Development and evaluation of an individual tree growth and yield model for the mixed species forest of the Adirondacks Region of New York, USA

Background： Growth and yield models are important tools for forest planning. Due to its geographic location, topology, and history of management, the forests of the Adirondacks Region of New York are unique and complex. However, only a relatively limited number of growth and yield models have been developed and/or can be reasonably extended to this region currently. Methods： in this analysis, 571 long-term continuous forest inventory plots with a total of 10 - 52 years of measurement data from four experimental forests maintained by the State University of New York College of Environmental Science and Forestry and one nonindustrial private forest were used to develop an individual tree growth model for the primary hardwood and softwood species in the region. Species-specific annualized static and dynamic equations were developed using the available data and the system was evaluated for long-term behavior. Results： Equivalence tests indicated that the Northeast Variant of the Forest Vegetation Simulator （FVS-NE） was biased in its estimation of tree total and bole height, diameter and height increment, and mortality for most species examined. In contrast, the developed static and annualized dynamic, species-specific equations performed quite well given the underlying variability in the data. Long-term model projections were consistent with the data and suggest a relatively robust system for prediction. Conclusions： Overall, the developed growth model showed reasonable behavior and is a significant improvement over existing models for the region. The model also highlighted the complexities of forest dynamics in the region and should help improve forest planning efforts there.

An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

Individual tree segmentation and biomass estimation based on UAV Digital aerial photograph

Digital aerial photograph(DAP)data is processed based on Structure from Motion(Sf M)algorithm and regional net adjustment method to generate digital surface discrete point clouds similar to Light Detection and Ranging(LiDAR)and digital orthophoto mosaic(DOM)similar to optical remote sensing image.In this study,we obtained highresolution images of mature forests of Chinese fir by unmanned aerial vehicle(UAV)flying through crossroute flight,and then reconstructed the threedimensional point clouds in the UAV aerial area by SfM technique.The point cloud segmentation(PCS)algorithm was used for the individual tree segmentation,and the F-score of the three sample plots were 0.91,0.94,and 0.94,respectively.Individual tree biomass modeling was conducted using 155 mature Chinese fir forests which were correctly segmented.The relative root mean squared error(rRMSE)values of random forest(RF),bagged tree(BT)and support vector regression(SVR)were 34.48%,35.74%and 40.93%,respectively.Our study demonstrated that DAP point clouds had great potential to extract forest vertical parameters and could be applied successfully in individual tree segmentation and individual tree biomass modeling.

Can visceral fat parameters based on computed tomography be used to predict occult peritoneal metastasis in gastric cancer?

BACKGROUND The preoperative prediction of peritoneal metastasis(PM)in gastric cancer would prevent unnecessary surgery and promptly indicate an appropriate treatment plan.AIM To explore the predictive value of visceral fat(VF)parameters obtained from preoperative computed tomography(CT)images for occult PM and to develop an individualized model for predicting occult PM in patients with gastric carcinoma(GC).METHODS A total of 128 confirmed GC cases(84 male and 44 female patients)that underwent CT scans were analyzed and categorized into PM-positive(n=43)and PM-negative(n=85)groups.The clinical characteristics and VF parameters of two regions of interest(ROIs)were collected.Univariate and stratified analyses based on VF volume were performed to screen for predictive characteristics for occult PM.Prediction models with and without VF parameters were established by multivariable logistic regression analysis.RESULTS The mean attenuations of VF_(ROI 1)and VF_(ROI 2)varied significantly between the PM-positive and PMnegative groups(P=0.044 and 0.001,respectively).The areas under the receiver operating characteristic curves(AUCs)of VF_(ROI 1)and VF_(ROI 2)were 0.599 and 0.657,respectively.The mean attenuation of VF_(ROI 2)was included in the final prediction combined model,but not an independent risk factor of PM(P=0.068).No significant difference was observed between the models with and without mean attenuation of VF(AUC:0.749 vs 0.730,P=0.339).CONCLUSION The mean attenuation of VF is a potential auxiliary parameter for predicting occult PM in patients with GC.

Existence and Uniqueness of Positive Solutions for a System of Multi-order Fractional Differential Equations

In this paper, we study a class of ruin problems, in which premiums and claims are dependent. Under the assumption that premium income is a stochastic process, we raise the model that premiums and claims are dependent, give its numerical characteristics and the ruin probability of the individual risk model in the surplus process. In addition, we promote the number of insurance policies to a Poisson process with parameter λ, using martingale methods to obtain the upper bound of the ultimate ruin probability.

Stochastic loss reserving using individual information model with over-dispersed Poisson

For stochastic loss reserving,we propose an individual information model(IIM)which accom-modates not only individual/micro data consisting of incurring times,reporting developments,settlement developments as well as payments of individual claims but also heterogeneity among policies.We give over-dispersed Poisson assumption about the moments of reporting developments and payments of every individual claims.Model estimation is conducted under quasi-likelihood theory.Analytic expressions are derived for the expectation and variance of outstanding liabilities,given historical observations.We utilise conditional mean square error of prediction(MSEP)to measure the accuracy of loss reserving and also theoretically prove that when risk portfolio size is large enough,IIM shows a higher prediction accuracy than individ-ual/micro data model(IDM)in predicting the outstanding liabilities,if the heterogeneity indeed influences claims developments and otherwise IIM is asymptotically equivalent to IDM.Some simulations are conducted to investigate the conditional MSEPs for IIM and IDM.A real data analysis is performed basing on real observations in health insurance.

SCHISTOX: An individual based model for the epidemiology and control of schistosomiasis

A stochastic individual based model,SCHISTOX,has been developed for the study of schistosome transmission dynamics and the impact of control by mass drug administration.More novel aspects that can be investigated include individual level adherence and access to treatment,multiple communities,human sex population dynamics,and implementation of a potential vaccine.Many of the model parameters have been estimated within previous studies and have been shown to vary between communities,such as the age-specific contact rates governing the age profiles of infection.However,uncertainty remains as there are wide ranges for certain parameter values and a few remain relatively unknown.We analyse the model dynamics by parameterizing it with published parameter values.We also discuss the development of SCHISTOX in the form of a publicly available open-source GitHub repository.The next key development stage involves validating the model by calibrating to epidemiological data.

Net Primary Productivity and Management Potential of Artificial Pinus tabulaeformis Forest in Shanxi Province

The dynamic variation of net primary productivity of artificial Pinus tabulaeformis forest was studied in Shanxi Province,and potential productivity of artificial forest was predicted to provide reference for improving quality of regional forest stand. The regression equation was established by using the stratification and harvesting method with the relative growth model. Cumulative method and Thornthwaite Memorial model was used to estimate the actual and potential productivity of the forest. The productivity of P. tabulaeformis forest increased with the increase of age and started decrease with the mature period. The actual productivity of P. tabulaeformis forest was 4. 462 t/( ha·year); the contribution rate of trees was 72. 17% of the total productivity,and with the increase of age,the total biomass increased but productivity decreased at late near-mature forest; the contribution rate of herb layer was 21. 16% in the young forest stage,and then decreased gradually. On the contrary,the contribution rate of shrub layer increased gradually,and the contribution rate of the grassland was more than that of the herb layer,so as the key period of structural management; the average potential productivity of forest was 8. 422 t/( ha·year),and the potential space of P. tabulaeformis was at least 32% in Shanxi Province. In conclusion,the potential space of productivity of P. tabulaeformis was at least 32%,and the primary limiting factor of P. tabulaeformis forest productivity in Shanxi Province was rainfall.

Development and validation of a web-based calculator to predict individualized conditional risk of site-specific recurrence in nasopharyngeal carcinoma: Analysis of 10,058 endemic cases

Background:Conditional survival(CS)provides dynamic prognostic estimates by considering the patients existing survival time.Since CS for endemic nasopharyngeal carcinoma(NPC)is lacking,we aimed to assess the CS of endemic NPC and establish a web-based calculator to predict individualized,conditional sitespecific recurrence risk.Methods:Using an NPC-specific database with a big-data intelligence platform,10,058 endemic patients with non-metastatic stage I–IVA NPC receiving intensity-modulated radiotherapy with or without chemotherapy between April 2009 and December 2015 were investigated.Crude CS estimates of conditional overall survival(COS),conditional disease-free survival(CDFS),conditional locoregional relapse-free survival(CLRRFS),conditional distant metastasis-free survival(CDMFS),and conditional NPC-specific survival(CNPC-SS)were calculated.Covariate-adjusted CS estimates were generated using inverse probability weighting.A prediction model was established using competing risk models and was externally validated with an independent,non-metastatic stage I–IVA NPC cohort undergoing intensity-modulated radiotherapy with or without chemotherapy(n=601)at another institution.Results:The median follow-up of the primary cohort was 67.2 months.The 5-year COS,CDFS,CLRRFS,CDMFS,and CNPC-SS increased from 86.2%,78.1%,89.8%,87.3%,and 87.6%at diagnosis to 87.3%,87.7%,94.4%,96.0%,and 90.1%,respectively,for an existing survival time of 3 years since diagnosis.Differences in CS estimates between prognostic factor subgroups of each endpoint were noticeable at diagnosis but diminished with time,whereas an ever-increasing disparity in CS between different age subgroups was observed over time.Notably,the prognoses of patients that were poor at diagnosis improved greatly as patients survived longer.For individualized CS predictions,we developed a web-based model to estimate the conditional risk of local(C-index,0.656),regional(0.667),bone(0.742),lung(0.681),and liver(0.711)recurrence,which significantly outperformed the current staging system(P<0.001).The performance of this webbased model was further validated using an external validation cohort(median follow-up,61.3 months),with C-indices of 0.672,0.736,0.754,0.663,and 0.721,respectively.Conclusions:We characterized the CS of endemic NPC in the largest cohort to date.Moreover,we established a web-based calculator to predict the CS of sitespecific recurrence,which may help to tailor individualized,risk-based,timeadapted follow-up strategies.

Landscape structure and suitable habitat analysis for effective restoration planning in semi-arid mountain forests

Background:Suitable habitat and landscape structure play a pivotal role in the success of forest restoration projects.This study aimed to model the habitat suitability of wild almond(Amygdalus scoparia Spach)using three individual species distribution models(SDMs),i.e.,backpropagation artificial neural network(BP-ANN),maximum entropy(MaxEnt),generalized linear model(GLM),as well as the ensemble technique along with measuring the landscape metrics and analyzing the relationship between the distribution of the suitable habitat of the species in different landform classes in Fars Province,southern Iran.Results:There was no clear difference in the prediction performance of the models.The BP-ANN had the highest accuracy(AUC=0.935 and k=0.757)in modeling habitat suitability of A.scoparia,followed by the ensemble technique,GLM,and MaxEnt models with the AUC values of 0.890,0.887,and 0.777,respectively.The highest discrimination capacity was associated to the BP-ANN model,and the highest reliability was related to the ensemble technique.Moreover,evaluation of variable importance showed that the occurrence of A.scoparia was strongly dependent on climatic variables,particularly isothermality(Bio 3),temperature seasonality(Bio 4),and precipitation of driest quarter(Bio 17).Analysis of the distribution of species habitat in different landform classes revealed that the canyon,mountain top,upland drainage,and hills in valley classes had the highest suitability for the species establishment.Conclusions:Considering the importance of landform in the establishment of plant habitats,the combination of the outputs of the SDMs,landform,and the use of landscape metrics could provide both a clear view of habitat conditions and the possibility of analyzing habitat patches and their relationships that can be very useful in managing the remaining forests in semi-arid regions.The canyon,mountain top,and upland drainage classes were found to be the most important landforms to provide the highest suitable environmental conditions for the establishment of A.scoparia.Therefore,such landforms should be given priority in restoration projects of forest in the study area.