Monterey Bay Aquarium Research Institute

About: Monterey Bay Aquarium Research Institute is a nonprofit organization based out in Castroville, California, United States. It is known for research contribution in the topics: Upwelling & Population. The organization has 630 authors who have published 2068 publications receiving 119899 citations. The organization is also known as: Monterey Bay Aquarium and Research Institute & MBARI.

Biological and Chemical Response of the Equatorial Pacific Ocean to the 1997-98 El Niño

Abstract: During the 1997–98 El Nino, the equatorial Pacific Ocean retained 0.7 × 10 15 grams of carbon that normally would have been lost to the atmosphere as carbon dioxide. The surface ocean became impoverished in plant nutrients, and chlorophyll concentrations were the lowest on record. A dramatic recovery occurred in mid-1998, the system became highly productive, analogous to coastal environments, and carbon dioxide flux out of the ocean was again high. The spatial extent of the phytoplankton bloom that followed recovery from El Nino was the largest ever observed for the equatorial Pacific. These chemical and ecological perturbations were linked to changes in the upwelling of nutrient-enriched waters. The description and explanation of these dynamic changes would not have been possible without an observing system that combines biological, chemical, and physical sensors on moorings with remote sensing of chlorophyll.

Pathways of carbon assimilation and ammonia oxidation suggested by environmental genomic analyses of marine Crenarchaeota.

Abstract: Marine Crenarchaeota represent an abundant component of oceanic microbiota with potential to significantly influence biogeochemical cycling in marine ecosystems. Prior studies using specific archaeal lipid biomarkers and isotopic analyses indicated that planktonic Crenarchaeota have the capacity for autotrophic growth, and more recent cultivation studies support an ammonia-based chemolithoautotrophic energy metabolism. We report here analysis of fosmid sequences derived from the uncultivated marine crenarchaeote, Cenarchaeum symbiosum, focused on the reconstruction of carbon and energy metabolism. Genes predicted to encode multiple components of a modified 3-hydroxypropionate cycle of autotrophic carbon assimilation were identified, consistent with utilization of carbon dioxide as a carbon source. Additionally, genes predicted to encode a near complete oxidative tricarboxylic acid cycle were also identified, consistent with the consumption of organic carbon and in the production of intermediates for amino acid and cofactor biosynthesis. Therefore, C. symbiosum has the potential to function either as a strict autotroph, or as a mixotroph utilizing both carbon dioxide and organic material as carbon sources. From the standpoint of energy metabolism, genes predicted to encode ammonia monooxygenase subunits, ammonia permease, urease, and urea transporters were identified, consistent with the use of reduced nitrogen compounds as energy sources fueling autotrophic metabolism. Homologues of these genes, recovered from ocean waters worldwide, demonstrate the conservation and ubiquity of crenarchaeal pathways for carbon assimilation and ammonia oxidation. These findings further substantiate the likely global metabolic importance of Crenarchaeota with respect to key steps in the biogeochemical transformation of carbon and nitrogen in marine ecosystems.

Best Practices for Scientific Computing

Abstract: Scientists spend an increasing amount of time building and using software. However, most scientists are never taught how to do this efficiently. As a result, many are unaware of tools and practices that would allow them to write more reliable and maintainable code with less effort. We describe a set of best practices for scientific software development that have solid foundations in research and experience, and that improve scientists' productivity and the reliability of their software. Software is as important to modern scientific research as telescopes and test tubes. From groups that work exclusively on computational problems, to traditional laboratory and field scientists, more and more of the daily operation of science revolves around developing new algorithms, managing and analyzing the large amounts of data that are generated in single research projects, combining disparate datasets to assess synthetic problems, and other computational tasks. Scientists typically develop their own software for these purposes because doing so requires substantial domain-specific knowledge. As a result, recent studies have found that scientists typically spend 30% or more of their time developing software [1],[2]. However, 90% or more of them are primarily self-taught [1],[2], and therefore lack exposure to basic software development practices such as writing maintainable code, using version control and issue trackers, code reviews, unit testing, and task automation. We believe that software is just another kind of experimental apparatus [3] and should be built, checked, and used as carefully as any physical apparatus. However, while most scientists are careful to validate their laboratory and field equipment, most do not know how reliable their software is [4],[5]. This can lead to serious errors impacting the central conclusions of published research [6]: recent high-profile retractions, technical comments, and corrections because of errors in computational methods include papers in Science [7],[8], PNAS [9], the Journal of Molecular Biology [10], Ecology Letters [11],[12], the Journal of Mammalogy [13], Journal of the American College of Cardiology [14], Hypertension [15], and The American Economic Review [16]. In addition, because software is often used for more than a single project, and is often reused by other scientists, computing errors can have disproportionate impacts on the scientific process. This type of cascading impact caused several prominent retractions when an error from another group's code was not discovered until after publication [6]. As with bench experiments, not everything must be done to the most exacting standards; however, scientists need to be aware of best practices both to improve their own approaches and for reviewing computational work by others. This paper describes a set of practices that are easy to adopt and have proven effective in many research settings. Our recommendations are based on several decades of collective experience both building scientific software and teaching computing to scientists [17],[18], reports from many other groups [19]–, guidelines for commercial and open source software development [26],, and on empirical studies of scientific computing [28]–[31] and software development in general (summarized in [32]). None of these practices will guarantee efficient, error-free software development, but used in concert they will reduce the number of errors in scientific software, make it easier to reuse, and save the authors of the software time and effort that can used for focusing on the underlying scientific questions. Our practices are summarized in Box 1; labels in the main text such as “(1a)” refer to items in that summary. For reasons of space, we do not discuss the equally important (but independent) issues of reproducible research, publication and citation of code and data, and open science. We do believe, however, that all of these will be much easier to implement if scientists have the skills we describe. Box 1. Summary of Best Practices Write programs for people, not computers. A program should not require its readers to hold more than a handful of facts in memory at once. Make names consistent, distinctive, and meaningful. Make code style and formatting consistent. Let the computer do the work. Make the computer repeat tasks. Save recent commands in a file for re-use. Use a build tool to automate workflows. Make incremental changes. Work in small steps with frequent feedback and course correction. Use a version control system. Put everything that has been created manually in version control. Don't repeat yourself (or others). Every piece of data must have a single authoritative representation in the system. Modularize code rather than copying and pasting. Re-use code instead of rewriting it. Plan for mistakes. Add assertions to programs to check their operation. Use an off-the-shelf unit testing library. Turn bugs into test cases. Use a symbolic debugger. Optimize software only after it works correctly. Use a profiler to identify bottlenecks. Write code in the highest-level language possible. Document design and purpose, not mechanics. Document interfaces and reasons, not implementations. Refactor code in preference to explaining how it works. Embed the documentation for a piece of software in that software. Collaborate. Use pre-merge code reviews. Use pair programming when bringing someone new up to speed and when tackling particularly tricky problems. Use an issue tracking tool. Write Programs for People, Not Computers Scientists writing software need to write code that both executes correctly and can be easily read and understood by other programmers (especially the author's future self). If software cannot be easily read and understood, it is much more difficult to know that it is actually doing what it is intended to do. To be productive, software developers must therefore take several aspects of human cognition into account: in particular, that human working memory is limited, human pattern matching abilities are finely tuned, and human attention span is short [33]–[37]. First, a program should not require its readers to hold more than a handful of facts in memory at once (1a). Human working memory can hold only a handful of items at a time, where each item is either a single fact or a “chunk” aggregating several facts [33],[34], so programs should limit the total number of items to be remembered to accomplish a task. The primary way to accomplish this is to break programs up into easily understood functions, each of which conducts a single, easily understood, task. This serves to make each piece of the program easier to understand in the same way that breaking up a scientific paper using sections and paragraphs makes it easier to read. Second, scientists should make names consistent, distinctive, and meaningful (1b). For example, using non-descriptive names, like a and foo, or names that are very similar, like results and results2, is likely to cause confusion. Third, scientists should make code style and formatting consistent (1c). If different parts of a scientific paper used different formatting and capitalization, it would make that paper more difficult to read. Likewise, if different parts of a program are indented differently, or if programmers mix CamelCaseNaming and pothole_case_naming, code takes longer to read and readers make more mistakes [35],[36].

Rethinking the marine carbon cycle: Factoring in the multifarious lifestyles of microbes

Abstract: BACKGROUND Marine ecosystems are composed of a diverse array of life forms, the majority of which are unicellular—archaea, bacteria, and eukaryotes. The power of these microbes to process carbon, shape Earth’s atmosphere, and fuel marine food webs has been established over the past 40 years. The marine biosphere is responsible for approximately half of global primary production, rivaling that of land plants. Unicellular eukaryotes (protists) are major contributors to this ocean productivity. In addition to photosynthetic growth, protists exhibit a range of other trophic modes, including predation, mixotrophy (a combination of photosynthetic and predatory-based nutrition), parasitism, symbiosis, osmotrophy, and saprotrophy (wherein extracellular enzymes break down organic matter to smaller compounds that are then transported into the cell by osmotrophy). ADVANCES Sensitive field approaches have illuminated the enormous diversity of protistan life (much of it uncultured) and, coupled with activity measurements, are leading to hypotheses about their ecological roles. In parallel, large-scale sequencing projects are providing fundamental advances in knowledge of genome/gene composition, especially among photosynthetic lineages, many of which are complex amalgams derived from multiple endosymbiotic mergers. Marine protists have yielded insight into basic biology, evolution, and molecular machineries that control organismal responses to the environment. These studies reveal tightly controlled signaling and transcriptional regulation as well as responses to limitation of resources such as iron, nitrogen, and vitamins, and offer understanding of animal and plant evolution. With the formulation of better computational approaches, hypotheses about interactions and trophic exchanges are becoming more exact and modelers more assertive at integrating different data types. At the same time, the impacts of climate change are being reported in multiple systems, of which polar environments are the touchstone of change. OUTLOOK Driven by the need to translate the biology of cells into processes at global scales, researchers must bring the conceptual framework of systems biology into bigger “ecosystems biology” models that broadly capture the geochemical activities of interacting plankton networks. Existing data show that protists are major components of marine food webs, but deducing and quantifying their ecosystem linkages and the resulting influences on carbon cycling is difficult. Genome-based functional predictions are complicated by the importance of cellular structures and flexible behaviors in protists, which are inherently more difficult to infer than the biochemical pathways typically studied in prokaryotes. Alongside the plethora of genes of unknown function, manipulable genetic systems are rare for marine protists. The development of genetic systems and gene editing for diverse, ecologically important lineages, as well as innovative tools for preserving microbe-microbe interactions during sampling, for visual observation, and for quantifying biogeochemical transformations, are critical but attainable goals. These must be implemented in both field work and laboratory physiology studies that examine multiple environmental factors. Expanding genome functional predictions to identify the molecular underpinnings of protistan trophic modes and realistically constrain metabolism will position the field to build reliable cell systems biology models and link these to field studies. By factoring in true complexities, we can capture key elements of protistan interactions for assimilation into more predictive global carbon cycle models.