Sea whip coral Leptogorgia virgulata in the Mid-Atlantic Bight: Colony complexity, age, and growth

The Octocoral Trait Database: a global database of trait information for octocoral species

Trait-based approaches are revolutionizing our understanding of high-diversity ecosystems by providing insights into the principles underlying key ecological processes, such as community assembly, species distribution, resilience, and the relationship between biodiversity and ecosystem functioning. In 2016, the Coral Trait Database advanced coral reef science by centralizing trait information for stony corals (i.e., Subphylum Anthozoa, Class Hexacorallia, Order Scleractinia). However, the absence of trait data for soft corals, gorgonians, and sea pens (i.e., Class Octocorallia) limits our understanding of ecosystems where these organisms are significant members and play pivotal roles. To address this gap, we introduce the Octocoral Trait Database, a global, open-source database of curated trait data for octocorals. This database houses species- and individual-level data, complemented by contextual information that provides a relevant framework for analyses. The inaugural dataset, OctocoralTraits v2.2, contains over 97,500 global trait observations across 98 traits and over 3,500 species. The database aims to evolve into a steadily growing, community-led resource that advances future marine science, with a particular emphasis on coral reef research.

Automating microsatellite screening and primer design from multi-individual libraries using Micro-Primers

Analysis of intra- and inter-population diversity has become important for defining the genetic status and distribution patterns of a species and a powerful tool for conservation programs, as high levels of inbreeding could lead into whole population extinction in few generations. Microsatellites (SSR) are commonly used in population studies but discovering highly variable regions across species' genomes requires demanding computation and laboratorial optimization. In this work, we combine next generation sequencing (NGS) with automatic computing to develop a genomic-oriented tool for characterizing SSRs at the population level. Herein, we describe a new Python pipeline, named Micro-Primers, designed to identify, and design PCR primers for amplification of SSR loci from a multi-individual microsatellite library. By combining commonly used programs for data cleaning and microsatellite mining, this pipeline easily generates, from a fastq file produced by high-throughput sequencing, standard information about the selected microsatellite loci, including the number of alleles in the population subset, and the melting temperature and respective PCR product of each primer set. Additionally, potential polymorphic loci can be identified based on the allele ranges observed in the population, to easily guide the selection of optimal markers for the species. Experimental results show that Micro-Primers significantly reduces processing time in comparison to manual analysis while keeping the same quality of the results. The elapsed times at each step can be longer depending on the number of sequences to analyze and, if not assisted, the selection of polymorphic loci from multiple individuals can represent a major bottleneck in population studies.