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Schmidt A, Hines J, Türke M, Buscot F, Schädler M, Weigelt A, Gebler A, Klotz S, Liu T, Reth S, Trogisch S, Roy J, Wirth C, Eisenhauer N. The iDiv Ecotron-A flexible research platform for multitrophic biodiversity research. Ecol Evol 2021; 11:15174-15190. [PMID: 34765169 PMCID: PMC8571575 DOI: 10.1002/ece3.8198] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 08/31/2021] [Accepted: 09/06/2021] [Indexed: 11/17/2022] Open
Abstract
Across the globe, ecological communities are confronted with multiple global environmental change drivers, and they are responding in complex ways ranging from behavioral, physiological, and morphological changes within populations to changes in community composition and food web structure with consequences for ecosystem functioning. A better understanding of global change-induced alterations of multitrophic biodiversity and the ecosystem-level responses in terrestrial ecosystems requires holistic and integrative experimental approaches to manipulate and study complex communities and processes above and below the ground. We argue that mesocosm experiments fill a critical gap in this context, especially when based on ecological theory and coupled with microcosm experiments, field experiments, and observational studies of macroecological patterns. We describe the design and specifications of a novel terrestrial mesocosm facility, the iDiv Ecotron. It was developed to allow the setup and maintenance of complex communities and the manipulation of several abiotic factors in a near-natural way, while simultaneously measuring multiple ecosystem functions. To demonstrate the capabilities of the facility, we provide a case study. This study shows that changes in aboveground multitrophic interactions caused by decreased predator densities can have cascading effects on the composition of belowground communities. The iDiv Ecotrons technical features, which allow for the assembly of an endless spectrum of ecosystem components, create the opportunity for collaboration among researchers with an equally broad spectrum of expertise. In the last part, we outline some of such components that will be implemented in future ecological experiments to be realized in the iDiv Ecotron.
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Affiliation(s)
- Anja Schmidt
- Helmholtz Centre for Environmental Research – UFZHalle (Saale)Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Leipzig UniversityLeipzigGermany
| | - Jes Hines
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Leipzig UniversityLeipzigGermany
| | - Manfred Türke
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Leipzig UniversityLeipzigGermany
| | - François Buscot
- Helmholtz Centre for Environmental Research – UFZHalle (Saale)Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Martin Schädler
- Helmholtz Centre for Environmental Research – UFZHalle (Saale)Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
| | - Alexandra Weigelt
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Leipzig UniversityLeipzigGermany
| | - Alban Gebler
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Leipzig UniversityLeipzigGermany
| | - Stefan Klotz
- Helmholtz Centre for Environmental Research – UFZHalle (Saale)Germany
| | - Tao Liu
- Key Laboratory of Vegetation Restoration and Management of Degraded EcosystemsSouth China Botanical GardenChinese Academy of SciencesGuangzhouChina
| | - Sascha Reth
- Umwelt‐Geräte‐Technik GmbH – UGTMünchebergGermany
| | - Stefan Trogisch
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Martin Luther University Halle‐WittenbergHalle (Saale)Germany
| | - Jacques Roy
- French National Centre for Scientific Research – CNRSParisFrance
| | - Christian Wirth
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Leipzig UniversityLeipzigGermany
| | - Nico Eisenhauer
- German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐LeipzigLeipzigGermany
- Leipzig UniversityLeipzigGermany
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Zhu Y, Sun G, Ding G, Zhou J, Wen M, Jin S, Zhao Q, Colmer J, Ding Y, Ober ES, Zhou J. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. PLANT PHYSIOLOGY 2021; 187:716-738. [PMID: 34608970 PMCID: PMC8491082 DOI: 10.1093/plphys/kiab324] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 06/22/2021] [Indexed: 05/12/2023]
Abstract
Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.
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Affiliation(s)
- Yulei Zhu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Gang Sun
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Guohui Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Mingxing Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Zhenjiang Institute of Agricultural Science in Hill Area of Jiangsu Province, Jurong 212400, China
| | - Shichao Jin
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Joshua Colmer
- Earlham Institute, Norwich Research Park, Norwich NR4 7UH, UK
| | - Yanfeng Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Eric S. Ober
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Ji Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
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Watt M, Fiorani F, Usadel B, Rascher U, Muller O, Schurr U. Phenotyping: New Windows into the Plant for Breeders. ANNUAL REVIEW OF PLANT BIOLOGY 2020; 71:689-712. [PMID: 32097567 DOI: 10.1146/annurev-arplant-042916-041124] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Plant phenotyping enables noninvasive quantification of plant structure and function and interactions with environments. High-capacity phenotyping reaches hitherto inaccessible phenotypic characteristics. Diverse, challenging, and valuable applications of phenotyping have originated among scientists, prebreeders, and breeders as they study the phenotypic diversity of genetic resources and apply increasingly complex traits to crop improvement. Noninvasive technologies are used to analyze experimental and breeding populations. We cover the most recent research in controlled-environment and field phenotyping for seed, shoot, and root traits. Select field phenotyping technologies have become state of the art and show promise for speeding up the breeding process in early generations. We highlight the technologies behind the rapid advances in proximal and remote sensing of plants in fields. We conclude by discussing the new disciplines working with the phenotyping community: data science, to address the challenge of generating FAIR (findable, accessible, interoperable, and reusable) data, and robotics, to apply phenotyping directly on farms.
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Affiliation(s)
- Michelle Watt
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Fabio Fiorani
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Björn Usadel
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
- Institute for Botany and Molecular Genetics, BioSC, RWTH Aachen University, 52074 Aachen, Germany
| | - Uwe Rascher
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Onno Muller
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
| | - Ulrich Schurr
- IBG-2: Plant Sciences, Institute of Bio- and Geosciences, Forschungszentrum Jülich, 52425 Jülich, Germany; ,
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Millar AJ, Urquiza U, Freeman PL, Hume A, Plotkin GD, Sorokina O, Zardilis A, Zielinski T. Practical steps to digital organism models, from laboratory model species to 'Crops in silico. JOURNAL OF EXPERIMENTAL BOTANY 2019; 70:2403-2418. [PMID: 30615184 DOI: 10.1093/jxb/ery435] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 11/28/2018] [Indexed: 05/20/2023]
Abstract
A recent initiative named 'Crops in silico' proposes that multi-scale models 'have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts' in plant science, particularly directed to crop species. To that end, the group called for 'a paradigm shift in plant modelling, from largely isolated efforts to a connected community'. 'Wet' (experimental) research has been especially productive in plant science, since the adoption of Arabidopsis thaliana as a laboratory model species allowed the emergence of an Arabidopsis research community. Parts of this community invested in 'dry' (theoretical) research, under the rubric of Systems Biology. Our past research combined concepts from Systems Biology and crop modelling. Here we outline the approaches that seem most relevant to connected, 'digital organism' initiatives. We illustrate the scale of experimental research required, by collecting the kinetic parameter values that are required for a quantitative, dynamic model of a gene regulatory network. By comparison with the Systems Biology Markup Language (SBML) community, we note computational resources and community structures that will help to realize the potential for plant Systems Biology to connect with a broader crop science community.
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Affiliation(s)
- Andrew J Millar
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Uriel Urquiza
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | | | - Alastair Hume
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
- EPCC, Bayes Centre, University of Edinburgh, Edinburgh, UK
| | - Gordon D Plotkin
- Laboratory for the Foundations of Computer Science, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Oxana Sorokina
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK
| | - Argyris Zardilis
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
| | - Tomasz Zielinski
- SynthSys and School of Biological Sciences, University of Edinburgh, Edinburgh, UK
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Reynolds D, Ball J, Bauer A, Davey R, Griffiths S, Zhou J. CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. Gigascience 2019; 8:giz009. [PMID: 30715329 PMCID: PMC6423370 DOI: 10.1093/gigascience/giz009] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2018] [Revised: 12/18/2018] [Accepted: 01/15/2019] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND High-quality plant phenotyping and climate data lay the foundation for phenotypic analysis and genotype-environment interaction, providing important evidence not only for plant scientists to understand the dynamics between crop performance, genotypes, and environmental factors but also for agronomists and farmers to closely monitor crops in fluctuating agricultural conditions. With the rise of Internet of Things technologies (IoT) in recent years, many IoT-based remote sensing devices have been applied to plant phenotyping and crop monitoring, which are generating terabytes of biological datasets every day. However, it is still technically challenging to calibrate, annotate, and aggregate the big data effectively, especially when they were produced in multiple locations and at different scales. FINDINGS CropSight is a PHP Hypertext Pre-processor and structured query language-based server platform that provides automated data collation, storage, and information management through distributed IoT sensors and phenotyping workstations. It provides a two-component solution to monitor biological experiments through networked sensing devices, with interfaces specifically designed for distributed plant phenotyping and centralized data management. Data transfer and annotation are accomplished automatically through an hypertext transfer protocol-accessible RESTful API installed on both device side and server side of the CropSight system, which synchronize daily representative crop growth images for visual-based crop assessment and hourly microclimate readings for GxE studies. CropSight also supports the comparison of historical and ongoing crop performance while different experiments are being conducted. CONCLUSIONS As a scalable and open-source information management system, CropSight can be used to maintain and collate important crop performance and microclimate datasets captured by IoT sensors and distributed phenotyping installations. It provides near real-time environmental and crop growth monitoring in addition to historical and current experiment comparison through an integrated cloud-ready server system. Accessible both locally in the field through smart devices and remotely in an office using a personal computer, CropSight has been applied to field experiments of bread wheat prebreeding since 2016 and speed breeding since 2017. We believe that the CropSight system could have a significant impact on scalable plant phenotyping and IoT-style crop management to enable smart agricultural practices in the near future.
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Affiliation(s)
- Daniel Reynolds
- Engineering Biology, Earlham Institute, Norwich Research Park, Colney Lane, Norwich, NR4 7UZ, UK
| | - Joshua Ball
- Engineering Biology, Earlham Institute, Norwich Research Park, Colney Lane, Norwich, NR4 7UZ, UK
| | - Alan Bauer
- Engineering Biology, Earlham Institute, Norwich Research Park, Colney Lane, Norwich, NR4 7UZ, UK
- Plant Phenomics Research Center, China-UK Plant Phenomics Research Centre, Nanjing Agricultural University, No 1, Weigang, Nanjing, Jiangsu Province, China, 210095
| | - Robert Davey
- Engineering Biology, Earlham Institute, Norwich Research Park, Colney Lane, Norwich, NR4 7UZ, UK
| | - Simon Griffiths
- Crop Genetics, John Innes Centre, Norwich Research Park, Colney Lane, Norwich, NR4 7UH, UK
| | - Ji Zhou
- Engineering Biology, Earlham Institute, Norwich Research Park, Colney Lane, Norwich, NR4 7UZ, UK
- Plant Phenomics Research Center, China-UK Plant Phenomics Research Centre, Nanjing Agricultural University, No 1, Weigang, Nanjing, Jiangsu Province, China, 210095
- University of East Anglia, Norwich Research Park, Norwich, NT4 7TJ, UK
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