strain rate and rolling temperature were selected as independent variables and the cubic spline as the smooth function for them.The modeling process of deformation resistance was realized by SAS software,and the influence curves of the independent variables on deformation resistance were obtained by local scoring algorithm.Some interesting phenomena were found,for example,there is a critical value of strain rate,and the deformation resistance increases before this value and then decreases.The results confirm that the new model has higher prediction accuracy than traditional ones and is suitable for carbon steel,microalloyed steel,alloyed steel and other steel grades.

Model-based estimation of above-ground biomass in the miombo ecoregion of Zambia

Background：Information on above-ground biomass（AGB） is important for managing forest resource use at local levels,land management planning at regional levels,and carbon emissions reporting at national and international levels.In many tropical developing countries,this information may be unreliable or at a scale too coarse for use at local levels.There is a vital need to provide estimates of AGB with quantifiable uncertainty that can facilitate land use management and policy development improvements.Model-based methods provide an efficient framework to estimate AGB.Methods：Using National Forest Inventory（NFI） data for a^1,000,000 ha study area in the miombo ecoregion,Zambia,we estimated AGB using predicted canopy cover,environmental data,disturbance data,and Landsat 8 OLI satellite imagery.We assessed different combinations of these datasets using three models,a semiparametric generalized additive model（GAM） and two nonlinear models（sigmoidal and exponential）,employing a genetic algorithm for variable selection that minimized root mean square prediction error（RMSPE）,calculated through cross-validation.We compared model fit statistics to a null model as a baseline estimation method.Using bootstrap resampling methods,we calculated 95% confidence intervals for each model and compared results to a simple estimate of mean AGB from the NFI ground plot data.Results：Canopy cover,soil moisture,and vegetation indices were consistently selected as predictor variables.The sigmoidal model and the GAM performed similarly;for both models the RMSPE was -36.8 tonnes per hectare（i.e.,57% of the mean）.However,the sigmoidal model was approximately 30% more efficient than the GAM,assessed using bootstrapped variance estimates relative to a null model.After selecting the sigmoidal model,we estimated total AGB for the study area at 64,526,209 tonnes（＋/- 477,730）,with a confidence interval 20 times more precise than a simple designbased estimate.Conclusions：Our findings demonstrate that NFI data may be combined with freely available satellite imagery and soils data to estimate total AGB with quantifiable uncertainty,while also providing spatially explicit AGB maps useful for management,planning,and reporting purposes.

A spatially-explicit count data regression for modeling the density of forest cockchafer(Melolontha hippocastani) larvae in the Hessian Ried(Germany)

Background： In this paper, a regression model for predicting the spatial distribution of forest cockchafer larvae in the Hessian Ried region （Germany） is presented. The forest cockchafer, a native biotic pest, is a major cause of damage in forests in this region particularly during the regeneration phase. The model developed in this study is based on a systematic sample inventory of forest cockchafer larvae by excavation across the Hessian Ried. These forest cockchafer larvae data were characterized by excess zeros and overdispersion. Methods： Using specific generalized additive regression models, different discrete distributions, including the Poisson, negative binomial and zero-inflated Poisson distributions, were compared. The methodology employed allowed the simultaneous estimation of non-linear model effects of causal covariates and, to account for spatial autocorrelation, of a 2-dimensional spatial trend function. In the validation of the models, both the Akaike information criterion （AIC） and more detailed graphical procedures based on randomized quantile residuals were used. Results： The negative binomial distribution was superior to the Poisson and the zero-inflated Poisson distributions, providing a near perfect fit to the data, which was proven in an extensive validation process. The causal predictors found to affect the density of larvae significantly were distance to water table and percentage of pure clay layer in the soil to a depth of I m. Model predictions showed that larva density increased with an increase in distance to the water table up to almost 4 m, after which it remained constant, and with a reduction in the percentage of pure clay layer. However this latter correlation was weak and requires further investigation. The 2-dimensional trend function indicated a strong spatial effect, and thus explained by far the highest proportion of variation in larva density. Conclusions： As such the model can be used to support forest practitioners in their decision making for regeneration and forest protection planning in the Hessian predicting future spatial patterns of the larva density is still comparatively weak. Ried. However, the application of the model for somewhat limited because the causal effects are

Learning distance effect on lignite quality variables at global and local scales

Determining scale and variable effects have critical importance in developing an energy resource policy.This study aims to explore the relationships in heterogeneous lignite sites using different scale models,spatial weighting as well as error-based pair-wise identification.From a statistical learning framework,the relationships among the quality variables such as geochemical variables and the contributions of the coordinates to quality measures have been exhibited by generalized additive models.In this way,the critical roles of spatial weights provided by the coordinates have been specified at a global scale.The experimental studies reveal that incorporating the geological weighting in the models as the additional information improves both accuracy and transparency.Because relationships among lignite quality variables and sampling locations are spatially non-stationary,the local structure and interdependencies among the variables were analyzed by geographically weighting regression.The local analyses including spatial patterns of bandwidths,search domains as well as residual-based areal dependencies provided not only the critical zones but also availability of pair-wise model alternatives by calibrating a model at each point for location-specific parameter learning.The results completely show that the weighting models applied at different scales can take spatial heterogeneity into consideration and these abilities provide some meta-data and specific information using in sustainable energy planning.