Spatially balanced sampling designs for environmental surveys

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.

Using population screening for recruitment of young adults engaged in illicit drug use: methodological issues and sampling outcomes

Social stigma, legal sanctions and the associated lack of sampling frames create barriers to the probabilistic sampling of those engaged in a variety of behaviour, including illicit drug use. We used a novel sampling approach to recruit respondents into a longitudinal study examining amphetamine-type stimulant use. A young adult population was screened for lifetime drug use to create a sampling frame of amphetamine-type stimulant users and non-users. We posted 12,118 screening questionnaires to a random selection of young adults listed on the electoral roll for Brisbane and the Gold Coast, Australia (N=107,275). Using a small pre-paid incentive and intensive telephone and postal reminders we attained a screening response rate of 49.9%. Eligible amphetamine-type stimulant users (used ecstasy or methamphetamine⩾3 times in past 12 months) and non-users (never used ecstasy or methamphetamine) were identified by screening responses. About two-thirds of each selected group took part in the longitudinal study. Comparisons with large-scale population survey data suggest the sample was broadly representative of young adult amphetamine-type stimulant users in Australia.

Probabilistically sampled and spectrally clustered plant species using phenotypic characteristics

Phenotypic characteristics of a plant species refers to its physical properties as cataloged by plant biologists at different research centers around the world. Clustering species based upon their phenotypic characteristics is used to obtain diverse sets of parents that are useful in their breeding programs. The Hierarchical Clustering (HC) algorithm is the current standard in clustering of phenotypic data. This algorithm suffers from low accuracy and high computational complexity issues. To address the accuracy challenge, we propose the use of Spectral Clustering (SC) algorithm. To make the algorithm computationally cheap, we propose using sampling, specifically, Pivotal Sampling that is probability based. Since application of samplings to phenotypic data has not been explored much, for effective comparison, another sampling technique called Vector Quantization (VQ) is adapted for this data as well. VQ has recently generated promising results for genotypic data. The novelty of our SC with Pivotal Sampling algorithm is in constructing the crucial similarity matrix for the clustering algorithm and defining probabilities for the sampling technique. Although our algorithm can be applied to any plant species, we tested it on the phenotypic data obtained from about 2,400 Soybean species. SC with Pivotal Sampling achieves substantially more accuracy (in terms of Silhouette Values) than all the other proposed competitive clustering with sampling algorithms (i.e. SC with VQ, HC with Pivotal Sampling, and HC with VQ). The complexities of our SC with Pivotal Sampling algorithm and these three variants are almost the same because of the involved sampling. In addition to this, SC with Pivotal Sampling outperforms the standard HC algorithm in both accuracy and computational complexity. We experimentally show that we are up to 45% more accurate than HC in terms of clustering accuracy. The computational complexity of our algorithm is more than a magnitude less than that of HC.

Sampling particulate matter for analysis - Controlling uncertainty and bias using the theory of sampling

Sampling particulate matter for measuring the content of an analyte is a routine operation in many fields of engineering and science. However, sampling can lead to important bias and variance in concentration estimation because of sampling errors stemming from particulate matter heterogeneity. The goal of this study was to quantify bias, reproducibility and the degree of representativeness of a probabilistic sampling (PS) technique following principles from the Theory of sampling (TOS) and grab sampling (GS). PS was designed to control sampling errors, while GS did not exert any control over them. GS also included a step of sieve screening, which is common during sampling in some fields (e.g. soil sampling). Both techniques were used with two different analytes, namely steel microspheres and copper sulfate, at two different concentrations, in order to assess sampling errors and sampling performance. The sampling method had the most significant effect on sampling bias and relative variance, and therefore, on the degree of sampling representativeness. The most important result is the demonstration that probabilistic sampling improves the degree of representativeness of concentrations measurements by more than two orders of magnitudes by significantly decreasing bias and relative variance. The lot containing the physical analyte lead to larger bias and relative variance compared to the lot containing the chemical analyte. GS resulted in largely biased results and a poor degree of representativeness. The results have also highlighted a significant problem associated with the screening of larger particles as performed in GS. This alteration of the primary sample decreased the variability of the resulting concentration measurements, but it also biased them significantly.

Long-term monitoring of plant diversity data in the "Montagna di Torricchio" Strict Nature Reserve, Italy

Long-term monitoring is pivotal for the collection of data capable of unravelling spatio-temporal changes in plant diversity. Here we present a dataset including plant presence and abundance data collected using a probabilistic sampling design in the "Montagna di Torricchio" Strict Nature Reserve, central Apennines, Italy. Five surveys were conducted in 35 plots during a period spanning 22 years (2002-2024). This dataset allows for the study of plant diversity changes over space and time across different habitat types by using statistical inference based on solid sampling theory.

Natural averaging may complement known biological constraints in sexual reproduction's advantages over asexual in conserving species quantitative traits

Commonly recognized effects of sexual reproduction include increased diversity, improved adaptability, enabling of DNA repair, constrained accumulation of deleterious mutations, and species genotype homogenization. Additionally, there are studies that show how sexual reproduction slows down certain evolutionary responses, offering advantages in population cumulative growth and stability over time and other metrics. Here, we contribute an observation of another distinct effect of sexual reproduction, focusing on retaining a species's key traits. In an initial mathematical analysis and simulation, we show that in an environment where copying is prone to error, quantitative polygenic traits that are shared within a parents' generation are transmitted to future generations under sexual reproduction with less deviation than under asexual reproduction. Furthermore, the model shows that this retention of common traits (abbr. RoCT), is driven by the very nature of mixing of parental traits, and occurs even before adding effects like trait-specific reproductive advantages, DNA repair, or the raising of reproductive barriers. Since survival of ecosystems depends on the ability of individuals to replace the networked interactions and interdependencies associated with failing, dying, or absent members of the same species, RoCT helps sustain species and ecosystems.