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Sundarbans mangrove forest, the world’s largest continuous mangrove forests expanding across India and Bangladesh, in recent times, is immensely threatened by degradation stress due to natural stressors and anthropogenic disturbances. The degradation across the 19 mangrove forests in Indian Sundarbans was evaluated by eight environmental criteria typical to mangrove ecosystem. In an attempt to find competent predictors for mangrove ecosystem degradation, key eco-physiological resilience trait complex specific for mangroves from 4922 individuals for physiological analyses with gene expression and 603 individuals for leaf tissue distributions from 16 mangroves and 15 associate species was assessed along the degradation gradient. The degradation data was apparently categorized into four and CDFA discriminates 97% of the eco-physiological resilience data into corresponding four groups. Predictive Bayesian regression models and mixed effects models indicate osmolyte accumulation and thickness of water storage tissue as primary predictors of each of the degradation criteria that appraise the degradation status of mangrove ecosystem. RDA analyses well represented response variables of degradation explained by explanatory resilience variables. We hypothesize that with the help of our predictive models the policy makers could trace even the cryptic process of mangrove degradation and save the respective forests in time by proposing appropriate action plans.

Population size estimation based on a two-sample capture–recapture type experiment is an interesting problem in various fields including epidemiology, public health, population studies, etc. The Li...

Anthropogenic coastal activities and natural stressors aggravate degradation of small coastal patches of mangroves, which in turn destroy local resilience of mangrove forests in the Indian Sundarbans, the continuous mangrove habitat that spans between India and Bangladesh. We conducted an analytical survey across 19 shoreline mangrove fringes spanning the Sundarbans, including both healthy and disturbed forests, and evaluated ninety-five 60-cm composite sediment cores across a degradation and salinity gradient from ~ 4 to ~ 12 ppt. Increased salinity and anoxicity greatly inhibited nutrient cycling and release by microbial decomposers, subsequently resulting in nutrient-poor soil as a condition of degradation. Nutrient limitation, salinity rise, anoxicity increase, and sulfide build-up negatively controlled forest structure causing declines of forest coverage from ~ 98 to ~ 11%. In addition, the tide-dominated salinity gradient controlling species zonation was disrupted in disturbed forests with salinity-sensitive species gradually disappearing. An obvious change in species distribution is anticipated while salt-sensitive Heritiera fomes, Xylocarpus spp., and Phoenix paludosa failed to cope with increased salinity, evident by their absence from many forests. Excoecaria agallocha and Avicennia spp. acclimated well and expanded freely into degraded forests across the Sundarbans. Overall, our study strongly establishes salinity intrusion as primary mechanism for mangrove degradation.

Efficient estimation of population size from dependent dual-record system (DRS) remains a statistical challenge in capture-recapture type experiment. Owing to the nonidentifiability of the suitable Time-Behavioral Response Variation model (denoted as $M_{tb}$) under DRS, few methods are developed in Bayesian paradigm based on informative priors. Our contribution in this article is in developing integrated likelihood function from model $M_{tb}$ based on a novel approach developed by Severini (2007, Biometrika). Suitable weight function on nuisance parameter is derived under the assumption of availability of knowledge on the direction of behavioral dependency. Such pseudo-likelihood function is constructed so that the resulting estimator possess some desirable properties including invariance and negligible prior (or weight) sensitiveness. Extensive simulations explore the better performance of our proposed method in most of the situations than the existing Bayesian methods. Moreover, being a non-Bayesian estimator, it simply avoids heavy computational effort and time. Finally, illustration based on two real life data sets on epidemiology and economic census are presented.

In this paper an estimator of the finite population mean using auxiliary information in sample surveys has been proposed. The bias and mean squared error are obtained under large sample approximation. It has been shown that the proposed estimator performs better than some recently published estimators.

An improved integrated likelihood is formulated with suitably constructed weight function following Severini (2007) to estimate population size in Dual-record System. The resulting likelihood has several desirable properties. A comparative ordering is established among the relevant estimates. Empirical results demonstrating efficiency and usefulness are reported.

Summary Population size estimation based on a capture–recapture experiment under a triple-record system is an interesting problem in various fields including epidemiology and population studies. In many real life scenarios, there is inherent dependence between capture and recapture attempts. We propose a novel model that successfully incorporates the possible dependence and the associated parameters have nice interpretations. We provide estimation methodology for the population size and the associated model parameters based on the maximum likelihood method. The model proposed is applied to analyse real data sets from public health and census coverage evaluation studies. The performance of the estimate proposed is evaluated through extensive simulation study and the results are compared with existing competitors. The results exhibit superiority of the model over the existing competitors both in real data analysis and in a simulation study.

Every large census operation should undergo evaluation programs to find the sources and extent of inherent coverage errors. In this article, we briefly discuss the statistical methodology to estimate the omission rate in Indian census using dual-system estimation (DSE) technique. We have explicitly studied the correlation bias factor involved in the estimate, its extent, and consequences. A new potential source of bias in the estimate is identified and discussed. During the survey, more efficient enumerators compared to the census operations are appointed, and this fact may inflate the dependency between two lists and lead to a significant bias. Some examples are given to demonstrate this argument in various plausible situations. We have suggested one simple and flexible approach which can control this bias. Our proposed estimator can efficiently overcome the potential bias by achieving the desired degree of accuracy (almost unbiased) with relatively higher efficiency. Overall improvements in the ...

Dual-record system (DRS) (equivalently two sample Capture-recapture experiment) model with time and behavioral response variation, has attracted much attention specifically in the domain of Official Statistics and Epidemiology. The relevant model suffers from parameter identifiability problem and proper Bayesian methodologies could be helpful to overcome the situation. In this article, we have formulated the population size estimation problem in DRS as a missing data analysis under both the known and unknown directional nature of underlying behavioral response effect. Two simple empirical Bayes approaches are proposed and investigated their performances for this complex model along with a fully Bayes treatment. Extensive simulation studies are carried out to compare the performances of these competitive approaches and a real data example is also illustrated. Finally, some features of these methods and recommendations to implement them in practice are explored depending upon the availability of knowledge on the nature of behavioral response effect., 25 pages, 3 figures