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Buchhorn K, Santos-Fernandez E, Mengersen K, Salomone R. Graph neural network-based anomaly detection for river network systems. F1000Res 2024; 12:991. [PMID: 38854704 PMCID: PMC11162521 DOI: 10.12688/f1000research.136097.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/16/2024] [Indexed: 06/11/2024] Open
Abstract
Background Water is the lifeblood of river networks, and its quality plays a crucial role in sustaining both aquatic ecosystems and human societies. Real-time monitoring of water quality is increasingly reliant on in-situ sensor technology.Anomaly detection is crucial for identifying erroneous patterns in sensor data, but can be a challenging task due to the complexity and variability of the data, even under typical conditions. This paper presents a solution to the challenging task of anomaly detection for river network sensor data, which is essential for accurate and continuous monitoring. Methods We use a graph neural network model, the recently proposed Graph Deviation Network (GDN), which employs graph attention-based forecasting to capture the complex spatio-temporal relationships between sensors. We propose an alternate anomaly threshold criteria for the model, GDN+, based on the learned graph. To evaluate the model's efficacy, we introduce new benchmarking simulation experiments with highly-sophisticated dependency structures and subsequence anomalies of various types. We also introduce software called gnnad. Results We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data. Conclusions Findings suggest that GDN+ outperforms the baseline approach in high-dimensional data, while also providing improved interpretability.
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Affiliation(s)
- Katie Buchhorn
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Edgar Santos-Fernandez
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Kerrie Mengersen
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Queensland, Australia
| | - Robert Salomone
- Centre for Data Science, Queensland University of Technology, Brisbane, Queensland, Australia
- School of Computer Science, Queensland University of Technology, Brisbane, Queensland, Australia
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Kelso JE, Saulnier W, Fritz KM, Nadeau TL, Topping B. The stream intermittency visualization dashboard: A web application for high-frequency logger data and daily flow observations. HYDROLOGICAL PROCESSES 2023; 37:10.1002/hyp.14809. [PMID: 37323824 PMCID: PMC10265789 DOI: 10.1002/hyp.14809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 01/09/2023] [Indexed: 06/17/2023]
Affiliation(s)
- Julia E Kelso
- Oak Ridge Institute of Science and Education Fellow at U.S. Environmental Protection Agency, Office of Wetlands, Oceans, and Watersheds, Washington, District of Columbia, USA
| | - William Saulnier
- Ecosystem Planning and Restoration, Raleigh, North Carolina, USA
| | - Ken M Fritz
- Office of Research and Development, U.S. Environmental Protection Agency, Cincinnati, Ohio, USA
| | - Tracie-Lynn Nadeau
- Office of Wetlands, Oceans, and Watersheds, U.S. Environmental Protection Agency, Washington, District of Columbia, USA
- Region 10, U.S. Environmental Protection Agency, Portland, Oregon, USA
| | - Brian Topping
- Office of Wetlands, Oceans, and Watersheds, U.S. Environmental Protection Agency, Washington, District of Columbia, USA
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Hatami Manesh M, Haghshenas A, Mirzaei M, Azadi H, Marofi S. Seasonal variations of polycyclic aromatic hydrocarbons in coastal sediments of a marine resource hot spot: the case of pars special economic energy zone, Iran. ENVIRONMENTAL GEOCHEMISTRY AND HEALTH 2021; 43:3897-3919. [PMID: 33742337 DOI: 10.1007/s10653-021-00863-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Accepted: 02/14/2021] [Indexed: 06/12/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are an important group of compounds of major environmental concern, which are in the class of persistent organic pollutants. Therefore, the key purpose of this research was to analyze seasonal fluctuations and to determine the probability of polycyclic aromatic hydrocarbons in coastal sediments of the Iranian Marine Resource Center based on the evaluation of 16 US-EPA important PAH compounds. These compounds have been collected from intertidal sediments located in the marine resources of southern Iran in different seasons. These samples of the surficial sediment were collected at the PSEEZ area using a stainless steel grab sampler in four seasons, from depths between 0.5 and 30 m. Surface sediment samples were removed by spoons and carefully placed in an aluminum foil; they were taken to the laboratory on ice and held at 20° C until their study. After extraction, by using a rotary evaporator apparatus, samples were condensed. The assay was added to roughly 2 g of activated copper flasks in the refrigerator for 36 h for desulfurization. Among different seasons, the highest concentration was observed in winter, with a mean of 281.3 ng g-1. According to ecological risk assessment (concentrations of possible effects, low effect range, degree of threshold effects, and median effect range), PAH risks in surface sediments of PSEEZ were lower than the threshold results levels (TEL), possible effects levels (PEL), low range of effects (ERL), and median range of effects (ERM), indicating that a biological effect would rarely occur. The dry weight scale of the concentration of ∑PAHs ranges from 145.7 to 348.42 ng g-1 with a mean quantity of 260.52 ng g-1. Therefore, according to the amount of ∑PAH concentration, the sediments in the PSEEZ area indicated moderate to heavy pollutions. In this way, the sedimentary surface ecosystems of the Persian Gulf were considered as moderately polluted compared with other ecosystems worldwide. Our study highlighted some of the research gaps in PAH contamination studies and the level of PAH contamination. Therefore, this study will provide a scientific background, planning, and policies for PAH pollution control and environmental protection in Iran and similar regions around the world.
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Affiliation(s)
- Masoud Hatami Manesh
- Young Researcher and Eite Club, Yasouj Branch, Islamic Azad University, Yasouj, Iran
| | - Arash Haghshenas
- Iran Shrimp Research Center, Agricultural Research, Education and Extension Organization, Iranian Fisheries Science Research Institute, Tehran, Iran
| | - Mohsen Mirzaei
- Department of Environment, School of Natural Resources and Marine Sciences, Tarbiat Modares University, Tehran, Iran.
| | - Hossein Azadi
- Department of Geography, Ghent University, Ghent, Belgium
- Research Group Climate Change and Security, Institute of Geography, University of Hamburg, Hamburg, Germany
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Prague, Czech Republic
| | - Safar Marofi
- Water Engineering Department, Bu-Ali Sina University, Hamedan, Iran
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Kong X, Zhan Q, Boehrer B, Rinke K. High frequency data provide new insights into evaluating and modeling nitrogen retention in reservoirs. WATER RESEARCH 2019; 166:115017. [PMID: 31491621 DOI: 10.1016/j.watres.2019.115017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 08/10/2019] [Accepted: 08/20/2019] [Indexed: 06/10/2023]
Abstract
Freshwater ecosystems including lakes and reservoirs are hot spots for retention of excess nitrogen (N) from anthropogenic sources, providing valuable ecological services for downstream and coastal ecosystems. Despite previous investigations, current quantitative understanding on the influential factors and underlying mechanisms of N retention in lentic freshwater systems is insufficient due to data paucity and limitation of modeling techniques. Our ability to reliably predict N retention for these systems therefore remains uncertain. Emerging high frequency monitoring techniques and well-developed ecosystem modeling shed light on this issue. In the present study, we explored the retention of NO3-N during a five-year period (2013-2017) in both annual and weekly scales in a highly flushed reservoir in Germany. We found that annual-averaged NO3-N retention efficiency could be up to 17% with an overall retention efficiency of ∼4% in such a system characterized by a water residence time (WRT) of ∼4 days. On the weekly scale, the reservoir displayed negative retention in winter (i.e. a source of NO3-N) and high positive retention in summer (i.e. a sink for NO3-N). We further identified the critical role of Chl-a concentration together with the well-recognized effects from WRT in dictating NO3-N retention efficiency, implying the significance of biological processes including phytoplankton dynamics in driving NO3-N retention. Furthermore, our modeling approach showed that an established process-based ecosystem model (PCLake) accounted for 58.0% of the variance in NO3-N retention efficiency, whereas statistical models obtained a lower value (40.5%). This finding exemplified the superior predictive power of process-based models over statistical models whenever ecological processes were at play. Overall, our study highlights the importance of high frequency data in providing new insights into evaluating and modeling N retention in reservoirs.
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Affiliation(s)
- Xiangzhen Kong
- Department of Lake Research, Helmholtz Centre for Environmental Research (UFZ), Brückstr. 3a, 39114, Magdeburg, Germany.
| | - Qing Zhan
- Department of Lake Research, Helmholtz Centre for Environmental Research (UFZ), Brückstr. 3a, 39114, Magdeburg, Germany; Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), P.O. Box 50, 6700 AB Wageningen, The Netherlands
| | - Bertram Boehrer
- Department of Lake Research, Helmholtz Centre for Environmental Research (UFZ), Brückstr. 3a, 39114, Magdeburg, Germany
| | - Karsten Rinke
- Department of Lake Research, Helmholtz Centre for Environmental Research (UFZ), Brückstr. 3a, 39114, Magdeburg, Germany
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Porter JH. Evaluating a thesaurus for discovery of ecological data. ECOL INFORM 2019. [DOI: 10.1016/j.ecoinf.2019.03.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Innovations in Monitoring With Water-Quality Sensors With Case Studies on Floods, Hurricanes, and Harmful Algal Blooms. SEP SCI TECHNOL 2019. [DOI: 10.1016/b978-0-12-815730-5.00010-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Cheruvelil KS, Soranno PA. Data-Intensive Ecological Research Is Catalyzed by Open Science and Team Science. Bioscience 2018. [DOI: 10.1093/biosci/biy097] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Kendra Spence Cheruvelil
- Professor in Lyman Briggs College and the Department of Fisheries and Wildlife
- Conceptualization and writing of this article
| | - Patricia A Soranno
- Professor in the Department of Fisheries and Wildlife, at Michigan State University, in East Lansing
- Conceptualization and writing of this article
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Murray NJ, Keith DA, Bland LM, Ferrari R, Lyons MB, Lucas R, Pettorelli N, Nicholson E. The role of satellite remote sensing in structured ecosystem risk assessments. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 619-620:249-257. [PMID: 29149749 DOI: 10.1016/j.scitotenv.2017.11.034] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Revised: 11/03/2017] [Accepted: 11/03/2017] [Indexed: 05/19/2023]
Abstract
The current set of global conservation targets requires methods for monitoring the changing status of ecosystems. Protocols for ecosystem risk assessment are uniquely suited to this task, providing objective syntheses of a wide range of data to estimate the likelihood of ecosystem collapse. Satellite remote sensing can deliver ecologically relevant, long-term datasets suitable for analysing changes in ecosystem area, structure and function at temporal and spatial scales relevant to risk assessment protocols. However, there is considerable uncertainty about how to select and effectively utilise remotely sensed variables for risk assessment. Here, we review the use of satellite remote sensing for assessing spatial and functional changes of ecosystems, with the aim of providing guidance on the use of these data in ecosystem risk assessment. We suggest that decisions on the use of satellite remote sensing should be made a priori and deductively with the assistance of conceptual ecosystem models that identify the primary indicators representing the dynamics of a focal ecosystem.
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Affiliation(s)
- Nicholas J Murray
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, New South Wales, Australia; School of Biological Sciences, University of Queensland, St. Lucia, Queensland 4072, Australia.
| | - David A Keith
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, New South Wales, Australia; New South Wales Office of Environment and Heritage, Hurstville, New South Wales, Australia.
| | - Lucie M Bland
- Deakin University, School of Life and Environmental Sciences, Centre for Integrative Ecology (Burwood Campus), 221 Burwood Highway, Burwood, VIC 3125, Australia.
| | - Renata Ferrari
- Australian Institute of Marine Science, Townsville, 4810, Australia
| | - Mitchell B Lyons
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, New South Wales, Australia.
| | - Richard Lucas
- Centre for Ecosystem Science, School of Biological, Earth and Environmental Sciences, University of New South Wales, New South Wales, Australia.
| | - Nathalie Pettorelli
- Institute of Zoology, Zoological Society of London, Regent's Park, NW1 4RY London, UK.
| | - Emily Nicholson
- Deakin University, School of Life and Environmental Sciences, Centre for Integrative Ecology (Burwood Campus), 221 Burwood Highway, Burwood, VIC 3125, Australia.
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Wireless Concrete Strength Monitoring of Wind Turbine Foundations. SENSORS 2017; 17:s17122928. [PMID: 29258176 PMCID: PMC5750550 DOI: 10.3390/s17122928] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 11/28/2017] [Accepted: 12/14/2017] [Indexed: 11/17/2022]
Abstract
Wind turbine foundations are typically cast in place, leaving the concrete to mature under environmental conditions that vary in time and space. As a result, there is uncertainty around the concrete’s initial performance, and this can encourage both costly over-design and inaccurate prognoses of structural health. Here, we demonstrate the field application of a dense, wireless thermocouple network to monitor the strength development of an onshore, reinforced-concrete wind turbine foundation. Up-to-date methods in fly ash concrete strength and maturity modelling are used to estimate the distribution and evolution of foundation strength over 29 days of curing. Strength estimates are verified by core samples, extracted from the foundation base. In addition, an artificial neural network, trained using temperature data, is exploited to demonstrate that distributed concrete strengths can be estimated for foundations using only sparse thermocouple data. Our techniques provide a practical alternative to computational models, and could assist site operators in making more informed decisions about foundation design, construction, operation and maintenance.
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Jarnevich CS, Talbert M, Morisette J, Aldridge C, Brown CS, Kumar S, Manier D, Talbert C, Holcombe T. Minimizing effects of methodological decisions on interpretation and prediction in species distribution studies: An example with background selection. Ecol Modell 2017. [DOI: 10.1016/j.ecolmodel.2017.08.017] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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The Oklahoma Mesonet: A Pilot Study of Environmental Sensor Data Citations. DATA SCIENCE JOURNAL 2017. [DOI: 10.5334/dsj-2017-047] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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Gries C, Gahler MR, Hanson PC, Kratz TK, Stanley EH. Information management at the North Temperate Lakes Long-term Ecological Research site — Successful support of research in a large, diverse, and long running project. ECOL INFORM 2016. [DOI: 10.1016/j.ecoinf.2016.08.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Marcé R, George G, Buscarinu P, Deidda M, Dunalska J, de Eyto E, Flaim G, Grossart HP, Istvanovics V, Lenhardt M, Moreno-Ostos E, Obrador B, Ostrovsky I, Pierson DC, Potužák J, Poikane S, Rinke K, Rodríguez-Mozaz S, Staehr PA, Šumberová K, Waajen G, Weyhenmeyer GA, Weathers KC, Zion M, Ibelings BW, Jennings E. Automatic High Frequency Monitoring for Improved Lake and Reservoir Management. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2016; 50:10780-10794. [PMID: 27597444 DOI: 10.1021/acs.est.6b01604] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent technological developments have increased the number of variables being monitored in lakes and reservoirs using automatic high frequency monitoring (AHFM). However, design of AHFM systems and posterior data handling and interpretation are currently being developed on a site-by-site and issue-by-issue basis with minimal standardization of protocols or knowledge sharing. As a result, many deployments become short-lived or underutilized, and many new scientific developments that are potentially useful for water management and environmental legislation remain underexplored. This Critical Review bridges scientific uses of AHFM with their applications by providing an overview of the current AHFM capabilities, together with examples of successful applications. We review the use of AHFM for maximizing the provision of ecosystem services supplied by lakes and reservoirs (consumptive and non consumptive uses, food production, and recreation), and for reporting lake status in the EU Water Framework Directive. We also highlight critical issues to enhance the application of AHFM, and suggest the establishment of appropriate networks to facilitate knowledge sharing and technological transfer between potential users. Finally, we give advice on how modern sensor technology can successfully be applied on a larger scale to the management of lakes and reservoirs and maximize the ecosystem services they provide.
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Affiliation(s)
- Rafael Marcé
- Catalan Institute for Water Research (ICRA) , Emili Grahit 101, 17003 Girona, Spain
| | - Glen George
- Freshwater Biological Association , 34786 Windermere, U.K
- Department of Geography and Earth Sciences, University of Aberystwyth , Aberystwyth, Ceredigion, SY23 3FL, U.K
| | - Paola Buscarinu
- Ente acque della Sardegna , via Mameli 88, 09123 Cagliari, Italy
| | - Melania Deidda
- Ente acque della Sardegna , via Mameli 88, 09123 Cagliari, Italy
| | - Julita Dunalska
- Department of Water Protection Engineering, University of Warmia and Mazury in Olsztyn , Prawocheńskiego strasse 1, 10-719 Olsztyn, Poland
| | - Elvira de Eyto
- Marine Institute , Furnace, Newport, County Mayo F28 PF65, Ireland
| | - Giovanna Flaim
- Research and Innovation Centre , Foundazione Edmund Mach, 38010 San Michele all' Adige, TN, Italy
| | - Hans-Peter Grossart
- Leibniz-Institute of Freshwater Ecology and Inland Fisheries , Alte Fischerhuette 2, 16775 Stechlin, Germany
- Potsdam University , Institute for Biochemistry and Biology, Maulbeerallee 2, 14469 Potsdam, Germany
| | - Vera Istvanovics
- MTA/BME Water Research Group , Műegyetem rkp. 3, 1111 Budapest, Hungary
| | - Mirjana Lenhardt
- Institute for Biological Research University of Belgrade , Bulevar Despota Stefana 142, 11000 Belgrade, Serbia
| | - Enrique Moreno-Ostos
- Marine Ecology and Limnology Research Group, Department of Ecology, University of Málaga , Campus Universitario de Teatinos S/N, 29071 Málaga, Spain
| | - Biel Obrador
- Department of Ecology, University of Barcelona , Av Diagonal 643, 08028 Barcelona, Spain
| | - Ilia Ostrovsky
- Israel Oceanographic and Limnological Research, Yigal Allon Kinneret Limnological Laboratory , 14850 Migdal, Israel
| | - Donald C Pierson
- Department of Limnology, Evolutionary Biology Centre , Norbyvägen 18 D, 752 36 Uppsala, Sweden
| | - Jan Potužák
- Institute of Botany, The Czech Academy of Sciences , Department of Vegetation Ecology, Lidická 25/27, 602 00 Brno, Czech Republic
| | - Sandra Poikane
- European Commission , Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi 2749, 21027 Ispra, Varese, Italy
| | - Karsten Rinke
- Helmholtz Centre for Environmental Research (UFZ) , Department of Lake Research, Brückstrasse 3a, D-39114 Magdeburg, Germany
| | - Sara Rodríguez-Mozaz
- Catalan Institute for Water Research (ICRA) , Emili Grahit 101, 17003 Girona, Spain
| | - Peter A Staehr
- Institute of Bioscience, Aarhus University , Frederiksborgvej 399, 4000 Roskilde, Denmark
| | - Kateřina Šumberová
- Institute of Botany, The Czech Academy of Sciences , Department of Vegetation Ecology, Lidická 25/27, 602 00 Brno, Czech Republic
| | - Guido Waajen
- Water Authority Brabantse Delta , P.O. Box 5520, 4801 DZ Breda, The Netherlands
| | - Gesa A Weyhenmeyer
- Department of Ecology and Genetics/Limnology, Uppsala University , Norbyvägen 18D, 75236 Uppsala, Sweden
| | - Kathleen C Weathers
- Cary Institute of Ecosystem Studies , Box AB, Millbrook, New York 12545, United States
| | - Mark Zion
- New York City Department of Environmental Protection , 71 Smith Avenue, Kingston, New York 12401, United States
| | - Bas W Ibelings
- Department F.-A. Forel for Environmental and Aquatic Sciences & Institute for Environmental Sciences, University of Geneva , 66 Boulevard Carl-Vogt, 1211 Geneva, Switzerland
| | - Eleanor Jennings
- Centre for Freshwater and Environmental Studies and Department of Applied Sciences, Dundalk Institute of Technology , Dundalk, County Louth A91 K584, Ireland
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Elliott KC, Cheruvelil KS, Montgomery GM, Soranno PA. Conceptions of Good Science in Our Data-Rich World. Bioscience 2016; 66:880-889. [PMID: 29599533 PMCID: PMC5862324 DOI: 10.1093/biosci/biw115] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Scientists have been debating for centuries the nature of proper scientific methods. Currently, criticisms being thrown at data-intensive science are reinvigorating these debates. However, many of these criticisms represent long-standing conflicts over the role of hypothesis testing in science and not just a dispute about the amount of data used. Here, we show that an iterative account of scientific methods developed by historians and philosophers of science can help make sense of data-intensive scientific practices and suggest more effective ways to evaluate this research. We use case studies of Darwin's research on evolution by natural selection and modern-day research on macrosystems ecology to illustrate this account of scientific methods and the innovative approaches to scientific evaluation that it encourages. We point out recent changes in the spheres of science funding, publishing, and education that reflect this richer account of scientific practice, and we propose additional reforms.
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Affiliation(s)
- Kevin C Elliott
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
| | - Kendra S Cheruvelil
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
| | - Georgina M Montgomery
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
| | - Patricia A Soranno
- Kevin C. Elliott is an associate professor in Lyman Briggs College, the Department of Fisheries and Wildlife, and the Department of Philosophy; Kendra S. Cheruvelil is an associate professor in Lyman Briggs College and the Department of Fisheries and Wildlife; Georgina M. Montgomery is an associate professor in Lyman Briggs College and the Department of History; and Patricia A. Soranno is a professor in the Department of Fisheries and Wildlife at Michigan State University, in East Lansing. All authors contributed equally to the conceptualization of the paper and the supporting research. KCE organized the collaboration and initiated the writing process. All authors contributed text, reviewed manuscript drafts, and approved the final version
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Moran MS, Heilman P, Peters DPC, Holifield Collins C. Agroecosystem research with big data and a modified scientific method using machine learning concepts. Ecosphere 2016. [DOI: 10.1002/ecs2.1493] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Affiliation(s)
- M. Susan Moran
- USDA ARS Southwest Watershed Research Center Tucson Arizona 85719 USA
| | - Philip Heilman
- USDA ARS Southwest Watershed Research Center Tucson Arizona 85719 USA
| | - Debra P. C. Peters
- USDA ARS Jornada Experimental Range and the Jornada Basin Long Term Ecological Research Program New Mexico State University Las Cruces New Mexico 88003 USA
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Huang BE, Mulyasasmita W, Rajagopal G. The path from big data to precision medicine. EXPERT REVIEW OF PRECISION MEDICINE AND DRUG DEVELOPMENT 2016. [DOI: 10.1080/23808993.2016.1157686] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Edgar GJ, Bates AE, Bird TJ, Jones AH, Kininmonth S, Stuart-Smith RD, Webb TJ. New Approaches to Marine Conservation Through the Scaling Up of Ecological Data. ANNUAL REVIEW OF MARINE SCIENCE 2016; 8:435-61. [PMID: 26253270 DOI: 10.1146/annurev-marine-122414-033921] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
In an era of rapid global change, conservation managers urgently need improved tools to track and counter declining ecosystem conditions. This need is particularly acute in the marine realm, where threats are out of sight, inadequately mapped, cumulative, and often poorly understood, thereby generating impacts that are inefficiently managed. Recent advances in macroecology, statistical analysis, and the compilation of global data will play a central role in improving conservation outcomes, provided that global, regional, and local data streams can be integrated to produce locally relevant and interpretable outputs. Progress will be assisted by (a) expanded rollout of systematic surveys that quantify species patterns, including some carried out with help from citizen scientists; (b) coordinated experimental research networks that utilize large-scale manipulations to identify mechanisms underlying these patterns;
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Affiliation(s)
- Graham J Edgar
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart 7004, Tasmania, Australia; ,
| | - Amanda E Bates
- National Oceanography Centre Southampton, University of Southampton, Southampton SO14 3ZH, United Kingdom;
| | - Tomas J Bird
- Department of Geography and the Environment, University of Southampton, Southampton SO17 1BJ, United Kingdom;
| | - Alun H Jones
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, United Kingdom; ,
| | - Stuart Kininmonth
- Stockholm Resilience Centre, Stockholm University, SE-106 91 Stockholm, Sweden;
| | - Rick D Stuart-Smith
- Institute for Marine and Antarctic Studies, University of Tasmania, Hobart 7004, Tasmania, Australia; ,
| | - Thomas J Webb
- Department of Animal and Plant Sciences, University of Sheffield, Sheffield S10 2TN, United Kingdom; ,
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Abstract
Digital technology is changing nature conservation in increasingly profound ways. We describe this impact and its significance through the concept of 'digital conservation', which we found to comprise five pivotal dimensions: data on nature, data on people, data integration and analysis, communication and experience, and participatory governance. Examining digital innovation in nature conservation and addressing how its development, implementation and diffusion may be steered, we warn against hypes, techno-fix thinking, good news narratives and unverified assumptions. We identify a need for rigorous evaluation, more comprehensive consideration of social exclusion, frameworks for regulation and increased multi-sector as well as multi-discipline awareness and cooperation. Along the way, digital technology may best be reconceptualised by conservationists from something that is either good or bad, to a dual-faced force in need of guidance.
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Affiliation(s)
- Koen Arts
- Forest and Nature Conservation Policy Group, Wageningen University, Droevendaalsesteeg 3, 6700 AA, Wageningen, the Netherlands.
- Centro de Pesquisa do Pantanal, Universidade Federal de Mato Grosso, Cuiabá, CEP: 78.068-360, Brazil.
| | - René van der Wal
- Aberdeen Centre for Environmental Sustainability (ACES), School of Biological Sciences, University of Aberdeen, Aberdeen, AB24 3UU, UK
| | - William M Adams
- Department of Geography, University of Cambridge, Downing Place, Cambridge, CB2 3EN, UK
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24
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Vanderbilt KL, Lin CC, Lu SS, Kassim AR, He H, Guo X, Gil IS, Blankman D, Porter JH. Fostering ecological data sharing: collaborations in the International Long Term Ecological Research Network. Ecosphere 2015. [DOI: 10.1890/es14-00281.1] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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25
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Soranno PA, Bissell EG, Cheruvelil KS, Christel ST, Collins SM, Fergus CE, Filstrup CT, Lapierre JF, Lottig NR, Oliver SK, Scott CE, Smith NJ, Stopyak S, Yuan S, Bremigan MT, Downing JA, Gries C, Henry EN, Skaff NK, Stanley EH, Stow CA, Tan PN, Wagner T, Webster KE. Building a multi-scaled geospatial temporal ecology database from disparate data sources: fostering open science and data reuse. Gigascience 2015; 4:28. [PMID: 26140212 PMCID: PMC4488039 DOI: 10.1186/s13742-015-0067-4] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Accepted: 06/09/2015] [Indexed: 11/19/2022] Open
Abstract
Although there are considerable site-based data for individual or groups of ecosystems, these datasets are widely scattered, have different data formats and conventions, and often have limited accessibility. At the broader scale, national datasets exist for a large number of geospatial features of land, water, and air that are needed to fully understand variation among these ecosystems. However, such datasets originate from different sources and have different spatial and temporal resolutions. By taking an open-science perspective and by combining site-based ecosystem datasets and national geospatial datasets, science gains the ability to ask important research questions related to grand environmental challenges that operate at broad scales. Documentation of such complicated database integration efforts, through peer-reviewed papers, is recommended to foster reproducibility and future use of the integrated database. Here, we describe the major steps, challenges, and considerations in building an integrated database of lake ecosystems, called LAGOS (LAke multi-scaled GeOSpatial and temporal database), that was developed at the sub-continental study extent of 17 US states (1,800,000 km2). LAGOS includes two modules: LAGOSGEO, with geospatial data on every lake with surface area larger than 4 ha in the study extent (~50,000 lakes), including climate, atmospheric deposition, land use/cover, hydrology, geology, and topography measured across a range of spatial and temporal extents; and LAGOSLIMNO, with lake water quality data compiled from ~100 individual datasets for a subset of lakes in the study extent (~10,000 lakes). Procedures for the integration of datasets included: creating a flexible database design; authoring and integrating metadata; documenting data provenance; quantifying spatial measures of geographic data; quality-controlling integrated and derived data; and extensively documenting the database. Our procedures make a large, complex, and integrated database reproducible and extensible, allowing users to ask new research questions with the existing database or through the addition of new data. The largest challenge of this task was the heterogeneity of the data, formats, and metadata. Many steps of data integration need manual input from experts in diverse fields, requiring close collaboration.
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Affiliation(s)
- Patricia A Soranno
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Edward G Bissell
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Kendra S Cheruvelil
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Samuel T Christel
- Center for Limnology, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Sarah M Collins
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - C Emi Fergus
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Christopher T Filstrup
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011 USA
| | - Jean-Francois Lapierre
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Noah R Lottig
- Center for Limnology Trout Lake Station, University of Wisconsin-Madison, Boulder Junction, WI 54512 USA
| | - Samantha K Oliver
- Center for Limnology, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Caren E Scott
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Nicole J Smith
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Scott Stopyak
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Shuai Yuan
- School of Natural Sciences, Trinity College Dublin, Dublin, Ireland
| | - Mary Tate Bremigan
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - John A Downing
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011 USA
| | - Corinna Gries
- Center for Limnology, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Emily N Henry
- Oregon State University, Tillamook County, Tillamook, OR 97141 USA
| | - Nick K Skaff
- Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824 USA
| | - Emily H Stanley
- Center for Limnology, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Craig A Stow
- NOAA Great Lakes Laboratory, Ann Arbor, MI 48108 USA
| | - Pang-Ning Tan
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824 USA
| | - Tyler Wagner
- US Geological Survey, Pennsylvania Cooperative Fish and Wildlife Research Unit, Pennsylvania State University, University Park, PA 16802 USA
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Pfeiffer DU, Stevens KB. Spatial and temporal epidemiological analysis in the Big Data era. Prev Vet Med 2015; 122:213-20. [PMID: 26092722 PMCID: PMC7114113 DOI: 10.1016/j.prevetmed.2015.05.012] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 05/27/2015] [Accepted: 05/31/2015] [Indexed: 10/27/2022]
Abstract
Concurrent with global economic development in the last 50 years, the opportunities for the spread of existing diseases and emergence of new infectious pathogens, have increased substantially. The activities associated with the enormously intensified global connectivity have resulted in large amounts of data being generated, which in turn provides opportunities for generating knowledge that will allow more effective management of animal and human health risks. This so-called Big Data has, more recently, been accompanied by the Internet of Things which highlights the increasing presence of a wide range of sensors, interconnected via the Internet. Analysis of this data needs to exploit its complexity, accommodate variation in data quality and should take advantage of its spatial and temporal dimensions, where available. Apart from the development of hardware technologies and networking/communication infrastructure, it is necessary to develop appropriate data management tools that make this data accessible for analysis. This includes relational databases, geographical information systems and most recently, cloud-based data storage such as Hadoop distributed file systems. While the development in analytical methodologies has not quite caught up with the data deluge, important advances have been made in a number of areas, including spatial and temporal data analysis where the spectrum of analytical methods ranges from visualisation and exploratory analysis, to modelling. While there used to be a primary focus on statistical science in terms of methodological development for data analysis, the newly emerged discipline of data science is a reflection of the challenges presented by the need to integrate diverse data sources and exploit them using novel data- and knowledge-driven modelling methods while simultaneously recognising the value of quantitative as well as qualitative analytical approaches. Machine learning regression methods, which are more robust and can handle large datasets faster than classical regression approaches, are now also used to analyse spatial and spatio-temporal data. Multi-criteria decision analysis methods have gained greater acceptance, due in part, to the need to increasingly combine data from diverse sources including published scientific information and expert opinion in an attempt to fill important knowledge gaps. The opportunities for more effective prevention, detection and control of animal health threats arising from these developments are immense, but not without risks given the different types, and much higher frequency, of biases associated with these data.
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Affiliation(s)
- Dirk U Pfeiffer
- Veterinary Epidemiology, Economics & Public Health Group, Department of Production & Population Health, Royal Veterinary College, London, UK.
| | - Kim B Stevens
- Veterinary Epidemiology, Economics & Public Health Group, Department of Production & Population Health, Royal Veterinary College, London, UK
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27
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Jones AS, Horsburgh JS, Reeder SL, Ramírez M, Caraballo J. A data management and publication workflow for a large-scale, heterogeneous sensor network. ENVIRONMENTAL MONITORING AND ASSESSMENT 2015; 187:348. [PMID: 25968554 PMCID: PMC4429147 DOI: 10.1007/s10661-015-4594-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2015] [Accepted: 05/05/2015] [Indexed: 06/04/2023]
Abstract
It is common for hydrology researchers to collect data using in situ sensors at high frequencies, for extended durations, and with spatial distributions that produce data volumes requiring infrastructure for data storage, management, and sharing. The availability and utility of these data in addressing scientific questions related to water availability, water quality, and natural disasters relies on effective cyberinfrastructure that facilitates transformation of raw sensor data into usable data products. It also depends on the ability of researchers to share and access the data in useable formats. In this paper, we describe a data management and publication workflow and software tools for research groups and sites conducting long-term monitoring using in situ sensors. Functionality includes the ability to track monitoring equipment inventory and events related to field maintenance. Linking this information to the observational data is imperative in ensuring the quality of sensor-based data products. We present these tools in the context of a case study for the innovative Urban Transitions and Aridregion Hydrosustainability (iUTAH) sensor network. The iUTAH monitoring network includes sensors at aquatic and terrestrial sites for continuous monitoring of common meteorological variables, snow accumulation and melt, soil moisture, surface water flow, and surface water quality. We present the overall workflow we have developed for effectively transferring data from field monitoring sites to ultimate end-users and describe the software tools we have deployed for storing, managing, and sharing the sensor data. These tools are all open source and available for others to use.
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Affiliation(s)
- Amber Spackman Jones
- Utah Water Research Laboratory, Utah State University, 8200 Old Main Hill, Logan, UT, 84322-8200, USA,
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28
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Jones C, Warburton B, Carver J, Carver D. Potential applications of wireless sensor networks for wildlife trapping and monitoring programs. WILDLIFE SOC B 2015. [DOI: 10.1002/wsb.543] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
| | | | - Julian Carver
- Seradigm Limited, 38 Hillsborough Terrace; Christchurch 8022 New Zealand
| | - Derek Carver
- Seradigm Limited, 90 Landsdowne Terrace; Christchurch 8022 New Zealand
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29
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Peters DPC, Havstad KM, Cushing J, Tweedie C, Fuentes O, Villanueva-Rosales N. Harnessing the power of big data: infusing the scientific method with machine learning to transform ecology. Ecosphere 2014. [DOI: 10.1890/es13-00359.1] [Citation(s) in RCA: 92] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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30
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Vitte C, Fustier MA, Alix K, Tenaillon MI. The bright side of transposons in crop evolution. Brief Funct Genomics 2014; 13:276-95. [PMID: 24681749 DOI: 10.1093/bfgp/elu002] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
The past decades have revealed an unexpected yet prominent role of so-called 'junk DNA' in the regulation of gene expression, thereby challenging our view of the mechanisms underlying phenotypic evolution. In particular, several mechanisms through which transposable elements (TEs) participate in functional genome diversity have been depicted, bringing to light the 'TEs bright side'. However, the relative contribution of those mechanisms and, more generally, the importance of TE-based polymorphisms on past and present phenotypic variation in crops species remain poorly understood. Here, we review current knowledge on both issues, and discuss how analyses of massively parallel sequencing data combined with statistical methodologies and functional validations will help unravelling the impact of TEs on crop evolution in a near future.
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31
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Carpenter SR, Brock WA, Cole JJ, Pace ML. A new approach for rapid detection of nearby thresholds in ecosystem time series. OIKOS 2013. [DOI: 10.1111/j.1600-0706.2013.00539.x] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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32
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Campbell JL, Rustad LE, Porter JH, Taylor JR, Dereszynski EW, Shanley JB, Gries C, Henshaw DL, Martin ME, Sheldon WM, Boose ER. Quantity is Nothing without Quality: Automated QA/QC for Streaming Environmental Sensor Data. Bioscience 2013. [DOI: 10.1525/bio.2013.63.7.10] [Citation(s) in RCA: 80] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
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33
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Peterson EE, Ver Hoef JM, Isaak DJ, Falke JA, Fortin MJ, Jordan CE, McNyset K, Monestiez P, Ruesch AS, Sengupta A, Som N, Steel EA, Theobald DM, Torgersen CE, Wenger SJ. Modelling dendritic ecological networks in space: an integrated network perspective. Ecol Lett 2013; 16:707-19. [DOI: 10.1111/ele.12084] [Citation(s) in RCA: 153] [Impact Index Per Article: 13.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2012] [Revised: 10/31/2012] [Accepted: 01/14/2013] [Indexed: 11/26/2022]
Affiliation(s)
- Erin E. Peterson
- CSIRO Division of Mathematics; Informatics and Statistics; Dutton Park; QLD; Australia
| | | | - Dan J. Isaak
- USDA Forest Service; Rocky Mountain Research Station; Boise; ID; USA
| | - Jeffrey A. Falke
- Department of Fisheries and Wildlife; Oregon State University; Corvallis; OR; USA
| | - Marie-Josée Fortin
- Department of Ecology & Evolutionary Biology; University of Toronto; Toronto; ON; Canada
| | - Chris E. Jordan
- NOAA/NMFS/NWFSC Conservation Biology Division; Seattle; WA; USA
| | - Kristina McNyset
- Department of Fisheries and Wildlife; Oregon State University; Corvallis; OR; USA
| | - Pascal Monestiez
- Inra, Unité Biostatistique et Processus Spatiaux; Avignon; France
| | - Aaron S. Ruesch
- School of Environmental and Forest Sciences; University of Washington; Seattle; WA; USA
| | - Aritra Sengupta
- Department of Statistics; The Ohio State University; Columbus; OH; USA
| | - Nicholas Som
- Department of Forest Ecosystems and Society; Oregon State University; Corvallis; OR; USA
| | - E. Ashley Steel
- USDA Forest Service; Pacific Northwest Research Station; Seattle; WA; USA
| | - David M. Theobald
- Department of Fish; Wildlife & Conservation Biology; Colorado State University; Fort Collins; CO; USA
| | - Christian E. Torgersen
- U.S. Geological Survey; Forest and Rangeland Ecosystem Science Center; Cascadia Field Station; School of Environmental and Forest Sciences; University of Washington; Seattle; WA; USA
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Abstract
The wide availability of high-performance computing systems, Grids and Clouds, allowed scientists and engineers to implement more and more complex applications to access and process large data repositories and run scientific experiments in silico on distributed computing platforms. Most of these applications are designed as workflows that include data analysis, scientific computation methods, and complex simulation techniques. Scientific applications require tools and high-level mechanisms for designing and executing complex workflows. For this reason, in the past years, many efforts have been devoted towards the development of distributed workflow management systems for scientific applications. This paper discusses basic concepts of scientific workflows and presents workflow system tools and frameworks used today for the implementation of application in science and engineering on high-performance computers and distributed systems. In particular, the paper reports on a selection of workflow systems largely used for solving scientific problems and discusses some open issues and research challenges in the area.
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