64 research outputs found
Meeting Impacts of Two Types of EMS Anonymity and Initial Difference in Opinions
A laboratoryexperiment was conducted to study the effects of two types of anonymityin an electronic meeting system (EMS) setting (source anonymity: participants know who their group members are but do not know the source of any comment, and participant anonymity:, participants do not know who their group members are), initial difference in opinions, and their interaction on participation and satisfaction. Results suggest that the effects of participant anonymityshould not be considered as similar in nature to but stronger than those of source anonymity. The extent to which source and participant anonymitymake a group salient to its members is proposed as a crucial determinant of the effects of source and participant anonymity
Envisioning a marine biodiversity observation network
Author Posting. © University of California Press and American Institute of Biological Sciences, 2013. This article is posted here by permission of University of California Press and American Institute of Biological Sciences for personal use, not for redistribution. The definitive version was published in BioScience 63 (2013): 350-361, doi:10.1525/bio.2013.63.5.8.Humans depend on diverse ocean ecosystems for food, jobs, and sustained well-being, yet many stressors threaten marine life. Extensive research has demonstrated that maintaining biodiversity promotes ocean health and service provision; therefore, monitoring the status and trends of marine biodiversity is important for effective ecosystem management. However, there is no systematic sustained program for evaluating ocean biodiversity. Coordinating existing monitoring and building a proactive marine biodiversity observation network will support efficient, economical resource management and conservation and should be a high priority. A synthesis of expert opinions suggests that, to be most effective, a marine biodiversity observation network should integrate biological levels, from genes to habitats; link biodiversity observations to abiotic environmental variables; site projects to incorporate environmental forcing and biogeography; and monitor adaptively to address emerging issues. We summarize examples illustrating how to leverage existing data and infrastructure to meet these goals
Satellite Sensor Requirements for Monitoring Essential Biodiversity Variables of Coastal Ecosystems
The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibratio
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An ocean-colour time series for use in climate studies: the experience of the ocean-colour climate change initiate (OC-CCI)
Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea viewingWide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation
coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel
Satellite sensor requirements for monitoring essential biodiversity variables of coastal ecosystems
© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecological Applications 28 (2018): 749-760, doi: 10.1002/eap.1682.The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satelliteâbased sensors can repeatedly record the visible and nearâinfrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100âm pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the shortâwave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14âbit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3âd repeat lowâEarth orbit could sample 30âkm swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.National Center for Ecological Analysis and Synthesis (NCEAS);
National Aeronautics and Space Administration (NASA) Grant Numbers: NNX16AQ34G, NNX14AR62A;
National Ocean Partnership Program;
NOAA US Integrated Ocean Observing System/IOOS Program Office;
Bureau of Ocean and Energy Management Ecosystem Studies program (BOEM) Grant Number: MC15AC0000
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Self-monitoring personality trait at work : an integrative narrative review and future research directions
In this narrative review, we provide an overview of the selfâmonitoring literature as it applies to the workplace context. Our starting point to the review is a metaâanalysis of selfâmonitoring literature published in 2002 by Day, Schleicher, Unckless, and Hiller. After providing an overview of the theoretical basis of selfâmonitoring and its measurement, we present a summary of the broad literature on selfâmonitoring to examine the implications of selfâmonitoring for employees and organizations. Based on our review, we identify the main outcomes of selfâmonitoring as well as findings of the literature treating selfâmonitoring as a moderator. We provide evidence that selfâmonitoring has potential downsides, which would benefit from further investigation. We conclude our review by identifying important potential future research directions
The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing
International audienceCurrent sampling of genomic sequence data from eukaryotes is relatively poor, biased, and inadequate to address important questions about their biology, evolution, and ecology; this Community Page describes a resource of 700 transcriptomes from marine microbial eukaryotes to help understand their role in the world's oceans
The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): illuminating the functional diversity of eukaryotic life in the oceans through transcriptome sequencing.
Microbial ecology is plagued by problems
of an abstract nature. Cell sizes are so
small and population sizes so large that
both are virtually incomprehensible. Niches
are so far from our everyday experience
as to make their very definition elusive.
Organisms that may be abundant and
critical to our survival are little understood,
seldom described and/or cultured,
and sometimes yet to be even seen. One
way to confront these problems is to use
data of an even more abstract nature:
molecular sequence data. Massive environmental
nucleic acid sequencing, such
as metagenomics or metatranscriptomics,
promises functional analysis of microbial
communities as a whole, without prior
knowledge of which organisms are in the
environment or exactly how they are
interacting. But sequence-based ecological
studies nearly always use a comparative
approach, and that requires relevant
reference sequences, which are an extremely
limited resource when it comes to
microbial eukaryotes.
In practice, this means sequence databases
need to be populated with enormous
quantities of data for which we have
some certainties about the source. Most
important is the taxonomic identity of
the organism from which a sequence is
derived and as much functional identification
of the encoded proteins as possible. In
an ideal world, such information would be
available as a large set of complete, well curated,
and annotated genomes for all the
major organisms from the environment
in question. Reality substantially diverges
from this ideal, but at least for bacterial
molecular ecology, there is a database
consisting of thousands of complete genomes
from a wide range of taxa,
supplemented by a phylogeny-driven approach
to diversifying genomics [2]. For
eukaryotes, the number of available genomes
is far, far fewer, and we have relied
much more heavily on random growth of
sequence databases, raising the
question as to whether this is fit for
purpose
The Marine Microbial Eukaryote Transcriptome Sequencing Project (MMETSP): Illuminating the Functional Diversity of Eukaryotic Life in the Oceans through Transcriptome Sequencing
Microbial ecology is plagued by problems of an abstract nature. Cell sizes are so small and population sizes so large that both are virtually incomprehensible. Niches are so far from our everyday experience as to make their very definition elusive. Organisms that may be abundant and critical to our survival are little understood, seldom described and/or cultured, and sometimes yet to be even seen. One way to confront these problems is to use data of an even more abstract nature: molecular sequence data. Massive environmental nucleic acid sequencing, such as metagenomics or metatranscriptomics, promises functional analysis of microbial communities as a whole, without prior knowledge of which organisms are in the environment or exactly how they are interacting. But sequence-based ecological studies nearly always use a comparative approach, and that requires relevant reference sequences, which are an extremely limited resource when it comes to microbial eukaryotes
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