Bias in spatiotemporal index standardization models caused by unbalanced sampling designs and allocation schemes

Some environmental studies use non-probabilistic sampling designs to draw samples from spatially distributed populations. Unfortunately, these samples can be difficult to analyse statistically and can give biased estimates of population characteristics. Spatially balanced sampling designs are probabilistic designs that spread the sampling effort evenly over the resource. These designs are particularly useful for environmental sampling because they produce good-sample coverage over the resource, they have precise design-based estimators and they can potentially reduce the sampling cost. The most popular spatially balanced design is Generalized Random Tessellation Stratified (GRTS), which has many desirable features including a spatially balanced sample, design-based estimators and the ability to select spatially balanced oversamples. This article considers the popularity of spatially balanced sampling, reviews several spatially balanced sampling designs and shows how these designs can be implemented in the statistical programming language R. We hope to increase the visibility of spatially balanced sampling and encourage environmental scientists to use these designs.

In this article, the study and prediction of the state of fish resources in the Yesil River was carried out to determine the boundary reference points of the reserve and management decisions in case they are exceeded, to ensure sustainable fisheries and preserve biological diversity. Ichthyological studies, retrospective analyses and rankings of the effects of the water supply of the Yesil River on fish stocks have been carried out, critical values ​​of the biomass of fishing reserves have been determined, as criteria for biologically safe fishing values and targets for management of fish stocks at reaching the boundary values ​​of 24.03 tonnes have been established. According to the results of studies of basic biological indicators, it was determined that roach, bream, pike and perch populations each had an LC50 above LM50, which indicates their low utilization in the fishery industry. A strategy for management of fish stocks, recommendations for the conservation of fishery resources and their rational use has been developed as a result of these findings. The list and status of the monitored indicators for the careful management of fish stocks, as well as a list and indicators of the boundary indicators for the state of fish stocks, was established. These parameters included the fish size of 50% of the sampled fish and 50% of maturity achieved, where the LC50 was observed in the fish in question from 19 to 45.4 cm and LM50 from 12.3 to 41 cm, respectively. The minimum and maximum river levels that ranged from 111 up to 159 cm, as well as the critical value of the commercial reserve, corresponded to the boundary value of 24.03 tonnes. The results of this work will serve as the basis for making future managerial decisions, necessary for sustainable fisheries and conservation of the biological diversity of the Yesil River.

The scope for research in practical fishery management

Electrons provide means of analyzing metal surfaces: Machinery, Vol. 53. No. 5.

Sample design is studied to estimate the number of records from singing rooms. The definition of survey population, stratification, sample size and allocation of sample are discussed with using the collected real data. The validity of sample design is testified by the log data collected from online singing room which is sampled with suggested design. Estimation of the number of hitting of off-line singing room is proposed and calculated from the log data collected from sampled off-line singing rooms. The difference of online and off-line singing rooms is analyzed and the method of distribution of singing room royalty is suggested.

United Nations Conference on Straddling Fish Stocks and Highly Migratory Fish Stocks: Agreement for the Implementation of the Provisions of the United Nations Convention of the Law of the Sea of 10 December 1982, Relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks - Volume 34 Issue 6

Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 Relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks (A/Conf.164/37) and Final Act of the Un Conference on Straddling Fish Stocks and Highly Migratory Fish Stocks (A/Conf.164/38) (VII)

There are several reasons why robust regression techniques are useful tools in sampling design. First of all, when stratified samples are considered, one needs to deal with three main issues: the sample size, the strata bounds determination and the sample allocation in the strata. Since the target variable Y, the objective of the survey, is unknown, some auxiliary information X known for the entire population from which the sample is drawn, is used. Such information is helpful as it is typically strongly correlated with the target Y. However, some discrepancies between these variables may arise. The use of auxiliary information, combined with the choice of the appropriate statistical model to estimate the relationship between Y and X, is crucial for the determination of the strata bounds, the size of the sample and the sampling rates according to a chosen precision level for the estimates, as has been shown by Rivest (2002). Nevertheless, this regression-based approach is highly sensitive to the presence of contaminated data. Since the key tool for stratified sampling is the measure of scale of Y conditional on the knowledge of the auxiliary X, a robust approach based on the S-estimator of the regression is proposed in this paper. The aim is to allow for robust sample size and strata bounds determination, together with optimal sample allocation. Simulation results based on data from the Construction sector of a Structural Business Survey illustrate the advantages of the proposed method.

Chinook Salmon Oncorhynchus tshawytscha sport fisheries in the Columbia River basin are commonly monitored using roving creel survey designs and require precise, unbiased catch estimates. The objective of this study was to examine the relative bias and precision of total catch estimates using various sampling designs to estimate angling effort under the assumption that mean catch rate was known. We obtained information on angling populations based on direct visual observations of portions of Chinook Salmon fisheries in three Idaho river systems over a 23-d period. Based on the angling population, Monte Carlo simulations were used to evaluate the properties of effort and catch estimates for each sampling design. All sampling designs evaluated were relatively unbiased. Systematic random sampling (SYS) resulted in the most precise estimates. The SYS and simple random sampling designs had mean square error (MSE) estimates that were generally half of those observed with cluster sampling designs. The SYS design was more efficient (i.e., higher accuracy per unit cost) than a two-cluster design. Increasing the number of clusters available for sampling within a day decreased the MSE of estimates of daily angling effort, but the MSE of total catch estimates was variable depending on the fishery. The results of our simulations provide guidelines on the relative influence of sample sizes and sampling designs on parameters of interest in short-duration Chinook Salmon fisheries. Received February 5, 2013; accepted July 1, 2013

A spatially balanced sample can produce good estimates of finite population quantities when the study variable is dependent over a spatial region with respect to a super-population model. In many longitudinal surveys for monitoring natural resources, annual samples are taken in space to estimate both annual status and annual change. In this paper, we propose a spatiotemporal balanced sampling design with a repeated panel such that the sample for each year is spatially balanced, and the sample combined from consecutive years is also spatially balanced. We propose design-based regression estimators of the annual status and change, and the corresponding variance estimators are also derived. Simulation studies show that the spatial balance of a sample generated by the proposed spatiotemporal balanced sampling design is good, and design-based regression estimators work well. The proposed spatiotemporal balanced sampling design is tested on data from the National Resources Inventory rangeland on-site survey conducted in Texas from 2009 to 2013. For the study variable “average soil aggregate stability,” the proposed sampling design and estimators are shown to have better performance compared with the original sample and estimators. Although the spatiotemporal balanced sampling design is proposed in a two-dimensional space, it can be generalized to higher dimensions easily. Supplementary materials accompanying this paper appear online.

Summary A distinction is made between the uses of models in sample design and in survey analysis. Sampling practitioners regularly employ models to guide their choice of sample design, but seldom place complete reliance in a model (which would eliminate the need for probability sampling). With the large samples typical of most surveys, they are reluctant to use model-based estimators of descriptive parameters because of the bias resulting from any misspecifications of the model. With small samples, however, they may prefer a model-based estimator, accepting its unknown bias where its variance is much smaller than the design-based estimator; synthetic estimation for small areas illustrates this point. Models are essential for handling nonresponse and with the technique of statistical matching. The use of models for predicting sampling errors is noted. Causal analysis of survey data necessarily involves models; even so, in some circumstances design-based analysis may provide greater protection against model misspecifications than model-based analysis.

BackgroundWe developed a novel approach to minimize batch effects when assigning samples to batches. Our algorithm selects a batch allocation, among all possible ways of assigning samples to batches, that minimizes differences in average propensity score between batches. This strategy was compared to randomization and stratified randomization in a case–control study (30 per group) with a covariate (case vs control, represented as β1, set to be null) and two biologically relevant confounding variables (age, represented as β2, and hemoglobin A1c (HbA1c), represented as β3). Gene expression values were obtained from a publicly available dataset of expression data obtained from pancreas islet cells. Batch effects were simulated as twice the median biological variation across the gene expression dataset and were added to the publicly available dataset to simulate a batch effect condition. Bias was calculated as the absolute difference between observed betas under the batch allocation strategies and the true beta (no batch effects). Bias was also evaluated after adjustment for batch effects using ComBat as well as a linear regression model. In order to understand performance of our optimal allocation strategy under the alternative hypothesis, we also evaluated bias at a single gene associated with both age and HbA1c levels in the ‘true’ dataset (CAPN13 gene).ResultsPre-batch correction, under the null hypothesis (β1), maximum absolute bias and root mean square (RMS) of maximum absolute bias, were minimized using the optimal allocation strategy. Under the alternative hypothesis (β2 and β3 for the CAPN13 gene), maximum absolute bias and RMS of maximum absolute bias were also consistently lower using the optimal allocation strategy. ComBat and the regression batch adjustment methods performed well as the bias estimates moved towards the true values in all conditions under both the null and alternative hypotheses. Although the differences between methods were less pronounced following batch correction, estimates of bias (average and RMS) were consistently lower using the optimal allocation strategy under both the null and alternative hypotheses.ConclusionsOur algorithm provides an extremely flexible and effective method for assigning samples to batches by exploiting knowledge of covariates prior to sample allocation.

The a4a initiative aims to provide timely and cost effective advice for the circa. 250 fish stocks that,\nthrough the EU Data Collection Framework, will have at least 10 years of data by the year 2020. Current\nprocesses for assessing the state of and managing fish stocks are intensive processes, each stock\nrequiring the attention of one or more stock assessment scientist to produce preliminary catch advice,\nwhich is subsequently reviewed by one or two committees before the final catch advice is published.\nIngrained in the development of these processes has been the development of more and more complex\nstock assessment models which typically require highly skilled personnel to set up and run.

United Nations Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of December 10, 1982, Relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks of August 4, 1995

The International Law of the Sea, or simply Law of the Sea, is a body of legal norms that regulate the use of the seas and delineate the powers and jurisdiction of States over various parts of the seas. The evolution of the Law of the Sea can be divided into three different eras: the 17th-century great debate over open versus closed seas, era of codification, and era of institutionalization. The debate between early scholars over the issue of whether the sea was open to all and subject to the freedom of the seas (mare liberum or open seas) or whether the seas could be subject to sovereignty by States (mare clausum or closed seas) became the generally accepted basis for contemporary law of the sea. The era of codification saw the convening of three United Nations Conferences on the Law of the Sea—UNCLOS I, UNCLOS II, and UNCLOS III. The Law of the Sea Convention (LOSC), adopted in 1982, initiated an era of the institutionalization of the law of the sea. From early in the 21st century, the international community appears to be leaning toward closed seas, but there are also indications that cooperative arrangements among parties on the law of the sea will be more prevalent. An example of such initiative is the 1995 Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks.