Mesoplankton is a key element of pelagic communities representing the largest biome on the planet. Many concepts in marine and freshwater biology are based on quantitative estimates of mesoplankton abundance, whereas precision of mesoplankton sampling remains underexplored and may depend on various factors. We analyzed ten contiguous daytime epipelagic samples in the Black Sea and 13 nighttime mesopelagic samples in the South Atlantic. We used a relative error as a measure of the sampling precision and ran a set of Generalized Linear Mixed Models (GLMMs) to estimate effects of six possible factors: abundance, size, diel migration, movement speed, taxonomic group, and net type. Abundance of taxa was the most powerful factor affecting sampling precision (positive effect) followed by the net type (BR provided better precision than Judey net) and taxonomic group. Conversely, size, movement speed, and diel migrations did not significantly influence sampling precision in all sample sets. We conclude that abundance and biomass of dominant species may be estimated with a satisfactory accuracy (relative error <20% of assessed values), which suggests that recent conceptions based on total mesoplankton abundance and biomass (contributed mainly by dominant taxa) are not greatly biased. Quantitative zooplankton structure and biodiversity assessed on the basis of non-transformed matrices are likely more relevant than those based on the root-transformed or presence/absence data.

Phylogeographic inference of the dispersal history of viral lineages offers key opportunities to tackle epidemiological questions about the spread of fast-evolving pathogens across human, animal and plant populations. In continuous space, i.e. when locations are specified by longitude and latitude, these reconstructions are however often limited by the availability or accessibility of precise sampling locations required for such spatially explicit analyses. We here review the different approaches that can be considered when genomic sequences are associated with a geographic area of sampling instead of precise coordinates. In particular, we describe and compare the approaches to define homogeneous and heterogeneous prior ranges of sampling coordinates.

Two recent publications in Educational and Psychological Measurement advocated that researchers consider using the a priori procedure. According to this procedure, the researcher specifies, prior to data collection, how close she wishes her sample mean(s) to be to the corresponding population mean(s), and the desired probability of being that close. A priori equations provide the necessary sample size to meet specifications under the normal distribution. Or, if sample size is taken as given, a priori equations provide the precision with which estimates of distribution means can be made. However, there is currently no way to perform these calculations under the more general family of skew-normal distributions. The present research provides the necessary equations. In addition, we show how skewness can increase the precision with which locations of distributions can be estimated. This conclusion, based on the perspective of improving sampling precision, contrasts with a typical argument in favor of performing transformations to normalize skewed data for the sake of performing more efficient significance tests.

After obtaining a sample of published, peer-reviewed articles from journals with high and low impact factors in social, cognitive, neuro-, developmental, and clinical psychology, we used a priori equations recently derived by Trafimow (Educational and Psychological Measurement, 77, 831-854, 2017; Trafimow & MacDonald in Educational and Psychological Measurement, 77, 204-219, 2017) to compute the articles\' median levels of precision. Our findings indicate that developmental research performs best with respect to precision, whereas cognitive research performs the worst; however, none of the psychology subfields excelled. In addition, we found important differences in precision between journals in the upper versus lower echelons with respect to impact factors in cognitive, neuro-, and clinical psychology, whereas the difference was dramatically attenuated for social and developmental psychology. Implications are discussed.