Using deep-sea images to examine ecosystem services associated with methane seeps.

Deep-sea images are routinely collected during at-sea expeditions and represent a repository of under-utilized knowledge. We leveraged dive videos collected by the remotely-operated vehicle Hercules (deployed from E/V Nautilus, operated by the Ocean Exploration Trust), and adapted biological trait analysis, to develop an approach that characterizes ecosystem services. Specifically, fisheries and climate-regulating services related to carbon are assessed for three southern California methane seeps: Point Dume (∼725 m), Palos Verdes (∼506 m), and Del Mar (∼1023 m). Our results enable qualitative intra-site comparisons that suggest seep activity influences ecosystem services differentially among sites, and site-to-site comparisons that suggest the Del Mar site provides the highest relative contributions to fisheries and carbon services. This study represents a first step towards ecosystem services characterization and quantification using deep-sea images. The results presented herein are foundational, and continued development should help guide research and management priorities by identifying potential sources of ecosystem services.

Scientific and budgetary trade-offs between morphological and molecular methods for deep-sea biodiversity assessment.

Deep-sea biodiversity, a source of critical ecological functions and ecosystem services, is increasingly subject to the threat of disturbance from existing practices (e.g., fishing, waste disposal, oil and gas extraction) as well as emerging industries such as deep-seabed mining. Current scientific tools may not be adequate for monitoring and assessing subsequent changes to biodiversity. In this paper, we evaluate the scientific and budgetary trade-offs associated with morphology-based taxonomy and metabarcoding approaches to biodiversity surveys in the context of nascent deep-seabed mining for polymetallic nodules in the Clarion-Clipperton Zone, the area of most intense interest. For the dominant taxa of benthic meiofauna, we discuss the types of information produced by these methods and use cost-effectiveness analysis to compare their abilities to yield biological and ecological data for use in environmental assessment and management. On the basis of our evaluation, morphology-based taxonomy is less cost-effective than metabarcoding but offers scientific advantages, such as the generation of density, biomass, and size structure data. Approaches that combine the two methods during the environmental assessment phase of commercial activities may facilitate future biodiversity monitoring and assessment for deep-seabed mining and for other activities in remote deep-sea habitats, for which taxonomic data and expertise are limited. Integr Environ Assess Manag 2022;18:655-663. © 2021 SETAC.

Climate change considerations are fundamental to management of deep-sea resource extraction.

Climate change manifestation in the ocean, through warming, oxygen loss, increasing acidification, and changing particulate organic carbon flux (one metric of altered food supply), is projected to affect most deep-ocean ecosystems concomitantly with increasing direct human disturbance. Climate drivers will alter deep-sea biodiversity and associated ecosystem services, and may interact with disturbance from resource extraction activities or even climate geoengineering. We suggest that to ensure the effective management of increasing use of the deep ocean (e.g., for bottom fishing, oil and gas extraction, and deep-seabed mining), environmental management and developing regulations must consider climate change. Strategic planning, impact assessment and monitoring, spatial management, application of the precautionary approach, and full-cost accounting of extraction activities should embrace climate consciousness. Coupled climate and biological modeling approaches applied in the water and on the seafloor can help accomplish this goal. For example, Earth-System Model projections of climate-change parameters at the seafloor reveal heterogeneity in projected climate hazard and time of emergence (beyond natural variability) in regions targeted for deep-seabed mining. Models that combine climate-induced changes in ocean circulation with particle tracking predict altered transport of early life stages (larvae) under climate change. Habitat suitability models can help assess the consequences of altered larval dispersal, predict climate refugia, and identify vulnerable regions for multiple species under climate change. Engaging the deep observing community can support the necessary data provisioning to mainstream climate into the development of environmental management plans. To illustrate this approach, we focus on deep-seabed mining and the International Seabed Authority, whose mandates include regulation of all mineral-related activities in international waters and protecting the marine environment from the harmful effects of mining. However, achieving deep-ocean sustainability under the UN Sustainable Development Goals will require integration of climate consideration across all policy sectors.

Eliminating primer dimers and improving SNP detection using self-avoiding molecular recognition systems.

Despite its widespread value to molecular biology, the polymerase chain reaction (PCR) encounters modes that unproductively consume PCR resources and prevent clean signals, especially when high sensitivity, high SNP discrimination, and high multiplexing are sought. Here, we show how "self-avoiding molecular recognition systems" (SAMRS) manage such difficulties. SAMRS nucleobases pair with complementary nucleotides with strengths comparable to the A:T pair, but do not pair with other SAMRS nucleobases. This should allow primers holding SAMRS components to avoid primer-primer interactions, preventing primer dimers, allowing more sensitive SNP detection, and supporting higher levels of multiplex PCR. The experiments here examine the PCR performances of primers containing different numbers of SAMRS components placed strategically at different positions, and put these performances in the context of estimates of SAMRS:standard pairing strengths. The impact of these variables on primer dimer formation, the overall efficiency and sensitivity of SAMRS-based PCR, and the value of SAMRS primers when detecting single nucleotide polymorphisms (SNPs) are also evaluated. With appropriately chosen polymerases, SNP discrimination can be greater than the conventional allele-specific PCR, with the further benefit of avoiding primer dimer artifacts. General rules guiding the design of SAMRS-modified primers are offered to support medical research and clinical diagnostics products.

Nucleoside analogs to manage sequence divergence in nucleic acid amplification and SNP detection.

Described here are the synthesis, enzymology and some applications of a purine nucleoside analog (H) designed to have two tautomeric forms, one complementary to thymidine (T), the other complementary to cytidine (C). The performance of H is compared by various metrics to performances of other 'biversal' analogs that similarly rely on tautomerism to complement both pyrimidines. These include (i) the thermodynamic stability of duplexes that pair these biversals with various standard nucleotides, (ii) the ability of the biversals to support polymerase chain reaction (PCR), (iii) the ability of primers containing biversals to equally amplify targets having polymorphisms in the primer binding site, and (iv) the ability of ligation-based assays to exploit the biversals to detect medically relevant single nucleotide polymorphisms (SNPs) in sequences flanked by medically irrelevant polymorphisms. One advantage of H over the widely used inosine 'universal base' and 'mixed sequence' probes is seen in ligation-based assays to detect SNPs. The need to detect medically relevant SNPs within ambiguous sequences is especially important when probing RNA viruses, which rapidly mutate to create drug resistance, but also suffer neutral drift, the second obstructing simple methods to detect the first. Thus, H is being developed to detect variants of viruses that are rapidly mutating.