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Abstract The availability of phylogenetic data has greatly expanded in recent years. As a result, a new era in phylogenetic analysis is dawning—one in which the methods we use to analyse and assess our data are the bottleneck to producing valuable phylogenetic hypotheses, rather than the need to acquire more data. This makes the ability to accurately appraise and evaluate new methods of phylogenetic analysis and phylogenetic artefact identification more important than ever. Incongruence in phylogenetic reconstructions based on different datasets may be due to two major sources: biological and methodological. Biological sources comprise processes like horizontal gene transfer, hybridization and incomplete lineage sorting, while methodological ones contain falsely assigned data or violations of the assumptions of the underlying model. While the former provides interesting insights into the evolutionary history of the investigated groups, the latter should be avoided or minimized as best as possible. However, errors introduced by methodology must first be excluded or minimized to be able to conclude that biological sources are the cause. Fortunately, a variety of useful tools exist to help detect such misassignments and model violations and to apply ameliorating measurements. Still, the number of methods and their theoretical underpinning can be overwhelming and opaque. Here, we present a practical and comprehensive review of recent developments in techniques to detect artefacts arising from model violations and poorly assigned data. The advantages and disadvantages of the different methods to detect such misleading signals in phylogenetic reconstructions are also discussed. As there is no one‐size‐fits‐all solution, this review can serve as a guide in choosing the most appropriate detection methods depending on both the actual dataset and the computational power available to the researcher. Ultimately, this informed selection will have a positive impact on the broader field, allowing us to better understand the evolutionary history of the group of interest.

Irrespective of the heuristic value of interpretations of developmental processes in terms of gene regulatory networks (GRNs), larger-angle views often suffer from: (i) an inadequate understanding of the relationship between genotype and phenotype; (ii) a predominantly zoocentric vision; and (iii) overconfidence in a putatively hierarchical organization of animal body plans. Here, we constructively criticize these assumptions. First, developmental biology is pervaded by adultocentrism, but development is not necessarily egg to adult. Second, during development, many unicells undergo transcriptomic profile transitions that are comparable to those recorded in pluricellular organisms; thus, their study should not be neglected from the GRN perspective. Third, the putatively hierarchical nature of the animal body is mirrored in the GRN logic, but in relating genotype to phenotype, independent assessments of the dynamics of the regulatory machinery and the animal’s architecture are required, better served by a combinatorial than by a hierarchical approach. The trade-offs between spatial and temporal aspects of regulation, as well as their evolutionary consequences, are also discussed. Multicellularity may derive from a unicell’s sequential phenotypes turned into different but coexisting, spatially arranged cell types. In turn, polyphenism may have been a crucial mechanism involved in the origin of complex life cycles.

In comparison to complex visual systems, non-directional photoreception – the most primitive form of biological photodetection – has been poorly investigated, although it is essential to many biological processes such as circadian and seasonal rhythms. Here we describe the spatiotemporal expression pattern of the major molecular actors mediating light reception – opsins – localized in the Strongylocentrotus purpuratus larva. In contrast to other zooplanktonic larvae, the echinopluteus lacks photoreceptor cells with observable shading pigments involved in directional visual tasks. Nonetheless, the echinopluteus expresses two distinct classes of opsins: a Go-opsin and a rhabdomeric opsin. The Go-opsin, Sp-opsin3.2, is detectable at early (3 days post fertilization) and four armed pluteus stages (4 days post fertilization) in two cells that flank the apical organ. To rule out the presence of shading pigments involved in directional photoreception, we used electron microscopy to explore the expression domain of Go-opsin Sp-opsin3.2 positive cells. The rhabdomeric opsin Sp-Opsin4 expression is detectable in clusters of cells located around the primary podia at the five-fold ectoderm pentagonal disc stage (day 18-21) and thereafter, thus indicating that Sp-Opsin4 may not be involved in the photoreception mechanism of the larva, but only of the juvenile. We discuss the putative function of the relevant cells in their neural context, and propose a model for understanding simple photodetection in marine larvae.