SeaWiFS Postlaunch Technical Report Series

This report documents the scientific activities on board the Royal Research Ship (RRS) James Clark Ross (JCR) during the fifth Atlantic Meridional Transect (AMT-5), 14 September to 17 October 1997. There are three objectives of the AMT Program. The first is to derive an improved understanding of the links between biogeochemical processes, biogenic gas exchange, air-sea interactions, and the effects on, and responses of, oceanic ecosystems to climate change. The second is to investigate the functional roles of biological particles and processes that influence ocean color in ecosystem dynamics. The Program relates directly to algorithm development and the validation of remotely-sensed observations of ocean color. Because the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) instrument achieved operational status during the cruise (on 18 September), AMT-5 was designated the SeaWiFS Atlantic Characterization Experiment (SeaACE) and was the only major research cruise involved in the validation of SeaWiFS data during the first 100 days of operations. The third objective involved the near-real time reporting of in situ light and pigment observations to the SeaWiFS Project, so the performance of the satellite sensor could be determined

The use of taxonomic relationships among species in applied ecological research: Baseline, steps forward and future challenges

Taxonomy is more than a mere exercise of nomenclature and classification of biological diversity: it profiles the identity of species by investigating their biological and ecological traits. Taxonomy is intimately related to ecology which, in turn, cannot be a mere exercise in describing ecological patterns, but instead requires deep knowledge of species’ biological structures, roles, interactions and functions. Thus, the study of taxonomic and phylogenetic relatedness of species is of paramount importance in ecological research, enabling insights into potential evolutionary patterns and processes, allowing a more comprehensive view of biodiversity, and providing opportunities to improve the assessment and monitoring of ecological changes in time and space. The work of K. Robert (‘Bob’) Clarke forged new pathways in this direction, providing new ideas and statistical tools to include and exploit taxonomic relationships in applied marine ecological studies and beyond, also inspiring the next generation of ecologists. In this short review, we synthesise the application and development of these tools and concepts in marine biodiversity research over the last three decades and suggest future pathways in this evolving field

Proteomic fingerprinting facilitates biodiversity assessments in understudied ecosystems: A case study on integrated taxonomy of deep sea copepods

Accurate and reliable biodiversity estimates of marine zooplankton are a prerequisite to understand how changes in diversity can affect whole ecosystems. Species identification in the deep sea is significantly impeded by high numbers of new species and decreasing numbers of taxonomic experts, hampering any assessment of biodiversity. We used in parallel morphological, genetic, and proteomic characteristics of specimens of calanoid copepods from the abyssal South Atlantic to test if proteomic fingerprinting can accelerate estimating biodiversity. We cross-validated the respective molecular discrimination methods with morphological identifications to establish COI and proteomic reference libraries, as they are a pre-requisite to assign taxonomic information to the identified molecular species clusters. Due to the high number of new species only 37% of the individuals could be assigned to species or genus level morphologically. COI sequencing was successful for 70% of the specimens analysed, while proteomic fingerprinting was successful for all specimens examined. Predicted species richness based on morphological and molecular methods was 42 morphospecies, 56 molecular operational taxonomic units (MOTUs) and 79 proteomic operational taxonomic units (POTUs), respectively. Species diversity was predicted based on proteomic profiles using hierarchical cluster analysis followed by application of the variance ratio criterion for identification of species clusters. It was comparable to species diversity calculated based on COI sequence distances. Less than 7% of specimens were misidentified by proteomic profiles when compared with COI derived MOTUs, indicating that unsupervised machine learning using solely proteomic data could be used for quickly assessing species diversity