Diversity patterns are determined by biogeographic, energetic, and anthropogenic factors, yet few studies have combined them into a large‐scale framework in order to decouple and compare their relative effects on fish faunas. Using an empirical dataset derived from 1527 underwater visual censuses (UVC) at 18 oceanic islands (five different marine provinces), we determined the relative influence of such factors on reef fish species richness, functional dispersion, density and biomass estimated from each UVC unit. Species richness presented low variation but was high at large island sites. High functional dispersion, density, and biomass were found at islands with large local species pool and distance from nearest reef. Primary productivity positively affected fish richness, density and biomass confirming that more productive areas support larger populations, and higher biomass and richness on oceanic islands. Islands densely populated by humans had lower fish species richness and biomass reflecting anthropogenic effects. Species richness, functional dispersion, and biomass were positively related to distance from the mainland. Overall, species richness and fish density were mainly influenced by biogeographical and energetic factors, whereas functional dispersion and biomass were strongly influenced by anthropogenic factors. Our results extend previous hypotheses for different assemblage metrics estimated from empirical data and confirm the negative impact of humans on fish assemblages, highlighting the need for conservation of oceanic islands.

Analyses of mitochondrial DNA and morphological variation were performed on specimens of all five currently recognised Syngnathus pipefish species from the eastern Pacific Ocean with type localities currently considered to lie within the Californian marine biogeographic province: kelp pipefish Syngnathus californiensis, bay pipefish S. leptorhynchus, barred pipefish S. auliscus, barcheek pipefish S. exilis and chocolate pipefish S. euchrous. Results consistently differentiate S. auliscus from the other species and fail to distinguish all other specimens as distinct species, as indicated by extensive morphological overlap as well as incomplete lineage sorting and considerably low genetic divergence for 16s and coI genes(<1%). This study presents a taxonomic revision of eastern Pacific Syngnathus spp. and proposes the synonymy of S. leptorhynchus, S. euchrous and S. exilis, under the senior synonym, S. californiensis. There is still a need to study populations of Syngnathus spp. from north and south of the Californian province to assess whether these too are synonyms of the two‐species recognised here.

Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long‐term species resilience. While contemporary genomic data can provide us with important insights into recent demographic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, utilize museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions and drivers, before highlighting overlooked areas where further temporal genomic studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.

A common way of illustrating phylogeographic results is through the use of haplotype networks. While these networks help to visualize relationships between individuals, populations, and species, evolutionary studies often only quantitatively analyze genetic diversity among haplotypes and ignore other network properties. Here, we present a new metric, haplotype network branch diversity (HBd), as an easy way to quantifiably compare haplotype network complexity. Our metric builds off the logic of combining genetic and topological diversity to estimate complexity previously used by the published metric haplotype network diversity (HNd). However, unlike HNd which uses a combination of network features to produce complexity values that cannot be defined in probabilistic terms, thereby obscuring the values’ implication for a sampled population, HBd uses frequencies of haplotype classes to incorporate topological information of networks, keeping the focus on the population and providing easy-to-interpret probabilistic values for randomly sampled individuals. The goal of this study is to introduce this more intuitive metric and provide an R script that allows researchers to calculate diversity and complexity indices from haplotype networks. A group of datasets, generated manually (model dataset) and based on published data (empirical dataset), were used to illustrate the behavior of HBd and both of its terms, haplotype diversity, and a new index called branch diversity. Results followed a predicted trend in both model and empirical datasets, from low metric values in simple networks to high values in complex networks. In short, the new combined metric joins genetic and topological diversity of haplotype networks, into a single complexity value. Based on our analysis, we recommend the use of HBd, as it makes direct comparisons of network complexity straightforward and provides probabilistic values that can readily discriminate situations that are difficult to resolve with available metrics.