Estimation of Population Mean on Recent Occasion under Non-Response in h-Occasion Successive Sampling

In this article, an attempt has been made to study on general estimation procedures of population mean on recent occasion when non-response occurs in h-occasion successive sampling. Suggested estimators have advantageously influenced the estimation procedures in the presence of non-response. Detailed properties of the suggested estimation procedures have been examined and compared with the estimation process of the same circumstances but in the absence of non-response. Empirical studies have been carried out to demonstrate the performances of the estimates and suitable recommendations have been made

Habitat fragmentation and anthropogenic factors affect wildcat Felis silvestris silvestris occupancy and detectability on Mt Etna

Knowledge of patterns of occupancy is crucial for planning sound biological management and for identifying areas which require paramount conservation attention. The European wildcat Felis silvestris is an elusive carnivore and is classified as ‘least concern’ on the IUCN red list, but with a decreasing population trend in some areas. Sicily hosts a peculiar wildcat population, which deserves conservation and management actions, due to its isolation from the mainland. Patterns of occupancy for wildcats are unknown in Italy, and especially in Sicily. We aimed to identify which ecological drivers determined wildcat occurrence on Mt Etna and to provide conservation actions to promote the wildcats’ long-term survival in this peculiar environment. The genetic identity of the wildcat population was confirmed through a scat-collection which detected 22 different wildcat individuals. We analysed wildcat detections collected by 91 cameras using an occupancy frame work to assess which covariates influenced the detection (p) and the occupancy (ψ) estimates. We recorded 70 detections of the target species from 38 cameras within 3377 trap-days. Wildcat detection was positively influenced by the distance to the major paved roads and negatively affected by the presence of humans. Wildcat occupancy was positively associated with mixed forest and negatively influenced by pine forest, fragmentation of mixed forest and altitude. A spatially explicit predicted occupancy map, validated using an independent dataset of wildcat presence records, showed that higher occupancy estimates were scattered, mainly located on the north face and at lower altitude. Habitat fragmentation has been claimed as a significant threat for the wildcat and this is the first study that has ascertained this as a limiting factor for wildcat occurrence. Conservation actions should promote interconnectivity between areas with high predicted wildcat occupancy while minimising the loss of habitat

Rcapture: Loglinear Models for Capture-Recapture in R

This article introduces Rcapture, an R package for capture-recapture experiments. The data for analysis consists of the frequencies of the observable capture histories over the t capture occasions of the experiment. A capture history is a vector of zeros and ones where one stands for a capture and zero for a miss. Rcapture can fit three types of models. With a closed population model, the goal of the analysis is to estimate the size N of the population which is assumed to be constant throughout the experiment. The estimator depends on the way in which the capture probabilities of the animals vary. Rcapture features several models for these capture probabilities that lead to different estimators for N. In an open population model, immigration and death occur between sampling periods. The estimation of survival rates is of primary interest. Rcapture can fit the basic Cormack-Jolly-Seber and Jolly-Seber model to such data. The third type of models fitted by Rcapture are robust design models. It features two levels of sampling; closed population models apply within primary periods and an open population model applies between periods. Most models in Rcapture have a loglinear form; they are fitted by carrying out a Poisson regression with the R function glm. Estimates of the demographic parameters of interest are derived from the loglinear parameter estimates; their variances are obtained by linearization. The novel feature of this package is the provision of several new options for modeling capture probabilities heterogeneity between animals in both closed population models and the primary periods of a robust design. It also implements many of the techniques developed by R. M. Cormack for open population models.

Calibration Estimation for Ratio Estimators in Stratified Sampling for Proportion Allocation

Calibration has established itself as an important methodological instrument in large scale production of statistics. In this paper, we propose calibration estimation for ratio estimator in stratified sampling and derive the estimator of the variance of the calibration estimation ratio estimator in stratified sampling in case proportion allocation

Estimating Mixed Logit Recreation Demand Models With Large Choice Sets

Discrete choice models are widely used in studies of recreation demand. They have proven valuable when modeling situations where decision makers face large choice sets and site substitution is important. However, when the choice set faced by the individual becomes very large (on the order of hundreds or thousands of alternatives), computational limitations make estimation with the full choice set intractable. Sampling of alternatives in a conditional logit framework is an effective method to limit computational burdens while still producing consistent estimates. This method is allowed by the existence of the independence of irrelevant alternatives (IIA) assumption. More advanced mixed logit models account for unobserved preference heterogeneity and overcome the behavioral limitations of the IIA assumption, however in doing so, prohibit sampling of alternatives. A method is developed where a latent class (finite mixture) model is estimated via the expectations-maximization algorithm and in doing so, allows consistent sampling of alternatives in a mixed logit model. The method is tested and applied to a recreational demand Wisconsin fishing survey.Sampling of alternatives, discrete choice, mixed logit, conditional logit, recreational demand, Wisconsin, fishing, microeconometrics, Environmental Economics and Policy, Research Methods/ Statistical Methods,

Sequential Regression Multiple Imputation for Incomplete Multivariate Data using Markov Chain Monte Carlo

This paper discusses the theoretical background to handling missing data in a multivariate context. Earlier methods for dealing with item non-response are reviewed, followed by an examination of some of the more modern methods and, in particular, multiple imputation. One such technique, known as sequential regression multivariate imputation, which employs a Markov chain Monte Carlo algorithm is described and implemented. It is demonstrated that distributional convergence is rapid and only a few imputations are necessary in order to produce accurate point estimates and preserve multivariate relationships, whilst adequately accounting for the uncertainty introduced by the imputation procedure. It is further shown that lower fractions of missing data and the inclusion of relevant covariates in the imputation model are desirable in terms of bias reduction.Missing data; Item non-response; Missingness mechanism; Imputation; Regression; Markov chain Monte Carlo.

Using Noninvasive Genetic Sampling to Assess and Monitor Grizzly Bear Population Status in the Northern Continental Divide Ecosystem

Wildlife managers need reliable estimates of population size, trend, and distribution to recover at–risk populations, yet obtaining these estimates is costly and often imprecise. The threatened grizzly bear (Ursus arctos) population in northwestern Montana has been managed for recovery since 1975, yet no rigorous data were available to evaluate the program’s success. We assessed population status using data from a large noninvasive genetic sampling project and 33–years of physical captures. Our abundance estimate, Nˆ= 765 (CV = 3.8%), was more than double the working estimate. Based on our results, the total known, human–caused mortality rate was 4.6%, slightly above the 4% level considered sustainable. Genetic diversity approached levels seen in relatively undisturbed populations, with the only signal of population fragmentation that aligned with landscape features being across U.S. Highway 2. I used these encounter data to parameterized a series of simulations to assess the ability of noninvasive genetic sampling, specifically surveys of naturally occurring bear rubs, to estimate population growth rates. I used data on 379 grizzly bears identified from bear rub surveys in a range of Pradel model simulations in program MARK. I evaluated model performance in terms of: (1) power to detect declines in population abundance, (2) precision and relative bias of estimates, and (3) sampling effort required to achieve 80% power to detect a decline within 10 years. Simulations suggest that annual bear rub surveys would exceed 80% power to detect a 3% annual decline within 6 years. Robust design models with 2 surveys per year provide precise and unbiased estimates of trend and abundance. Designs with 1 survey per year are less expensive but only yield trend and apparent survival estimates. I provide recommendations for designing a program to monitor population trends by sampling at bear rubs. Systematic bear rub surveys may provide a viable alternative to telemetry–based methods for monitoring trends in grizzly bear populations. This study illustrates the power of molecular techniques to rapidly assess population status and trends at landscape scales and provide detailed demographic and genetic data to guide and evaluate recovery efforts