Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonl...Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly-Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.展开更多
Bangladesh is a subtropical monsoon climate characterized by wide seasonal variations in rainfall, moderately warm temperatures, and high humidity. Rainfall is the main source of irrigation water everywhere in the Ban...Bangladesh is a subtropical monsoon climate characterized by wide seasonal variations in rainfall, moderately warm temperatures, and high humidity. Rainfall is the main source of irrigation water everywhere in the Bangladesh where the inhabitants derive their income primarily from farming. Stochastic rainfall models were concerned with the occurrence of wet day and depth of rainfall for different regions to model the daily occurrence of rainfall and achieved satisfactory results around the world. In connection to the Markov chain of different order, logistic regression is conducted to visualize the dependence of current rainfall upon the rainfall of previous two-time period. It had been shown that wet day of the previous two time period compared to the dry day of previous two time period influences positively the wet day of current time period, that is the dependency of dry-wet spell for the occurrence of rain in the rainy season from April to September in the study area. Daily data are collected from meteorological department of about 26 years on rainfall of Dhaka station during the period January 1985-August 2011 to conduct the study. The test result shows that the occurrence of rainfall follows a second order Markov chain and logistic regression also tells that dry followed by dry and wet followed by wet is more likely for the rainfall of Dhaka station and also the model could perform adequately for many applications of rainfall data satisfactorily.展开更多
Aims More data are needed about how genetic variation(GV)and envi-ronmental factors influence phenotypic variation within the natural populations of long-lived species with broad geographic distribu-tions.To fill this...Aims More data are needed about how genetic variation(GV)and envi-ronmental factors influence phenotypic variation within the natural populations of long-lived species with broad geographic distribu-tions.To fill this gap,we examined the correlations among envi-ronmental factors and phenotypic variation within and among 13 natural populations of Pinus tabulaeformis consisting of four demo-graphically distinct groups within the entire distributional range.Methods Using the Akaike’s information Criterion(AiC)model,we measured 12 morphological traits and constructed alternative candidate models for the relationships between each morphological trait and key climatic variables and genetic groups.We then compared the AiC weight for each candidate model to identify the best approximating model for ecogeographical variation of P.tabulaeformis.The partitioning of vari-ance was assessed subsequently by evaluating the independent vari-ables of the selected best models using partial redundancy analysis.Important Findings Significant phenotypic variation of the morphological traits was observed both within individual populations and among populations.Variation partition analyses showed that most of the phenotypic variation was co-determined by both GV and climatic factors.GV accounted for the largest proportion of reproductive trait variation,whereas local key climatic factors(i.e.actual evapotranspiration,AET)accounted for the largest proportion of phenotypic variation in the remaining investigated traits.Our results indicate that both genetic divergence and key environmental factors affect the phenotypic variation observed among populations of this species,and that reproductive and vegetative traits adaptively respond differently with respect to local environmental conditions.This partitioning of factors can inform those making predictions about phenotypic variation in response to future changes in climatic conditions(particularly those affecting AET).展开更多
文摘Mark-recapture models are extensively used in quantitative population ecology, providing estimates of population vital rates, such as survival, that are difficult to obtain using other methods. Vital rates are commonly modeled as functions of explanatory covariates, adding considerable flexibility to mark-recapture models, but also increasing the subjectivity and complexity of the modeling process. Consequently, model selection and the evaluation of covariate structure remain critical aspects of mark-recapture modeling. The difficulties involved in model selection are compounded in Cormack-Jolly-Seber models because they are composed of separate sub-models for survival and recapture probabilities, which are conceptualized independently even though their parameters are not statistically independent. The construction of models as combinations of sub-models, together with multiple potential covariates, can lead to a large model set. Although desirable, estimation of the parameters of all models may not be feasible. Strategies to search a model space and base inference on a subset of all models exist and enjoy widespread use. However, even though the methods used to search a model space can be expected to influence parameter estimation, the assessment of covariate importance, and therefore the ecological interpretation of the modeling results, the performance of these strategies has received limited investigation. We present a new strategy for searching the space of a candidate set of Cormack-Jolly-Seber models and explore its performance relative to existing strategies using computer simulation. The new strategy provides an improved assessment of the importance of covariates and covariate combinations used to model survival and recapture probabilities, while requiring only a modest increase in the number of models on which inference is based in comparison to existing techniques.
文摘Bangladesh is a subtropical monsoon climate characterized by wide seasonal variations in rainfall, moderately warm temperatures, and high humidity. Rainfall is the main source of irrigation water everywhere in the Bangladesh where the inhabitants derive their income primarily from farming. Stochastic rainfall models were concerned with the occurrence of wet day and depth of rainfall for different regions to model the daily occurrence of rainfall and achieved satisfactory results around the world. In connection to the Markov chain of different order, logistic regression is conducted to visualize the dependence of current rainfall upon the rainfall of previous two-time period. It had been shown that wet day of the previous two time period compared to the dry day of previous two time period influences positively the wet day of current time period, that is the dependency of dry-wet spell for the occurrence of rain in the rainy season from April to September in the study area. Daily data are collected from meteorological department of about 26 years on rainfall of Dhaka station during the period January 1985-August 2011 to conduct the study. The test result shows that the occurrence of rainfall follows a second order Markov chain and logistic regression also tells that dry followed by dry and wet followed by wet is more likely for the rainfall of Dhaka station and also the model could perform adequately for many applications of rainfall data satisfactorily.
基金Program from Chinese National Basic Research Program(2014CB954203)grants from the National Natural Science Foundation of China(31322010,31270753,31000286)the National Youth Top-notch Talent Support Program to J.D.and Fundamental Research Funds for Central Universities(lzujbky-2012-k23).
文摘Aims More data are needed about how genetic variation(GV)and envi-ronmental factors influence phenotypic variation within the natural populations of long-lived species with broad geographic distribu-tions.To fill this gap,we examined the correlations among envi-ronmental factors and phenotypic variation within and among 13 natural populations of Pinus tabulaeformis consisting of four demo-graphically distinct groups within the entire distributional range.Methods Using the Akaike’s information Criterion(AiC)model,we measured 12 morphological traits and constructed alternative candidate models for the relationships between each morphological trait and key climatic variables and genetic groups.We then compared the AiC weight for each candidate model to identify the best approximating model for ecogeographical variation of P.tabulaeformis.The partitioning of vari-ance was assessed subsequently by evaluating the independent vari-ables of the selected best models using partial redundancy analysis.Important Findings Significant phenotypic variation of the morphological traits was observed both within individual populations and among populations.Variation partition analyses showed that most of the phenotypic variation was co-determined by both GV and climatic factors.GV accounted for the largest proportion of reproductive trait variation,whereas local key climatic factors(i.e.actual evapotranspiration,AET)accounted for the largest proportion of phenotypic variation in the remaining investigated traits.Our results indicate that both genetic divergence and key environmental factors affect the phenotypic variation observed among populations of this species,and that reproductive and vegetative traits adaptively respond differently with respect to local environmental conditions.This partitioning of factors can inform those making predictions about phenotypic variation in response to future changes in climatic conditions(particularly those affecting AET).
