On univariate optimal partitioning by complete enumeration.

Partitioning a set of elements into a given number of classes to find a globally optimal solution can be challenging due to the combinatorial explosion of the problem size. In the univariate case, where elements can be ordered, the number of partitions is significantly lower than in the multivariate case, and the problem is easier to handle. In this article, we focus on the univariate case and propose using complete enumeration to find a globally optimal solution. Although complete enumeration may also be computationally prohibitive as the number of elements and classes increases, it can be feasible in some situations. For such cases, we propose an algorithm that generates all contiguous partitions for a variable number of classes to be used with any objective function or set of constraints.•We compare exact problem sizes and approximate time complexities for multivariate and univariate partitioning.•We fill a technical gap in the literature by providing a valuable tool for researchers or engineers who need to exactly solve unusual univariate partitioning problems.•We use a convenient data structure for representing partitions of elements into classes and an iterative algorithm that simulates nested loops for any depth level, allowing for efficient generation of all possible contiguous partitions.

On evaluating the efficiency of the delta-lognormal mean estimator and predictor.

A variable taking positive values from a lognormal distribution and null values with a given probability is distributed according to the so-called delta-lognormal distribution. Two situations arise depending on whether the data are regarded as a random sample from an infinite population (superpopulation) or from a finite population, itself considered as a random sample from a superpopulation. In the case of an infinite population, estimating the mean can be accomplished using a uniformly minimum-variance unbiased estimator (UMVUE). Likewise, the prediction of the mean in the case of a finite population may be based on the UMVUE. In both cases, one expects a gain in precision when taking into account the shape of the distribution by relying on the UMVUE rather than on the sample mean, which is a nonparametric estimator (or predictor).1.For the infinite population case, the relative efficiency results presented in this article are more complete and more accurate than those published so far.2.The article fills a gap regarding the question of relative efficiency in the case of a finite population.3.Calculations were performed using the exact expression for the variance of the UMVUE of the mean, expressed in terms of the confluent hypergeometric limit function.

On the non-recursive implementation of multistage sampling without replacement.

Variance estimation in multistage sampling without replacement usually requires considerable computational effort. One option is to implement explicit formulas on a computer, at least for some specific sampling designs. This approach becomes quite cumbersome to handle beyond two stages, both from the formulation and computer implementation points of view. Another option is to provide a general method to compute variance estimates for any number of stages. Such an approach may involve data structures and estimators which are recursively defined. •The solution we present in this article is intended to be both general and computationally efficient by relying on a full-iterative implementation.•The definition of the estimators remains implicit as in the recursive approach, but is expressed in terms of recurrence relations translated into iterative algorithms.•These algorithms rely only on (dense) array data structures. Moreover, most of the necessary computer memory is only used during preliminary steps and is not required when performing the statistical calculations.

Attenuating the nonresponse bias in hunting bag surveys: The multiphase sampling strategy.

Reliable hunting bag statistics are a prerequisite for sustainable harvest management based on quantitative modeling. Estimating the total hunting bag for a given game species is faced with a multiplicity of error sources. Of particular concern is the nonresponse error. We consider that the major cause of nonresponse bias is when the reluctance to respond is related to a null harvest, which leads to a potentially important overestimation. For tackling the nonresponse bias issue, we advocate the repeated subsampling of nonrespondents, with a final phase of personal interview by phone, intended to be without nonresponse. When a 100% response rate is actually reached at the last phase, both total and sampling variance can be estimated without bias, whatever the response rates at the previous phases. The actual case of imperfect response at the last phase is studied using Monte Carlo simulations. For imperfect response at the last phase, we show that the estimators we advocate are biased downwards but that these bias remain very moderate if the response rate at the last phase is high enough, depending on the circumstances. Furthermore, we illustrate that increasing the number of phases improves the nonresponse bias attenuation. In case of a hunting bag collecting scheme prone to a high nonresponse rate, for obtaining a very satisfying nonresponse bias attenuation we advocate relying on the multiphase sampling strategy with two- or three-phases, and a response rate in the last phase of at least 90%.

Accounting for sampling error when inferring population synchrony from time-series data: a Bayesian state-space modelling approach with applications.

BACKGROUND: Data collected to inform time variations in natural population size are tainted by sampling error. Ignoring sampling error in population dynamics models induces bias in parameter estimators, e.g., density-dependence. In particular, when sampling errors are independent among populations, the classical estimator of the synchrony strength (zero-lag correlation) is biased downward. However, this bias is rarely taken into account in synchrony studies although it may lead to overemphasizing the role of intrinsic factors (e.g., dispersal) with respect to extrinsic factors (the Moran effect) in generating population synchrony as well as to underestimating the extinction risk of a metapopulation. METHODOLOGY/PRINCIPAL FINDINGS: The aim of this paper was first to illustrate the extent of the bias that can be encountered in empirical studies when sampling error is neglected. Second, we presented a space-state modelling approach that explicitly accounts for sampling error when quantifying population synchrony. Third, we exemplify our approach with datasets for which sampling variance (i) has been previously estimated, and (ii) has to be jointly estimated with population synchrony. Finally, we compared our results to those of a standard approach neglecting sampling variance. We showed that ignoring sampling variance can mask a synchrony pattern whatever its true value and that the common practice of averaging few replicates of population size estimates poorly performed at decreasing the bias of the classical estimator of the synchrony strength. CONCLUSION/SIGNIFICANCE: The state-space model used in this study provides a flexible way of accurately quantifying the strength of synchrony patterns from most population size data encountered in field studies, including over-dispersed count data. We provided a user-friendly R-program and a tutorial example to encourage further studies aiming at quantifying the strength of population synchrony to account for uncertainty in population size estimates.

Spread of avian influenza viruses by common teal (Anas crecca) in Europe.

Since the recent spread of highly pathogenic (HP) H5N1 subtypes, avian influenza virus (AIV) dispersal has become an increasing focus of research. As for any other bird-borne pathogen, dispersal of these viruses is related to local and migratory movements of their hosts. In this study, we investigated potential AIV spread by Common Teal (Anas crecca) from the Camargue area, in the South of France, across Europe. Based on bird-ring recoveries, local duck population sizes and prevalence of infection with these viruses, we built an individual-based spatially explicit model describing bird movements, both locally (between wintering areas) and at the flyway scale. We investigated the effects of viral excretion duration and inactivation rate in water by simulating AIV spread with varying values for these two parameters. The results indicate that an efficient AIV dispersal in space is possible only for excretion durations longer than 7 days. Virus inactivation rate in the environment appears as a key parameter in the model because it allows local persistence of AIV over several months, the interval between two migratory periods. Virus persistence in water thus represents an important component of contamination risk as ducks migrate along their flyway. Based on the present modelling exercise, we also argue that HP H5N1 AIV is unlikely to be efficiently spread by Common Teal dispersal only.