1
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Dantzer B, Mabry KE, Bernhardt JR, Cox RM, Francis CD, Ghalambor CK, Hoke KL, Jha S, Ketterson E, Levis NA, McCain KM, Patricelli GL, Paull SH, Pinter-Wollman N, Safran RJ, Schwartz TS, Throop HL, Zaman L, Martin LB. Understanding Organisms Using Ecological Observatory Networks. Integr Org Biol 2023; 5:obad036. [PMID: 37867910 PMCID: PMC10586040 DOI: 10.1093/iob/obad036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 06/07/2023] [Accepted: 09/21/2023] [Indexed: 10/24/2023] Open
Abstract
Human activities are rapidly changing ecosystems around the world. These changes have widespread implications for the preservation of biodiversity, agricultural productivity, prevalence of zoonotic diseases, and sociopolitical conflict. To understand and improve the predictive capacity for these and other biological phenomena, some scientists are now relying on observatory networks, which are often composed of systems of sensors, teams of field researchers, and databases of abiotic and biotic measurements across multiple temporal and spatial scales. One well-known example is NEON, the US-based National Ecological Observatory Network. Although NEON and similar networks have informed studies of population, community, and ecosystem ecology for years, they have been minimally used by organismal biologists. NEON provides organismal biologists, in particular those interested in NEON's focal taxa, with an unprecedented opportunity to study phenomena such as range expansions, disease epidemics, invasive species colonization, macrophysiology, and other biological processes that fundamentally involve organismal variation. Here, we use NEON as an exemplar of the promise of observatory networks for understanding the causes and consequences of morphological, behavioral, molecular, and physiological variation among individual organisms.
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Affiliation(s)
- B Dantzer
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109,USA
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109,USA
| | - K E Mabry
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109,USA
- Department of Biology, New Mexico State University, Las Cruces, NM 88003,USA
| | - J R Bernhardt
- Department of Biology, New Mexico State University, Las Cruces, NM 88003,USA
- Department of Integrative Biology, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - R M Cox
- Department of Biology, University of Virginia, Charlottesville, VA 22940,USA
- Department of Biological Sciences, California Polytechnic State University, San Luis Obispo, CA 93407,USA
| | - C D Francis
- Department of Biological Sciences, California Polytechnic State University, San Luis Obispo, CA 93407,USA
- Department of Biology, Centre for Biodiversity Dynamics (CBD), Norwegian University of Science and Technology (NTNU), N‐7491 Trondheim, Norway
| | - C K Ghalambor
- Department of Biology, Centre for Biodiversity Dynamics (CBD), Norwegian University of Science and Technology (NTNU), N‐7491 Trondheim, Norway
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - K L Hoke
- Department of Biology, Colorado State University, Fort Collins, CO 80523, USA
| | - S Jha
- Department of Integrative Biology, University of Texas at Austin, Austin, TX 78712,USA
| | - E Ketterson
- Department of Biology, Indiana University, 1001 E. Third Street, Bloomington, IN 47405,USA
| | - N A Levis
- Department of Biology, Indiana University, 1001 E. Third Street, Bloomington, IN 47405,USA
| | - K M McCain
- Global Health and Infectious Disease Research Center, College of Public Health, University of South Florida, Tampa, FL 33612,USA
| | - G L Patricelli
- Department of Evolution and Ecology, University of California, Davis, CA 95616,USA
| | - S H Paull
- Battelle, National Ecological Observatory Network, 1685 38th Street, Boulder, CO 80301, USA
| | - N Pinter-Wollman
- Department of Ecology and Evolutionary Biology, University of California Los Angeles, 621 Charles E. Young Drive South, Los Angeles, CA 90095, USA
| | - R J Safran
- Department of Ecology and Evolutionary Biology, University of Colorado, Boulder 80309,USA
| | - T S Schwartz
- Department of Biological Sciences, Auburn University, Auburn, AL 36849, USA
| | - H L Throop
- School of Earth and Space Exploration and School of Life Sciences, Arizona State University, Tempe, AZ 85287, USA
| | - L Zaman
- Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI 48109,USA
- Center for the Study of Complex Systems, University of Michigan, Ann Arbor, MI 48109, USA
| | - L B Martin
- Global Health and Infectious Disease Research Center and Center for Genomics, College of Public Health, University of South Florida, Tampa, FL 33612,USA
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2
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Besson M, Alison J, Bjerge K, Gorochowski TE, Høye TT, Jucker T, Mann HMR, Clements CF. Towards the fully automated monitoring of ecological communities. Ecol Lett 2022; 25:2753-2775. [PMID: 36264848 PMCID: PMC9828790 DOI: 10.1111/ele.14123] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/09/2022] [Accepted: 09/06/2022] [Indexed: 01/12/2023]
Abstract
High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.
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Affiliation(s)
- Marc Besson
- School of Biological SciencesUniversity of BristolBristolUK,Sorbonne Université CNRS UMR Biologie des Organismes Marins, BIOMBanyuls‐sur‐MerFrance
| | - Jamie Alison
- Department of EcoscienceAarhus UniversityAarhusDenmark,UK Centre for Ecology & HydrologyBangorUK
| | - Kim Bjerge
- Department of Electrical and Computer EngineeringAarhus UniversityAarhusDenmark
| | - Thomas E. Gorochowski
- School of Biological SciencesUniversity of BristolBristolUK,BrisEngBio, School of ChemistryUniversity of BristolCantock's CloseBristolBS8 1TSUK
| | - Toke T. Høye
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
| | - Tommaso Jucker
- School of Biological SciencesUniversity of BristolBristolUK
| | - Hjalte M. R. Mann
- Department of EcoscienceAarhus UniversityAarhusDenmark,Arctic Research CentreAarhus UniversityAarhusDenmark
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4
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Sturtevant C, DeRego E, Metzger S, Ayres E, Allen D, Burlingame T, Catolico N, Cawley K, Csavina J, Durden D, Florian C, Frost S, Gaddie R, Knapp E, Laney C, Lee R, Lenz D, Litt G, Luo H, Roberti J, Slemmons C, Styers K, Tran C, Vance T, SanClements M. A process approach to quality management doubles
NEON
sensor data quality. Methods Ecol Evol 2022. [DOI: 10.1111/2041-210x.13943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Cove Sturtevant
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Elizabeth DeRego
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Stefan Metzger
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Edward Ayres
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Dan Allen
- National Ecological Observatory Network, Battelle Boulder CO USA
| | | | - Nora Catolico
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Kaelin Cawley
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Janae Csavina
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - David Durden
- National Ecological Observatory Network, Battelle Boulder CO USA
| | | | - Shalane Frost
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Ross Gaddie
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Elizabeth Knapp
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Christine Laney
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Robert Lee
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Dawn Lenz
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Guy Litt
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Hongyan Luo
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Joshua Roberti
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Caleb Slemmons
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Kevin Styers
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Chau Tran
- National Ecological Observatory Network, Battelle Boulder CO USA
| | - Tanya Vance
- National Ecological Observatory Network, Battelle Boulder CO USA
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5
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Curating 62 Years of Walnut Gulch Experimental Watershed Data: Improving the Quality of Long-Term Rainfall and Runoff Datasets. WATER 2022. [DOI: 10.3390/w14142198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The curation of hydrologic data includes quality control, documentation, database development, and provisions for public access. This article describes the development of new quality control procedures for experimental watersheds like the Walnut Gulch Experimental Watersheds (WGEW). WGEW is a 149 km2 watershed outdoor hydrologic laboratory equipped with a dense network of hydro-climatic instruments since the 1950s. To improve data accuracy from the constantly growing instrumentation networks in numerous experimental watersheds, we developed five new QAQC tools based on fundamental hydrologic principles. The tools include visual analysis of interpolated rainfall maps and evaluating temporal, spatial, and quantitative relationships between paired rainfall-runoff events, including runoff lag time, runoff coefficients, multiple regression, and association methods. The methods identified questionable rainfall and runoff observations in the WGEW database that were not usually captured by the existing QAQC procedures. The new tools were evaluated and confirmed using existing metadata, paper charts, and graphical visualization tools. It was found that 13% of the days (n = 780) with rainfall and 7% of the runoff events sampled had errors. Omitting these events improved the quality and reliability of the WGEW dataset for hydrologic modeling and analyses. This indicated the effectiveness of application of conventional hydrologic relations to improve the QAQC strategy for experimental watershed datasets.
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6
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Dynamic Differential Game Strategy of the Energy Big Data Ecosystem Considering Technological Innovation. SUSTAINABILITY 2022. [DOI: 10.3390/su14127158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study discusses how to create strategic value through energy big data and how to promote stakeholder interaction mechanisms in the evolution of the energy big data ecosystem. We use differential game methods to study the interaction between one power grid enterprise (PG) and one technology supplier (TS) under three different cost-sharing contracts: without cost-sharing contract, cost-sharing contract, and two-way subsidy contract. The effectiveness of the dynamic equilibrium strategies under different contracts is verified via numerical simulations. The results show that under the centralized decision scenario, the technological innovation investment, the degree of technological advancement of PG and TS, and the total profit of the supply chain system are superior to the decentralized decision scenario. The extent of TS technology innovation investment depends on the share rate of PG. Technology innovation investment and the profits of energy big data service supply chain stakeholders will increase with the sensitivity coefficient of technological advancement. Compared with contracts without cost-sharing and with cost-sharing, the two-way subsidy contract can provide the Pareto optimal solution for the investment trajectory of technological innovation and long-term profits. Theoretically, this study reveals a new perspective in the research on the relationship between power grid enterprises and technology suppliers under dynamic technology innovation. In practice, this study facilitates power grid enterprises and technology suppliers to form a closer cooperative relationship in the energy big data ecosystem. More importantly, it is helpful for power grid enterprises to make optimal transaction decisions at different stages of energy big data ecosystem evolution.
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