Introduction to the study of meiofauna

TL;DR: Overall spatial and temporal patterns of species diversity and major changes and threats were assessed, and temporal trends indicated that overexploitation and habitat loss have been the main human drivers of historical changes in biodiversity.

TL;DR: This study provides scientific evidence that the conservation of deep-sea biodiversity is a priority for a sustainable functioning of the worlds' oceans and suggests that mutually positive functional interactions can be common in the largest biome of the authors' biosphere.

TL;DR: A series of cladistic analyses assesses the status and membership of the taxon Polychaeta and provides a new classification, which does not use Linnaean categories, and comprises two clades, the Scolecida and Palpata.

TL;DR: Most contaminants in estuaries reside in sediments, and meiofauna are intimately associated with sediments over their entire life-cycle, thus they are increasingly being used as pollution sentinels.

TL;DR: In contrast to Shaw et al. (1983) I believe the lognormal distribution of individuals among species is an adequate description of most benthic communities, given that no community should be expected to be in complete equilibrium and give a perfect fit.

TL;DR: Seven years (1970-1976) of density data on three harpacticoid copepods were analysed by Maximum Entropy spectral Analysis, indicating population regulation is also different.

TL;DR: It is suggested that pattern analysis, in addition to its usual function of simplification of complex data, may contribute to the analysis of grazing experiments in the special case in which there is reason to suspect the existence of an external non-random environmental factor.

Abstract: SUMMARY This paper is concerned with the representation of a multivariate sample of size n as points P1, P2, ..., PI in a Euclidean space. The interpretation of the distance A(Pi, Pj) between the ith andjth members of the sample is discussed for some commonly used types of analysis, including both Q and R techniques. When all the distances between n points are known a method is derived which finds their co-ordinates referred to principal axes. A set of necessary and sufficient conditions for a solution to exist in real Euclidean space is found. Q and R techniques are defined as being dual to one another when they both lead to a set of n points with the same inter-point distances. Pairs of dual techniques are derived. In factor analysis the distances between points whose co-ordinates are the estimated factor scores can be interpreted as D2 with a singular dispersion matrix.