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Du L, Pang Y. Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data. PLANT PHENOMICS (WASHINGTON, D.C.) 2024; 6:0145. [PMID: 38332900 PMCID: PMC10851578 DOI: 10.34133/plantphenomics.0145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 01/08/2024] [Indexed: 02/10/2024]
Abstract
Identifying the spatiotemporal distributions and phenotypic characteristics of understory saplings is beneficial in exploring the internal mechanisms of plant regeneration and providing technical assistances for continues cover forest management. However, it is challenging to detect the understory saplings using 2-dimensional (2D) spectral information produced by conventional optical remotely sensed data. This study proposed an automatic method to detect the regenerated understory saplings based on the 3D structural information from aerial laser scanning (ALS) data. By delineating individual tree crown using the improved spectral clustering algorithm, we successfully removed the overstory canopy and associated trunk points. Then, individual understory saplings were segmented using an adaptive-mean-shift-based clustering algorithm. This method was tested in an experimental forest farm of North China. Our results showed that the detection rates of understory saplings ranged from 94.41% to 152.78%, and the matching rates increased from 62.59% to 95.65% as canopy closure went down. The ALS-based sapling heights well captured the variations of field measurements [R2 = 0.71, N = 3,241, root mean square error (RMSE) = 0.26 m, P < 0.01] and terrestrial laser scanning (TLS)-based measurements (R2 = 0.78, N =443, RMSE = 0.23 m, P < 0.01). The ALS-based sapling crown width was comparable with TLS-based measurements (R2 = 0.64, N = 443, RMSE = 0.24 m). This study provides a solution for the quantification of understory saplings, which can be used to improve forest ecosystem resilence through regulating the dynamics of forest gaps to better utilize light resources.
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Affiliation(s)
- Liming Du
- Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
- Key Laboratory of Forestry Remote Sensing and Information System,
National Forestry and Grassland Administration, Beijing 100091, China
| | - Yong Pang
- Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
- Key Laboratory of Forestry Remote Sensing and Information System,
National Forestry and Grassland Administration, Beijing 100091, China
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Grubinger S, Coops NC, O'Neill GA. Picturing local adaptation: Spectral and structural traits from drone remote sensing reveal clinal responses to climate transfer in common-garden trials of interior spruce (Picea engelmannii × glauca). GLOBAL CHANGE BIOLOGY 2023; 29:4842-4860. [PMID: 37424219 DOI: 10.1111/gcb.16855] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/12/2023] [Revised: 06/01/2023] [Accepted: 06/13/2023] [Indexed: 07/11/2023]
Abstract
Common-garden trials of forest trees provide phenotype data used to assess growth and local adaptation; this information is foundational to tree breeding programs, genecology, and gene conservation. As jurisdictions consider assisted migration strategies to match populations to suitable climates, in situ progeny and provenance trials provide experimental evidence of adaptive responses to climate change. We used drone technology, multispectral imaging, and digital aerial photogrammetry to quantify spectral traits related to stress, photosynthesis, and carotenoids, and structural traits describing crown height, size, and complexity at six climatically disparate common-garden trials of interior spruce (Picea engelmannii × glauca) in western Canada. Through principal component analysis, we identified key components of climate related to temperature, moisture, and elevational gradients. Phenotypic clines in remotely sensed traits were analyzed as trait correlations with provenance climate transfer distances along principal components (PCs). We used traits showing clinal variation to model best linear unbiased predictions for tree height (R2 = .98-.99, root mean square error [RMSE] = 0.06-0.10 m) and diameter at breast height (DBH, R2 = .71-.97, RMSE = 2.57-3.80 mm) and generated multivariate climate transfer functions with the model predictions. Significant (p < .05) clines were present for spectral traits at all sites along all PCs. Spectral traits showed stronger clinal variation than structural traits along temperature and elevational gradients and along moisture gradients at wet, coastal sites, but not at dry, interior sites. Spectral traits may capture patterns of local adaptation to temperature and montane growing seasons which are distinct from moisture-limited patterns in stem growth. This work demonstrates that multispectral indices improve the assessment of local adaptation and that spectral and structural traits from drone remote sensing produce reliable proxies for ground-measured height and DBH. This phenotyping framework contributes to the analysis of common-garden trials towards a mechanistic understanding of local adaptation to climate.
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Affiliation(s)
- Samuel Grubinger
- Faculty of Forestry, Integrated Remote Sensing Studio, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nicholas C Coops
- Faculty of Forestry, Integrated Remote Sensing Studio, University of British Columbia, Vancouver, British Columbia, Canada
| | - Gregory A O'Neill
- BC Ministry of Forests, Kalamalka Forestry Centre, Vernon, British Columbia, Canada
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Tian Z, Yang Z, Lu Z, Luo B, Hao Y, Wang X, Yang F, Wang S, Chen C, Dong R. Effect of genotype and environment on agronomical characters of alfalfa (Medicago sativa L.) in a typical acidic soil environment in southwest China. FRONTIERS IN SUSTAINABLE FOOD SYSTEMS 2023. [DOI: 10.3389/fsufs.2023.1144061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023] Open
Abstract
Alfalfa (Medicago sativa L.), an important perennial legume forage crop with high nutritional value and forage yield, is widely used in animal husbandry. However, it is very sensitive to aluminum, which severely limits its growth in acidic soils. In this study, we analyzed the genotype variation of each agronomic trait in 44 alfalfa varieties in two acidic soil environments. Then, analysis of variance (ANOVA) of the variance components was performed using the Residual Maximum Likelihood (REML). The best linear unbiased predictor analysis was used to obtain the mean trait of each variety, and the mean values were used to construct the mean matrix of varieties × traits and interaction analysis of varieties × years. The results showed that there was significant (P < 0.05) genotypic variation for each trait of the 44 varieties and the genetic diversity was abundant. The average repeatability (R value) of interannual plant height (PH), stem thickness (ST), number of branches (NS), fresh weight (FW), total fresh weight (TFW), and total dry weight (TDW) was high (0.21–0.34), whereas the genetics were relatively stable. PH, NS, FW, TFW, and dry weight (DW) were positively correlated (P < 0.01) with TDW. Six alfalfa varieties (Algonquin, Xinjiang daye, Trifecta, Vernal, WL354HQ, and Boja) with excellent TDW and TFW were identified in different years, environmental regions, and climatic altitudes. Our research results can provide suggestions and critical information regarding the future improvement and development of new alfalfa strains and varieties that are resistant to acidic soil conditions.
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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De Marco A, Sicard P, Feng Z, Agathokleous E, Alonso R, Araminiene V, Augustatis A, Badea O, Beasley JC, Branquinho C, Bruckman VJ, Collalti A, David‐Schwartz R, Domingos M, Du E, Garcia Gomez H, Hashimoto S, Hoshika Y, Jakovljevic T, McNulty S, Oksanen E, Omidi Khaniabadi Y, Prescher A, Saitanis CJ, Sase H, Schmitz A, Voigt G, Watanabe M, Wood MD, Kozlov MV, Paoletti E. Strategic roadmap to assess forest vulnerability under air pollution and climate change. GLOBAL CHANGE BIOLOGY 2022; 28:5062-5085. [PMID: 35642454 PMCID: PMC9541114 DOI: 10.1111/gcb.16278] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 03/02/2022] [Accepted: 05/18/2022] [Indexed: 05/13/2023]
Abstract
Although it is an integral part of global change, most of the research addressing the effects of climate change on forests have overlooked the role of environmental pollution. Similarly, most studies investigating the effects of air pollutants on forests have generally neglected the impacts of climate change. We review the current knowledge on combined air pollution and climate change effects on global forest ecosystems and identify several key research priorities as a roadmap for the future. Specifically, we recommend (1) the establishment of much denser array of monitoring sites, particularly in the South Hemisphere; (2) further integration of ground and satellite monitoring; (3) generation of flux-based standards and critical levels taking into account the sensitivity of dominant forest tree species; (4) long-term monitoring of N, S, P cycles and base cations deposition together at global scale; (5) intensification of experimental studies, addressing the combined effects of different abiotic factors on forests by assuring a better representation of taxonomic and functional diversity across the ~73,000 tree species on Earth; (6) more experimental focus on phenomics and genomics; (7) improved knowledge on key processes regulating the dynamics of radionuclides in forest systems; and (8) development of models integrating air pollution and climate change data from long-term monitoring programs.
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Affiliation(s)
| | | | - Zhaozhong Feng
- Key Laboratory of Agro‐Meteorology of Jiangsu Province, School of Applied MeteorologyNanjing University of Information Science & TechnologyNanjingChina
| | - Evgenios Agathokleous
- Key Laboratory of Agro‐Meteorology of Jiangsu Province, School of Applied MeteorologyNanjing University of Information Science & TechnologyNanjingChina
| | - Rocio Alonso
- Ecotoxicology of Air Pollution, CIEMATMadridSpain
| | - Valda Araminiene
- Lithuanian Research Centre for Agriculture and ForestryKaunasLithuania
| | - Algirdas Augustatis
- Faculty of Forest Sciences and EcologyVytautas Magnus UniversityKaunasLithuania
| | - Ovidiu Badea
- “Marin Drăcea” National Institute for Research and Development in ForestryVoluntariRomania
- Faculty of Silviculture and Forest Engineering“Transilvania” UniversityBraşovRomania
| | - James C. Beasley
- Savannah River Ecology Laboratory and Warnell School of Forestry and Natural ResourcesUniversity of GeorgiaAikenSouth CarolinaUSA
| | - Cristina Branquinho
- Centre for Ecology, Evolution and Environmental Changes, Faculdade de CiênciasUniversidade de LisboaLisbonPortugal
| | - Viktor J. Bruckman
- Commission for Interdisciplinary Ecological StudiesAustrian Academy of SciencesViennaAustria
| | | | | | - Marisa Domingos
- Instituto de BotanicaNucleo de Pesquisa em EcologiaSao PauloBrazil
| | - Enzai Du
- Faculty of Geographical ScienceBeijing Normal UniversityBeijingChina
| | | | - Shoji Hashimoto
- Department of Forest SoilsForestry and Forest Products Research InstituteTsukubaJapan
| | | | | | | | - Elina Oksanen
- Department of Environmental and Biological SciencesUniversity of Eastern FinlandJoensuuFinland
| | - Yusef Omidi Khaniabadi
- Department of Environmental Health EngineeringIndustrial Medial and Health, Petroleum Industry Health Organization (PIHO)AhvazIran
| | | | - Costas J. Saitanis
- Lab of Ecology and Environmental ScienceAgricultural University of AthensAthensGreece
| | - Hiroyuki Sase
- Ecological Impact Research DepartmentAsia Center for Air Pollution Research (ACAP)NiigataJapan
| | - Andreas Schmitz
- State Agency for Nature, Environment and Consumer Protection of North Rhine‐WestphaliaRecklinghausenGermany
| | | | - Makoto Watanabe
- Institute of AgricultureTokyo University of Agriculture and Technology (TUAT)FuchuJapan
| | - Michael D. Wood
- School of Science, Engineering and EnvironmentUniversity of SalfordSalfordUK
| | | | - Elena Paoletti
- Department of Forest SoilsForestry and Forest Products Research InstituteTsukubaJapan
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Nguyen PT, Shi F, Wang J, Badenhorst PE, Spangenberg GC, Smith KF, Daetwyler HD. Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data. FRONTIERS IN PLANT SCIENCE 2022; 13:950720. [PMID: 36003811 PMCID: PMC9393552 DOI: 10.3389/fpls.2022.950720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: F M ~ L V ∗ L V _ D e n ∗ N D V I had a determination of coefficient R 2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables- F M ~ L V ∗ L V _ D e n . In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.
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Affiliation(s)
- Phat T. Nguyen
- School of Applied System Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Fan Shi
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Junping Wang
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, Australia
| | | | - German C. Spangenberg
- School of Applied System Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Kevin F. Smith
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, Australia
- Faculty of Veterinary and Agricultural Sciences, School of Agriculture and Food, The University of Melbourne, Melbourne, VIC, Australia
| | - Hans D. Daetwyler
- School of Applied System Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
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du Toit F, Coops NC, Ratcliffe B, El-Kassaby YA. Generating Douglas-fir Breeding Value Estimates Using Airborne Laser Scanning Derived Height and Crown Metrics. FRONTIERS IN PLANT SCIENCE 2022; 13:893017. [PMID: 35909722 PMCID: PMC9330362 DOI: 10.3389/fpls.2022.893017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 06/10/2022] [Indexed: 06/15/2023]
Abstract
Progeny test trials in British Columbia are essential in assessing the genetic performance via the prediction of breeding values (BVs) for target phenotypes of parent trees and their offspring. Accurate and timely collection of phenotypic data is critical for estimating BVs with confidence. Airborne Laser Scanning (ALS) data have been used to measure tree height and structure across a wide range of species, ages and environments globally. Here, we analyzed a Coastal Douglas-fir [Pseudotsuga menziesii var. menziesii (Mirb.)] progeny test trial located in British Columbia, Canada, using individual tree high-density Airborne Laser Scanning (ALS) metrics and traditional ground-based phenotypic observations. Narrow-sense heritability, genetic correlations, and BVs were estimated using pedigree-based single and multi-trait linear models for 43 traits. Comparisons of genetic parameter estimates between ALS metrics and traditional ground-based measures and single- and multi-trait models were conducted based on the accuracy and precision of the estimates. BVs were estimated for two ALS models (ALSCAN and ALSACC) representing two model-building approaches and compared to a baseline model using field-measured traits. The ALSCAN model used metrics reflecting aspects of vertical distribution of biomass within trees, while ALSACC represented the most statistically accurate model. We report that the accuracy of both the ALSCAN (0.8239) and ALSACC (0.8254) model-derived BVs for mature tree height is a suitable proxy for ground-based mature tree height BVs (0.8316). Given the cost efficiency of ALS, forest geneticists should explore this technology as a viable tool to increase breeding programs' overall efficiency and cost savings.
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Affiliation(s)
- Francois du Toit
- Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
| | - Nicholas C. Coops
- Department of Forest Resources Management, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British Columbia, Vancouver, BC, Canada
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Assessing the Potential of Backpack-Mounted Mobile Laser Scanning Systems for Tree Phenotyping. REMOTE SENSING 2022. [DOI: 10.3390/rs14143344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Phenotyping has been a reality for aiding the selection of optimal crops for specific environments for decades in various horticultural industries. However, until recently, phenotyping was less accessible to tree breeders due to the size of the crop, the length of the rotation and the difficulty in acquiring detailed measurements. With the advent of affordable and non-destructive technologies, such as mobile laser scanners (MLS), phenotyping of mature forests is now becoming practical. Despite the potential of MLS technology, few studies included detailed assessments of its accuracy in mature plantations. In this study, we assessed a novel, high-density MLS operated below canopy for its ability to derive phenotypic measurements from mature Pinus radiata. MLS data were co-registered with above-canopy UAV laser scanner (ULS) data and imported to a pipeline that segments individual trees from the point cloud before extracting tree-level metrics. The metrics studied include tree height, diameter at breast height (DBH), stem volume and whorl characteristics. MLS-derived tree metrics were compared to field measurements and metrics derived from ULS alone. Our pipeline was able to segment individual trees with a success rate of 90.3%. We also observed strong agreement between field measurements and MLS-derived DBH (R2 = 0.99, RMSE = 5.4%) and stem volume (R2 = 0.99, RMSE = 10.16%). Additionally, we proposed a new variable height method for deriving DBH to avoid swelling, with an overall accuracy of 52% for identifying the correct method for where to take the diameter measurement. A key finding of this study was that MLS data acquired from below the canopy was able to derive canopy heights with a level of accuracy comparable to a high-end ULS scanner (R2 = 0.94, RMSE = 3.02%), negating the need for capturing above-canopy data to obtain accurate canopy height models. Overall, the findings of this study demonstrate that even in mature forests, MLS technology holds strong potential for advancing forest phenotyping and tree measurement.
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Phenotypic Traits Extraction and Genetic Characteristics Assessment of Eucalyptus Trials Based on UAV-Borne LiDAR and RGB Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14030765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Phenotype describes the physical, physiological and biochemical characteristics of organisms that are determined or influenced by genes and environment. Accurate extraction of phenotypic data is a prerequisite for comprehensive forest phenotyping in order to improve the growth and development of forest plantations. Combined with the assessments of genetic characteristics, forest phenotyping will help to accelerate the breeding process, improve stress resistance and enhance the quality of the planted forest. In this study, we disposed our study in Eucalyptus trials within the Gaofeng forest farm (a typical Eucalyptus plantation site in southern China) for a high-throughput phenotypic traits extraction and genetic characteristics analysis based on high-density point clouds (acquired by a UAV-borne LiDAR sensor) and high-resolution RGB images (acquired by a UAV-borne camera), aiming at developing a high-resolution and high-throughput UAV-based phenotyping approach for tree breeding. First, we compared the effect of CHM-based Marker-Controlled Watershed Segmentation (MWS) and Point Cloud-based Cluster Segmentation (PCS) for extracting individual trees; Then, the phenotypic traits (i.e., tree height, diameter at breast height, crown width), the structural metrics (n = 19) and spectral indices (n = 9) of individual trees were extracted and assessed; Finally, a genetic characteristics analysis was carried out based on the above results, and we compared the differences between high-throughput phenotyping by UAV-based data and on manual measurements. Results showed that: in the relatively low stem density site of the trial (760 n/ha), the overall accuracy of MWS and PCS was similar, while in the higher stem density sites (982 n/ha, 1239 n/ha), the overall accuracy of MWS (F(2) = 0.93, F(3) = 0.86) was higher than PCS (F(2) = 0.84, F(3) = 0.74); With the increase of stem density, the difference between the overall accuracy of MWS and PCS gradually expanded. Both UAV–LiDAR extracted phenotypic traits and manual measurements were significantly different across the Eucalyptus clones (P < 0.05), as were most of the structural metrics (47/57) and spectral indices (26/27), revealing the genetic divergence between the clones. The rank of clones demonstrated that the pure clones (of E. urophylla), the hybrid clones (of E. urophylla as the female parent) and the hybrid clones (of E. wetarensis and E. grandis) have a higher fineness of growth. This study proved that UAV-based fine-resolution remote sensing could be an efficient, accurate and precise technology in phenotyping (used in genetic analysis) for tree breeding.
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Harrison PA, Camarretta N, Krisanski S, Bailey TG, Davidson NJ, Bain G, Hamer R, Gardiner R, Proft K, Taskhiri MS, Turner P, Turner D, Lucieer A. From communities to individuals: Using remote sensing to inform and monitor woodland restoration. ECOLOGICAL MANAGEMENT & RESTORATION 2021. [DOI: 10.1111/emr.12505] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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11
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Machwitz M, Pieruschka R, Berger K, Schlerf M, Aasen H, Fahrner S, Jiménez-Berni J, Baret F, Rascher U. Bridging the Gap Between Remote Sensing and Plant Phenotyping-Challenges and Opportunities for the Next Generation of Sustainable Agriculture. FRONTIERS IN PLANT SCIENCE 2021; 12:749374. [PMID: 34751225 PMCID: PMC8571019 DOI: 10.3389/fpls.2021.749374] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 09/27/2021] [Indexed: 05/27/2023]
Affiliation(s)
- Miriam Machwitz
- Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Belval, Luxembourg
| | - Roland Pieruschka
- Institute of Bio and Geosciences, Plant Sciences, Forschungszentrum Jülich, Helmholtz-Verband Deutscher Forschungszentren, Jülich, Germany
| | - Katja Berger
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Martin Schlerf
- Department of Environmental Research and Innovation, Luxembourg Institute of Science and Technology, Belval, Luxembourg
| | - Helge Aasen
- Department of Environmental Systems Science, Crop Science, Eidgenössische Technische Hochschule (ETH) Zurich, Zurich, Switzerland
| | - Sven Fahrner
- Institute of Bio and Geosciences, Plant Sciences, Forschungszentrum Jülich, Helmholtz-Verband Deutscher Forschungszentren, Jülich, Germany
| | - Jose Jiménez-Berni
- Instituto de Agricultura Sostenible, Consejo Superior de Investigaciones Científicas, Cordoba, Spain
| | | | - Uwe Rascher
- Forschungszentrum Jülich, Institute of Bio- and Geosciences Plant Sciences (IBG-2), Jülich, Germany
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Opgenoorth L, Dauphin B, Benavides R, Heer K, Alizoti P, Martínez-Sancho E, Alía R, Ambrosio O, Audrey A, Auñón F, Avanzi C, Avramidou E, Bagnoli F, Barbas E, Bastias CC, Bastien C, Ballesteros E, Beffa G, Bernier F, Bignalet H, Bodineau G, Bouic D, Brodbeck S, Brunetto W, Buchovska J, Buy M, Cabanillas-Saldaña AM, Carvalho B, Cheval N, Climent JM, Correard M, Cremer E, Danusevičius D, Del Caño F, Denou JL, di Gerardi N, Dokhelar B, Ducousso A, Eskild Nilsen A, Farsakoglou AM, Fonti P, Ganopoulos I, García Del Barrio JM, Gilg O, González-Martínez SC, Graf R, Gray A, Grivet D, Gugerli F, Hartleitner C, Hollenbach E, Hurel A, Issehut B, Jean F, Jorge V, Jouineau A, Kappner JP, Kärkkäinen K, Kesälahti R, Knutzen F, Kujala ST, Kumpula TA, Labriola M, Lalanne C, Lambertz J, Lascoux M, Lejeune V, Le-Provost G, Levillain J, Liesebach M, López-Quiroga D, Meier B, Malliarou E, Marchon J, Mariotte N, Mas A, Matesanz S, Meischner H, Michotey C, Milesi P, Morganti S, Nievergelt D, Notivol E, Ostreng G, Pakull B, Perry A, Piotti A, Plomion C, Poinot N, Pringarbe M, Puzos L, Pyhäjärvi T, Raffin A, Ramírez-Valiente JA, Rellstab C, Remi D, Richter S, Robledo-Arnuncio JJ, San Segundo S, Savolainen O, Schueler S, Schneck V, Scotti I, Semerikov V, Slámová L, Sønstebø JH, Spanu I, Thevenet J, Tollefsrud MM, Turion N, Vendramin GG, Villar M, von Arx G, Westin J, Fady B, Myking T, Valladares F, Aravanopoulos FA, Cavers S. The GenTree Platform: growth traits and tree-level environmental data in 12 European forest tree species. Gigascience 2021; 10:6177710. [PMID: 33734368 PMCID: PMC7970660 DOI: 10.1093/gigascience/giab010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2020] [Revised: 12/07/2020] [Accepted: 02/03/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Progress in the field of evolutionary forest ecology has been hampered by the huge challenge of phenotyping trees across their ranges in their natural environments, and the limitation in high-resolution environmental information. FINDINGS The GenTree Platform contains phenotypic and environmental data from 4,959 trees from 12 ecologically and economically important European forest tree species: Abies alba Mill. (silver fir), Betula pendula Roth. (silver birch), Fagus sylvatica L. (European beech), Picea abies (L.) H. Karst (Norway spruce), Pinus cembra L. (Swiss stone pine), Pinus halepensis Mill. (Aleppo pine), Pinus nigra Arnold (European black pine), Pinus pinaster Aiton (maritime pine), Pinus sylvestris L. (Scots pine), Populus nigra L. (European black poplar), Taxus baccata L. (English yew), and Quercus petraea (Matt.) Liebl. (sessile oak). Phenotypic (height, diameter at breast height, crown size, bark thickness, biomass, straightness, forking, branch angle, fructification), regeneration, environmental in situ measurements (soil depth, vegetation cover, competition indices), and environmental modeling data extracted by using bilinear interpolation accounting for surrounding conditions of each tree (precipitation, temperature, insolation, drought indices) were obtained from trees in 194 sites covering the species' geographic ranges and reflecting local environmental gradients. CONCLUSION The GenTree Platform is a new resource for investigating ecological and evolutionary processes in forest trees. The coherent phenotyping and environmental characterization across 12 species in their European ranges allow for a wide range of analyses from forest ecologists, conservationists, and macro-ecologists. Also, the data here presented can be linked to the GenTree Dendroecological collection, the GenTree Leaf Trait collection, and the GenTree Genomic collection presented elsewhere, which together build the largest evolutionary forest ecology data collection available.
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Affiliation(s)
- Lars Opgenoorth
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany.,Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Benjamin Dauphin
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Raquel Benavides
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Katrin Heer
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Paraskevi Alizoti
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | | | - Ricardo Alía
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Olivier Ambrosio
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Albet Audrey
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Francisco Auñón
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Camilla Avanzi
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Evangelia Avramidou
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Francesca Bagnoli
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Evangelos Barbas
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Cristina C Bastias
- Centre d'Ecologie Fonctionnelle et Evolutive (CEFE), CNRS, UMR 5175, 34090, Montpellier, France
| | - Catherine Bastien
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Dept ECOFA, 45075, Orléans, France
| | - Eduardo Ballesteros
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Giorgia Beffa
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Frédéric Bernier
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Henri Bignalet
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Guillaume Bodineau
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), GBFOR, 45075, Orléans, France
| | - Damien Bouic
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Sabine Brodbeck
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - William Brunetto
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Jurata Buchovska
- Vytautas Magnus University, Studentu Street 11, 53361, Akademija, Lithuania
| | - Melanie Buy
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Ana M Cabanillas-Saldaña
- Departamento de Agricultura, Ganadería y Medio Ambiente, Gobierno de Aragón, P. Mª Agustín 36, 50071, Zaragoza, Spain
| | - Bárbara Carvalho
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Nicolas Cheval
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - José M Climent
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Marianne Correard
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Eva Cremer
- Bavarian Institute for Forest Genetics, Forstamtsplatz 1, 83317, Teisendorf, Germany
| | | | - Fernando Del Caño
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Jean-Luc Denou
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Nicolas di Gerardi
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Bernard Dokhelar
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | | | - Anne Eskild Nilsen
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Anna-Maria Farsakoglou
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Patrick Fonti
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Ioannis Ganopoulos
- Institute of Plant Breeding and Genetic Resources, Hellenic Agricultural Organization DEMETER (ex NAGREF), 57001, Thermi, Greece
| | - José M García Del Barrio
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Olivier Gilg
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | | | - René Graf
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Alan Gray
- UK Centre for Ecology and Hydrology, Bush Estate Penicuik, EH26 0QB, Edinburgh, UK
| | - Delphine Grivet
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Felix Gugerli
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | | | - Enja Hollenbach
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Agathe Hurel
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Bernard Issehut
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Florence Jean
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Veronique Jorge
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), ONF, BIOFORA, 45075, Orléans, France
| | - Arnaud Jouineau
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Jan-Philipp Kappner
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Katri Kärkkäinen
- Natural Resources Institute Finland, Paavo Havaksentie 3, 90014, University of Oulu, Finland
| | - Robert Kesälahti
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Florian Knutzen
- Bavarian Institute for Forest Genetics, Forstamtsplatz 1, 83317, Teisendorf, Germany
| | - Sonja T Kujala
- Natural Resources Institute Finland, Paavo Havaksentie 3, 90014, University of Oulu, Finland
| | - Timo A Kumpula
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Mariaceleste Labriola
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Celine Lalanne
- INRAE, Univsité de Bordeaux, BIOGECO, 33770, Cestas, France
| | - Johannes Lambertz
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Martin Lascoux
- Department of Ecology & Genetics, EBC, Uppsala University, Norbyvägen 18D, 75236, Uppsala, Sweden
| | - Vincent Lejeune
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), GBFOR, 45075, Orléans, France
| | | | - Joseph Levillain
- Université de Lorraine, AgroParisTech, INRAE, SILVA, 54000, Nancy, France
| | - Mirko Liesebach
- Thünen Institute of Forest Genetics, Sieker Landstr. 2, 22927, Grosshansdorf, Germany
| | - David López-Quiroga
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Benjamin Meier
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Ermioni Malliarou
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Jérémy Marchon
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Nicolas Mariotte
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Antonio Mas
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Silvia Matesanz
- Área de Biodiversidad y Conservación, Universidad Rey Juan Carlos, Calle Tulipán s/n, 28933, Móstoles, Spain
| | - Helge Meischner
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Célia Michotey
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), URGI, Versailles, France
| | - Pascal Milesi
- Department of Ecology & Genetics, EBC, Science for Life Laboratory, Uppsala University, 75236, Uppsala, Sweden
| | - Sandro Morganti
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Daniel Nievergelt
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Eduardo Notivol
- Centro de Investigación y Tecnología Agroalimentaria de Aragón - Unidad de Recursos Forestales (CITA), Avda. Montañana 930, 50059, Zaragoza, Spain
| | - Geir Ostreng
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Birte Pakull
- Thünen Institute of Forest Genetics, Sieker Landstr. 2, 22927, Grosshansdorf, Germany
| | - Annika Perry
- UK Centre for Ecology and Hydrology, Bush Estate Penicuik, EH26 0QB, Edinburgh, UK
| | - Andrea Piotti
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | | | - Nicolas Poinot
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Mehdi Pringarbe
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Luc Puzos
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Tanja Pyhäjärvi
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Annie Raffin
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - José A Ramírez-Valiente
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Christian Rellstab
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Dourthe Remi
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), UEFP, 33610, Cestas, France
| | - Sebastian Richter
- Philipps University Marburg, Faculty of Biology, Plant Ecology and Geobotany, Karl-von-Frisch Strasse 8, 35043, Marburg, Germany
| | - Juan J Robledo-Arnuncio
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Sergio San Segundo
- Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria - Centro de Investigación Forestal (INIA-CIFOR), Ctra. de la Coruña km 7.5, 28040, Madrid, Spain
| | - Outi Savolainen
- University of Oulu, Pentti Kaiteran katu 1, 90014, University of Oulu, Finland
| | - Silvio Schueler
- Austrian Research Centre for Forests (BFW), Seckendorff-Gudent-Weg 8, 1131, Wien, Austria
| | - Volker Schneck
- Thünen Institute of Forest Genetics, Eberswalder Chaussee 3a, 15377, Waldsieversdorf, Germany
| | - Ivan Scotti
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Vladimir Semerikov
- Institute of Plant and Animal Ecology, Ural branch of RAS, 8 Marta St. 202, 620144, Ekaterinburg, Russia
| | - Lenka Slámová
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | - Jørn Henrik Sønstebø
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Ilaria Spanu
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Jean Thevenet
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Mari Mette Tollefsrud
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Norbert Turion
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Giovanni Giuseppe Vendramin
- Institute of Biosciences and BioResources, National Research Council (CNR), via Madonna del Piano 10, 50019, Sesto, Fiorentino, Italy
| | - Marc Villar
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), ONF, BIOFORA, 45075, Orléans, France
| | - Georg von Arx
- Swiss Federal Research Institute WSL, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland
| | | | - Bruno Fady
- Institut National de Recherche en Agriculture, Alimentation et Environment (INRAE), Domaine Saint Paul, Site Agroparc, 84914, Avignon, France
| | - Tor Myking
- Division of Forestry and Forest Resources, Norwegian Institute of Bioeconomy Research (NIBIO), P.O. Box 115, 1431, Ås, Norway
| | - Fernando Valladares
- LINCGlobal, Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales, CSIC, Serrano 115 dpdo, 28006, Madrid, Spain
| | - Filippos A Aravanopoulos
- Aristotle University of Thessaloniki, School of Forestry and Natural Environment, Laboratory of Forest Genetics and Tree Improvement, 54124, Thessaloniki, Greece
| | - Stephen Cavers
- UK Centre for Ecology and Hydrology, Bush Estate Penicuik, EH26 0QB, Edinburgh, UK
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Li D, Quan C, Song Z, Li X, Yu G, Li C, Muhammad A. High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field. Front Bioeng Biotechnol 2021; 8:623705. [PMID: 33520974 PMCID: PMC7838587 DOI: 10.3389/fbioe.2020.623705] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
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Affiliation(s)
- Daoliang Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Chaoqun Quan
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhaoyang Song
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Xiang Li
- Department of Psychology, College of Education, Hubei University, Wuhan, China
| | - Guanghui Yu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Cheng Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Akhter Muhammad
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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14
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Pont D, Dungey HS, Suontama M, Stovold GT. Spatial Models With Inter-Tree Competition From Airborne Laser Scanning Improve Estimates of Genetic Variance. FRONTIERS IN PLANT SCIENCE 2021; 11:596315. [PMID: 33488644 PMCID: PMC7817535 DOI: 10.3389/fpls.2020.596315] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 12/08/2020] [Indexed: 06/12/2023]
Abstract
Phenotyping individual trees to quantify interactions among genotype, environment, and management practices is critical to the development of precision forestry and to maximize the opportunity of improved tree breeds. In this study we utilized airborne laser scanning (ALS) data to detect and characterize individual trees in order to generate tree-level phenotypes and tree-to-tree competition metrics. To examine our ability to account for environmental variation and its relative importance on individual-tree traits, we investigated the use of spatial models using ALS-derived competition metrics and conventional autoregressive spatial techniques. Models utilizing competition covariate terms were found to quantify previously unexplained phenotypic variation compared with standard models, substantially reducing residual variance and improving estimates of heritabilities for a set of operationally relevant traits. Models including terms for spatial autocorrelation and competition performed the best and were labelled ACE (autocorrelation-competition-error) models. The best ACE models provided statistically significant reductions in residuals ranging from -65.48% for tree height (H) to -21.03% for wood stiffness (A), and improvements in narrow sense heritabilities from 38.64% for H to 14.01% for A. Individual tree phenotyping using an ACE approach is therefore recommended for analyses of research trials where traits are susceptible to spatial effects.
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Affiliation(s)
- David Pont
- Forest Informatics, Scion, Rotorua, New Zealand
| | | | - Mari Suontama
- Forest Genetics, Scion, Rotorua, New Zealand
- Tree Breeding, Skogforsk, Umeå, Sweden
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From Drones to Phenotype: Using UAV-LiDAR to Detect Species and Provenance Variation in Tree Productivity and Structure. REMOTE SENSING 2020. [DOI: 10.3390/rs12193184] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The use of unmanned aerial vehicles (UAVs) for remote sensing of natural environments has increased over the last decade. However, applications of this technology for high-throughput individual tree phenotyping in a quantitative genetic framework are rare. We here demonstrate a two-phased analytical pipeline that rapidly phenotypes and filters for genetic signals in traditional and novel tree productivity and architectural traits derived from ultra-dense light detection and ranging (LiDAR) point clouds. The goal of this study was rapidly phenotype individual trees to understand the genetic basis of ecologically and economically significant traits important for guiding the management of natural resources. Individual tree point clouds were acquired using UAV-LiDAR captured over a multi-provenance common-garden restoration field trial located in Tasmania, Australia, established using two eucalypt species (Eucalyptus pauciflora and Eucalyptus tenuiramis). Twenty-five tree productivity and architectural traits were calculated for each individual tree point cloud. The first phase of the analytical pipeline found significant species differences in 13 of the 25 derived traits, revealing key structural differences in productivity and crown architecture between species. The second phase investigated the within species variation in the same 25 structural traits. Significant provenance variation was detected for 20 structural traits in E. pauciflora and 10 in E. tenuiramis, with signals of divergent selection found for 11 and 7 traits, respectively, putatively driven by the home-site environment shaping the observed variation. Our results highlight the genetic-based diversity within and between species for traits important for forest structure, such as crown density and structural complexity. As species and provenances are being increasingly translocated across the landscape to mitigate the effects of rapid climate change, our results that were achieved through rapid phenotyping using UAV-LiDAR, raise the need to understand the functional value of productivity and architectural traits reflecting species and provenance differences in crown structure and the interplay they have on the dependent biotic communities.
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16
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Klápště J, Dungey HS, Graham NJ, Telfer EJ. Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight. BMC PLANT BIOLOGY 2020; 20:205. [PMID: 32393229 DOI: 10.1186/s12870-12020-02403-12876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/23/2020] [Indexed: 05/28/2023]
Abstract
BACKGROUND Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping. RESULTS We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits. CONCLUSIONS Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand.
| | - Heidi S Dungey
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
| | - Natalie J Graham
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
| | - Emily J Telfer
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010, New Zealand
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Klápště J, Dungey HS, Graham NJ, Telfer EJ. Effect of trait's expression level on single-step genomic evaluation of resistance to Dothistroma needle blight. BMC PLANT BIOLOGY 2020; 20:205. [PMID: 32393229 PMCID: PMC7216529 DOI: 10.1186/s12870-020-02403-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 04/23/2020] [Indexed: 05/23/2023]
Abstract
BACKGROUND Many conifer breeding programs are paying increasing attention to breeding for resistance to needle disease due to the increasing importance of climate change. Phenotyping of traits related to resistance has many biological and temporal constraints that can often confound the ability to achieve reliable phenotypes and consequently, reliable genetic progress. The development of next generation sequencing platforms has also enabled implementation of genomic approaches in species lacking robust reference genomes. Genomic selection is, therefore, a promising strategy to overcome the constraints of needle disease phenotyping. RESULTS We found high accuracy in the prediction of genomic breeding values in the disease-related traits that were well characterized, reaching 0.975 for genotyped individuals and 0.587 for non-genotyped individuals. This compared well with pedigree-based accuracies of up to 0.746. Surprisingly, poorly phenotyped disease traits also showed very high accuracy in terms of correlation of predicted genomic breeding values with pedigree-based counterparts. However, this was likely caused by the fact that both were clustered around the population mean, while deviations from the population mean caused by genetic effects did not appear to be well described. Caution should therefore be taken with the interpretation of results in poorly phenotyped traits. CONCLUSIONS Implementation of genomic selection in this test population of Pinus radiata resulted in a relatively high prediction accuracy of needle loss due to Dothistroma septosporum compared with a pedigree-based approach. Using genomics to avoid biological/temporal constraints where phenotyping is reliable appears promising. Unsurprisingly, reliable phenotyping, resulting in good heritability estimates, is a fundamental requirement for the development of a reliable prediction model. Furthermore, our results are also specific to the single pathogen mating-type that is present in New Zealand, and may change with future incursion of other pathogen varieties. There is no doubt, however, that once a robust genomic prediction model is built, it will be invaluable to not only select for host tolerance, but for other economically important traits simultaneously. This tool will thus future-proof our forests by mitigating the risk of disease outbreaks induced by future changes in climate.
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Affiliation(s)
- Jaroslav Klápště
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Heidi S. Dungey
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Natalie J. Graham
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
| | - Emily J. Telfer
- Scion (New Zealand Forest Research Institute Ltd.), 49 Sala Street, Rotorua, 3010 New Zealand
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Bombrun M, Dash JP, Pont D, Watt MS, Pearse GD, Dungey HS. Forest-Scale Phenotyping: Productivity Characterisation Through Machine Learning. FRONTIERS IN PLANT SCIENCE 2020; 11:99. [PMID: 32210980 PMCID: PMC7068454 DOI: 10.3389/fpls.2020.00099] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 01/22/2020] [Indexed: 06/10/2023]
Abstract
Advances in remote sensing combined with the emergence of sophisticated methods for large-scale data analytics from the field of data science provide new methods to model complex interactions in biological systems. Using a data-driven philosophy, insights from experts are used to corroborate the results generated through analytical models instead of leading the model design. Following such an approach, this study outlines the development and implementation of a whole-of-forest phenotyping system that incorporates spatial estimates of productivity across a large plantation forest. In large-scale plantation forestry, improving the productivity and consistency of future forests is an important but challenging goal due to the multiple interactions between biotic and abiotic factors, the long breeding cycle, and the high variability of growing conditions. Forest phenotypic expression is highly affected by the interaction of environmental conditions and forest management but the understanding of this complex dynamics is incomplete. In this study, we collected an extensive set of 2.7 million observations composed of 62 variables describing climate, forest management, tree genetics, and fine-scale terrain information extracted from environmental surfaces, management records, and remotely sensed data. Using three machine learning methods, we compared models of forest productivity and evaluate the gain and Shapley values for interpreting the influence of categorical variables on the power of these methods to predict forest productivity at a landscape level. The most accurate model identified that the most important drivers of productivity were, in order of importance, genetics, environmental conditions, leaf area index, topology, and soil properties, thus describing the complex interactions of the forest. This approach demonstrates that new methods in remote sensing and data science enable powerful, landscape-level understanding of forest productivity. The phenotyping method developed here can be used to identify superior and inferior genotypes and estimate a productivity index for individual site. This approach can improve tree breeding and deployment of the right genetics to the right site in order to increase the overall productivity across planted forests.
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Affiliation(s)
| | | | - David Pont
- Forest Informatics, Scion, Rotorua, New Zealand
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Wegrzyn JL, Falk T, Grau E, Buehler S, Ramnath R, Herndon N. Cyberinfrastructure and resources to enable an integrative approach to studying forest trees. Evol Appl 2020; 13:228-241. [PMID: 31892954 PMCID: PMC6935593 DOI: 10.1111/eva.12860] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 08/11/2019] [Accepted: 08/14/2019] [Indexed: 12/19/2022] Open
Abstract
Sequencing technologies and bioinformatic approaches are now available to resolve the challenges associated with complex and heterozygous genomes. Increased access to less expensive and more effective instrumentation will contribute to a wealth of high-quality plant genomes in the next few years. In the meantime, more than 370 tree species are associated with public projects in primary repositories that are interrogating expression profiles, identifying variants, or analyzing targeted capture without a high-quality reference genome. Genomic data from these projects generates sequences that represent intermediate assemblies for transcriptomes and genomes. These data contribute to forest tree biology, but the associated sequence remains trapped in supplemental files that are poorly integrated in plant community databases and comparative genomic platforms. Successful implementation of life science cyberinfrastructure is improving data standards, ontologies, analytic workflows, and integrated database platforms for both model and non-model plant species. Unique to forest trees with large populations that are long-lived, outcrossing, and genetically diverse, the phenotypic and environmental metrics associated with georeferenced populations are just as important as the genomic data sampled for each individual. To address questions related to forest health and productivity, cyberinfrastructure must keep pace with the magnitude of genomic and phenomic sampling of larger populations. This review examines the current landscape of cyberinfrastructure, with an emphasis on best practices and resources to align community data with the Findable, Accessible, Interoperable, and Reusable (FAIR) guidelines.
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Affiliation(s)
- Jill L. Wegrzyn
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticut
| | - Taylor Falk
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticut
| | - Emily Grau
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticut
| | - Sean Buehler
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticut
| | - Risharde Ramnath
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticut
| | - Nic Herndon
- Department of Ecology and Evolutionary BiologyUniversity of ConnecticutStorrsConnecticut
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Lenz PRN, Nadeau S, Mottet M, Perron M, Isabel N, Beaulieu J, Bousquet J. Multi-trait genomic selection for weevil resistance, growth, and wood quality in Norway spruce. Evol Appl 2020; 13:76-94. [PMID: 31892945 PMCID: PMC6935592 DOI: 10.1111/eva.12823] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 04/18/2019] [Accepted: 05/15/2019] [Indexed: 12/12/2022] Open
Abstract
Plantation-grown trees have to cope with an increasing pressure of pest and disease in the context of climate change, and breeding approaches using genomics may offer efficient and flexible tools to face this pressure. In the present study, we targeted genetic improvement of resistance of an introduced conifer species in Canada, Norway spruce (Picea abies (L.) Karst.), to the native white pine weevil (Pissodes strobi Peck). We developed single- and multi-trait genomic selection (GS) models and selection indices considering the relationships between weevil resistance, intrinsic wood quality, and growth traits. Weevil resistance, acoustic velocity as a proxy for mechanical wood stiffness, and average wood density showed moderate-to-high heritability and low genotype-by-environment interactions. Weevil resistance was genetically positively correlated with tree height, height-to-diameter at breast height (DBH) ratio, and acoustic velocity. The accuracy of the different GS models tested (GBLUP, threshold GBLUP, Bayesian ridge regression, BayesCπ) was high and did not differ among each other. Multi-trait models performed similarly as single-trait models when all trees were phenotyped. However, when weevil attack data were not available for all trees, weevil resistance was more accurately predicted by integrating genetically correlated growth traits into multi-trait GS models. A GS index that corresponded to the breeders' priorities achieved near maximum gains for weevil resistance, acoustic velocity, and height growth, but a small decrease for DBH. The results of this study indicate that it is possible to breed for high-quality, weevil-resistant Norway spruce reforestation stock with high accuracy achieved from single-trait or multi-trait GS.
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Affiliation(s)
- Patrick R. N. Lenz
- Canadian Wood Fibre CentreNatural Resources CanadaQuébecQuébecCanada
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
| | - Simon Nadeau
- Canadian Wood Fibre CentreNatural Resources CanadaQuébecQuébecCanada
| | - Marie‐Josée Mottet
- Ministère des Forêts, de la Faune et des ParcsGouvernement du Québec, Direction de la recherche forestièreQuébecQuébecCanada
| | - Martin Perron
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
- Ministère des Forêts, de la Faune et des ParcsGouvernement du Québec, Direction de la recherche forestièreQuébecQuébecCanada
| | - Nathalie Isabel
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
- Laurentian Forestry CentreNatural Resources CanadaQuébecQuébecCanada
| | - Jean Beaulieu
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
| | - Jean Bousquet
- Canada Research Chair in Forest GenomicsInstitute of Integrative Biology and Systems, Centre for Forest ResearchUniversité LavalQuébecQuébecCanada
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Harfouche AL, Jacobson DA, Kainer D, Romero JC, Harfouche AH, Scarascia Mugnozza G, Moshelion M, Tuskan GA, Keurentjes JJ, Altman A. Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence. Trends Biotechnol 2019; 37:1217-1235. [DOI: 10.1016/j.tibtech.2019.05.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/18/2019] [Accepted: 05/23/2019] [Indexed: 12/20/2022]
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Modelling the Effect of Microsite Influences on the Growth and Survival of Juvenile Eucalyptus globoidea (Blakely) and Eucalyptus bosistoana (F. Muell) in New Zealand. FORESTS 2019. [DOI: 10.3390/f10100857] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The effect of microsite on juvenile forest plantation yield is rarely explored. This is because juvenile plantation growth is considered to be reasonably homogenous due to a lack of resource competition between trees prior to canopy closure. However, models of juvenile plantation height growth and survival that are sensitive to microsite variation could aid decisions relating to site preparation, plantation establishment and early silvicultural treatments. In this study, juvenile Eucalyptus bosistoana and E. globoidea height growth and survival proportion were modelled against topographic and environmental microsite characteristics as independent variables. The experiment included three different sites situated in a sub-humid region of New Zealand. A total of 540 plots were planted with 18,540 trees in regular rows and columns. Micro-topographical variables significantly influenced height growth and survival proportion of both E. bosistoana and E. globoidea, but species differed in their responses. More sheltered microsites yielded greater height growth and survival for both species. The height of both species was influenced by wind exposure, morphometric protection, and distance from the nearest ridge. E. bosistoana height was also influenced by topographic position and surface plan curvature. Survival was affected by surface profile curvature for both species, while E. globoidea survival was also impacted by surface plan curvature and distance from the top ridge. This study identified microsite factors influencing juvenile height and survival of two Eucalyptus species.
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Grattapaglia D, Silva-Junior OB, Resende RT, Cappa EP, Müller BSF, Tan B, Isik F, Ratcliffe B, El-Kassaby YA. Quantitative Genetics and Genomics Converge to Accelerate Forest Tree Breeding. FRONTIERS IN PLANT SCIENCE 2018; 9:1693. [PMID: 30524463 PMCID: PMC6262028 DOI: 10.3389/fpls.2018.01693] [Citation(s) in RCA: 77] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2018] [Accepted: 10/31/2018] [Indexed: 05/18/2023]
Abstract
Forest tree breeding has been successful at delivering genetically improved material for multiple traits based on recurrent cycles of selection, mating, and testing. However, long breeding cycles, late flowering, variable juvenile-mature correlations, emerging pests and diseases, climate, and market changes, all pose formidable challenges. Genetic dissection approaches such as quantitative trait mapping and association genetics have been fruitless to effectively drive operational marker-assisted selection (MAS) in forest trees, largely because of the complex multifactorial inheritance of most, if not all traits of interest. The convergence of high-throughput genomics and quantitative genetics has established two new paradigms that are changing contemporary tree breeding dogmas. Genomic selection (GS) uses large number of genome-wide markers to predict complex phenotypes. It has the potential to accelerate breeding cycles, increase selection intensity and improve the accuracy of breeding values. Realized genomic relationships matrices, on the other hand, provide innovations in genetic parameters' estimation and breeding approaches by tracking the variation arising from random Mendelian segregation in pedigrees. In light of a recent flow of promising experimental results, here we briefly review the main concepts, analytical tools and remaining challenges that currently underlie the application of genomics data to tree breeding. With easy and cost-effective genotyping, we are now at the brink of extensive adoption of GS in tree breeding. Areas for future GS research include optimizing strategies for updating prediction models, adding validated functional genomics data to improve prediction accuracy, and integrating genomic and multi-environment data for forecasting the performance of genetic material in untested sites or under changing climate scenarios. The buildup of phenotypic and genome-wide data across large-scale breeding populations and advances in computational prediction of discrete genomic features should also provide opportunities to enhance the application of genomics to tree breeding.
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Affiliation(s)
- Dario Grattapaglia
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Orzenil B. Silva-Junior
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Programa de Ciências Genômicas e BiotecnologiaUniversidade Católica de Brasília, Brasília, Brazil
| | | | - Eduardo P. Cappa
- Centro de Investigación de Recursos Naturales, Instituto de Recursos BiológicosINTA, Buenos Aires, Argentina
- Consejo Nacional de Investigaciones Científicas y TécnicasBuenos Aires, Argentina
| | - Bárbara S. F. Müller
- EMBRAPA Recursos Genéticos e BiotecnologiaBrasília, Brazil
- Departamento de Biologia CelularUniversidade de Brasília, Brasília, Brazil
| | - Biyue Tan
- Biomaterials DivisionStora Enso AB, Stockholm, Sweden
| | - Fikret Isik
- Department of Forestry and Environmental Resources, North Carolina State UniversityRaleigh, NC, United States
| | - Blaise Ratcliffe
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
| | - Yousry A. El-Kassaby
- Department of Forest and Conservation Sciences, Faculty of Forestry, University of British ColumbiaVancouver, BC, Canada
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