Seamounts are considered important sources of biodiversity and minerals. However, their biodiversity and health status are not well understood; therefore, potential conservation problems are unknown. The mesophotic reefs of the Vitória-Trindade Seamount Chain (VTC) were investigated via benthic community and fish surveys, metagenomic and water chemistry analyses, and water microbial abundance estimations. The VTC is a mosaic of reef systems and includes fleshy algae dominated rhodolith beds, crustose coralline algae (CCA) reefs, and turf algae dominated rocky reefs of varying health levels. Macro-carnivores and larger fish presented higher biomass at the CCA reefs (4.4 kg per frame) than in the rhodolith beds and rocky reefs (0.0 to 0.1 kg per frame). A larger number of metagenomic sequences identified as primary producers (e.g., Chlorophyta and Streptophyta) were found at the CCA reefs. However, the rocky reefs contained more diseased corals (>90%) than the CCA reefs (~40%) and rhodolith beds (~10%). Metagenomic analyses indicated a heterotrophic and fast-growing microbiome in rocky reef corals that may possibly lead to unhealthy conditions possibly enhanced by environmental features (e.g. light stress and high loads of labile dissolved organic carbon). VTC mounts represent important hotspots of biodiversity that deserve further conservation actions.

Scientific research is increasingly assisted by computer-based experiments. Such experiments are often composed of a vast number of loosely-coupled computational tasks that are specified and automated as scientific workflows. This large scale is also characteristic of the data that flows within such "manytask" computations (MTC). Provenance information can record the behavior of such computational experiments via the lineage of process and data artifacts. However, work to date has focused on data models, leaving unsolved issues of recording MTC behavior such as task runtimes and failures, or the impact of environment on performance and accuracy. In this work we contribute with MTCProv, a provenance query framework for many-task scientific computing that captures the runtime execution details of MTC workflow tasks on parallel and distributed systems, in addition to standard prospective and data derivation provenance. To help users query provenance data we provide a high level interface that hides relational query complexities. We evaluate MTCProv using applications in protein science and social network analysis, and describe how important query patterns such as correlations between provenance, runtime data, and scientific parameters are simplified and expressed.

The unprecedented size of the human population, along with its associated economic activities, has an ever‐increasing impact on global environments. Across the world, countries are concerned about the growing resource consumption and the capacity of ecosystems to provide resources. To effectively conserve biodiversity, it is essential to make indicators and knowledge openly available to decision‐makers in ways that they can effectively use them. The development and deployment of tools and techniques to generate these indicators require having access to trustworthy data from biological collections, field surveys and automated sensors, molecular data, and historic academic literature. The transformation of these raw data into synthesized information that is fit for use requires going through many refinement steps. The methodologies and techniques applied to manage and analyze these data constitute an area usually called biodiversity informatics. Biodiversity data follow a life cycle consisting of planning, collection, certification, description, preservation, discovery, integration, and analysis. Researchers, whether producers or consumers of biodiversity data, will likely perform activities related to at least one of these steps. This article explores each stage of the life cycle of biodiversity data, discussing its methodologies, tools, and challenges. This article is categorized under: Algorithmic Development > Biological Data Mining