Species distribution models are used to aid our understanding of the processes driving the spatial patterns of species’ habitats. This approach has received criticism, however, largely because it neglects landscape m...Species distribution models are used to aid our understanding of the processes driving the spatial patterns of species’ habitats. This approach has received criticism, however, largely because it neglects landscape metrics. We examined the relative impacts of landscape predictors on the accuracy of habitat models by constructing distribution models at regional scales incorporating environmental variables (climate, topography, vegetation, and soil types) and secondary species occurrence data, and using them to predict the occurrence of 36 species in 15 forest fragments where we conducted rapid surveys. We then selected six landscape predictors at the landscape scale and ran general linear models of species presence/absence with either a single scale predictor (the probabilities of occurrence of the distribution models or landscape variables) or multiple scale predictors (distribution models + one landscape variable). Our results indicated that distribution models alone had poor predictive abilities but were improved when landscape predictors were added; the species responses were not, however, similar to the multiple scale predictors. Our study thus highlights the importance of considering landscape metrics to generate more accurate habitat suitability models.展开更多
In stratified survey sampling, sometimes we have complete auxiliary information. One of the fundamental questions is how to effectively use the complete auxiliary information at the estimation stage. In this paper, we...In stratified survey sampling, sometimes we have complete auxiliary information. One of the fundamental questions is how to effectively use the complete auxiliary information at the estimation stage. In this paper, we extend the model-calibration method to obtain estimators of the finite population mean by using complete auxiliary information from stratified sampling survey data. We show that the resulting estimators effectively use auxiliary information at the estimation stage and possess a number of attractive features such as asymptotically design-unbiased irrespective of the working model and approximately model-unbiased under the model. When a linear working-model is used, the resulting estimators reduce to the usual calibration estimator（or GREG）.展开更多
Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have dev...Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.展开更多
Ongoing climate changes have a direct impact on forest growth;they also affect natural fire regimes,with further implications for forest composition.Understanding of how these will affect forests on decadal-to-centenn...Ongoing climate changes have a direct impact on forest growth;they also affect natural fire regimes,with further implications for forest composition.Understanding of how these will affect forests on decadal-to-centennial timescales is limited.Here we use reconstructions of past vegetation,fire regimes and climate during the Holocene to examine the relative importance of changes in climate and fire regimes for the abundance of key tree species in northeastern China.We reconstructed vegetation changes and fire regimes based on pollen and charcoal records from Gushantun peatland.We then used generalized linear modelling to investigate the impact of reconstructed changes in summer temperature,annual precipitation,background levels of fire,fire frequency and fire magnitude to identify the drivers of decadal-to-centennial changes in forest openness and composition.Changes in climate and fire regimes have independent impacts on the abundance of the key tree taxa.Climate variables are generally more important than fire variables in determining the abundance of individual taxa.Precipitation is the only determinant of forest openness,but summer temperature is more important than precipitation for individual tree taxa with warmer summers causing a decrease in cold-tolerant conifers and an increase in warmth-demanding broadleaved trees.Both background level and fire frequency have negative relationships with the abundance of most tree taxa;only Pinus increases as fire frequency increases.The magnitude of individual fires does not have a significant impact on species abundance on this timescale.Both climate and fire regime characteristics must be considered to understand changes in forest composition on the decadal-to-centennial timescale.There are differences,both in sign and magnitude,in the response of individual tree species to individual drivers.展开更多
Many products always operate under various complex environment conditions. To describe the dynamic influence of environment factors on their reliability, a method of reliability sensitivity analysis is proposed. In th...Many products always operate under various complex environment conditions. To describe the dynamic influence of environment factors on their reliability, a method of reliability sensitivity analysis is proposed. In this method, the location parameter is assumed as a function of relevant environment variables while the scale parameter is assumed as an unknown positive constant. Then, the location parameter function is constructed by using the method of radial basis function. Using the varied environment test data, the log-likelihood function is transformed to a generalized linear expression by describing the indicator as Poisson variable. With the generalized linear model, the maximum likelihood estimations of the model coefficients are obtained. With the reliability model, the reliability sensitivity is obtained. An instance analysis shows that the method is feasible to analyze the dynamic variety characters of reliability along with environment factors and is straightforward for engineering application.展开更多
in this paper, we describe a new method for assessing the degree in which the individual case influence the maximum likelihood estimation of link parameter in generalized linear model. Several influential statistics ...in this paper, we describe a new method for assessing the degree in which the individual case influence the maximum likelihood estimation of link parameter in generalized linear model. Several influential statistics are illustrated with two examples.展开更多
基金supported by the Biota Minas Program(Proc.No.APQ 03549-09)FAPEMIG(Proc.No.PCE-00106-12)
文摘Species distribution models are used to aid our understanding of the processes driving the spatial patterns of species’ habitats. This approach has received criticism, however, largely because it neglects landscape metrics. We examined the relative impacts of landscape predictors on the accuracy of habitat models by constructing distribution models at regional scales incorporating environmental variables (climate, topography, vegetation, and soil types) and secondary species occurrence data, and using them to predict the occurrence of 36 species in 15 forest fragments where we conducted rapid surveys. We then selected six landscape predictors at the landscape scale and ran general linear models of species presence/absence with either a single scale predictor (the probabilities of occurrence of the distribution models or landscape variables) or multiple scale predictors (distribution models + one landscape variable). Our results indicated that distribution models alone had poor predictive abilities but were improved when landscape predictors were added; the species responses were not, however, similar to the multiple scale predictors. Our study thus highlights the importance of considering landscape metrics to generate more accurate habitat suitability models.
基金Supported by the National Natural Science Foundation of China(10571093)
文摘In stratified survey sampling, sometimes we have complete auxiliary information. One of the fundamental questions is how to effectively use the complete auxiliary information at the estimation stage. In this paper, we extend the model-calibration method to obtain estimators of the finite population mean by using complete auxiliary information from stratified sampling survey data. We show that the resulting estimators effectively use auxiliary information at the estimation stage and possess a number of attractive features such as asymptotically design-unbiased irrespective of the working model and approximately model-unbiased under the model. When a linear working-model is used, the resulting estimators reduce to the usual calibration estimator（or GREG）.
基金This research was funded by the National Natural Science Foundation of China(grant no.32271881).
文摘Forest fires are natural disasters that can occur suddenly and can be very damaging,burning thousands of square kilometers.Prevention is better than suppression and prediction models of forest fire occurrence have developed from the logistic regression model,the geographical weighted logistic regression model,the Lasso regression model,the random forest model,and the support vector machine model based on historical forest fire data from 2000 to 2019 in Jilin Province.The models,along with a distribution map are presented in this paper to provide a theoretical basis for forest fire management in this area.Existing studies show that the prediction accuracies of the two machine learning models are higher than those of the three generalized linear regression models.The accuracies of the random forest model,the support vector machine model,geographical weighted logistic regression model,the Lasso regression model,and logistic model were 88.7%,87.7%,86.0%,85.0%and 84.6%,respectively.Weather is the main factor affecting forest fires,while the impacts of topography factors,human and social-economic factors on fire occurrence were similar.
基金This work was supported by the National Nature Science Foundation of China(awards 42,271,162,41,971,100)the Natural Science Foundation of Jilin Province(award 20220101149JC)the Scholarship Fund from China Scholarship Council(award 202,206,620,038).
文摘Ongoing climate changes have a direct impact on forest growth;they also affect natural fire regimes,with further implications for forest composition.Understanding of how these will affect forests on decadal-to-centennial timescales is limited.Here we use reconstructions of past vegetation,fire regimes and climate during the Holocene to examine the relative importance of changes in climate and fire regimes for the abundance of key tree species in northeastern China.We reconstructed vegetation changes and fire regimes based on pollen and charcoal records from Gushantun peatland.We then used generalized linear modelling to investigate the impact of reconstructed changes in summer temperature,annual precipitation,background levels of fire,fire frequency and fire magnitude to identify the drivers of decadal-to-centennial changes in forest openness and composition.Changes in climate and fire regimes have independent impacts on the abundance of the key tree taxa.Climate variables are generally more important than fire variables in determining the abundance of individual taxa.Precipitation is the only determinant of forest openness,but summer temperature is more important than precipitation for individual tree taxa with warmer summers causing a decrease in cold-tolerant conifers and an increase in warmth-demanding broadleaved trees.Both background level and fire frequency have negative relationships with the abundance of most tree taxa;only Pinus increases as fire frequency increases.The magnitude of individual fires does not have a significant impact on species abundance on this timescale.Both climate and fire regime characteristics must be considered to understand changes in forest composition on the decadal-to-centennial timescale.There are differences,both in sign and magnitude,in the response of individual tree species to individual drivers.
文摘Many products always operate under various complex environment conditions. To describe the dynamic influence of environment factors on their reliability, a method of reliability sensitivity analysis is proposed. In this method, the location parameter is assumed as a function of relevant environment variables while the scale parameter is assumed as an unknown positive constant. Then, the location parameter function is constructed by using the method of radial basis function. Using the varied environment test data, the log-likelihood function is transformed to a generalized linear expression by describing the indicator as Poisson variable. With the generalized linear model, the maximum likelihood estimations of the model coefficients are obtained. With the reliability model, the reliability sensitivity is obtained. An instance analysis shows that the method is feasible to analyze the dynamic variety characters of reliability along with environment factors and is straightforward for engineering application.
文摘in this paper, we describe a new method for assessing the degree in which the individual case influence the maximum likelihood estimation of link parameter in generalized linear model. Several influential statistics are illustrated with two examples.
