scholarly journals Association of spectroscopically determined leaf nutrition related traits and breeding selection in Sassafras tzumu

2021 ◽  
Author(s):  
Jun Liu ◽  
Yang Sun ◽  
Wenjian Liu ◽  
Zifeng Tan ◽  
Jingmin Jiang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Plant Traits ◽  
Leaf Traits ◽  
Performance Model ◽  
Breeding Program ◽  
Least Squares Regression ◽  
Living Tree ◽  
Standard Normal Variate ◽  
Breeding Selection ◽  
Leaf Nutrition

Abstract Background: Plant traits related to nutrition have an influential role on tree growth, tree production and nutrient cycling. Therefore, the breeding program should consider the genetics of the traits. However, the measurement methods could seriously affect the progress of breeding selection program. In this study, we tested the ability of spectroscopy to quantify the specific leaf nutrition traits including Anthocyanins (ANTH), flavonoids (FLAV) and Nitrogen balance index (NBI), and estimated the genetic variation of these leaf traits based on the spectroscopic predicted data. Live fresh leaves of Sassafras tzumu were selected for spectral collection, after which concentrations of ANTH, FLAV and NBI were analyzed by standard analytical methods. Partial least squares regression (PLSR), five spectra pre-processing methods, and four variable selection algorisms were conducted for the optimal prediction model selection. Each trait model was simulated 200 times for error estimation. Results: The Standard Normal Variate (SNV) to the ANTH model and 1st derivatives to the FLAV and NBI models, combined with significant Multivariate Correlation (sMC) algorithm variable selection are finally regarded as the best performance model. The ANTH model produced the highest accuracy of prediction with a mean R2 of 0.72 and mean RMSE of 0.10 %, followed by FLAV and NBI model (mean R2 =0.58, mean RMSE = 0.11 % and mean R2 =0.44, mean RMSE = 0.04 %). High heritability was found of ANTH FLAV and NBI with h2 of 0.78, 0.58 and 0.61 respectively. It shows that it is benefitting and possible of breeding selection for the improvement of leaf nutrition traits. Conclusions: Spectroscopy can successfully characterize the leaf nutrition traits in living tree leaves and the ability to simultaneous multiple plant traits provides a promising and high-throughput tool for the quick analysis of large size samples and serves for genetic breeding program.

scholarly journals Association of spectroscopically determined leaf nutrition related traits and breeding selection in Sassafras tzumu

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Jun Liu ◽  
Yang Sun ◽  
Wenjian Liu ◽  
Zifeng Tan ◽  
Jingmin Jiang ◽  
...  
Keyword(s):  
Variable Selection ◽  
Plant Traits ◽  
Leaf Traits ◽  
Breeding Program ◽  
Least Squares Regression ◽  
Living Tree ◽  
Standard Normal Variate ◽  
Standard Normal ◽  
Breeding Selection ◽  
Leaf Nutrition

Abstract Background Plant traits related to nutrition have an influential role in tree growth, tree production and nutrient cycling. Therefore, the breeding program should consider the genetics of the traits. However, the measurement methods could seriously affect the progress of breeding selection program. In this study, we tested the ability of spectroscopy to quantify the specific leaf nutrition traits including anthocyanins (ANTH), flavonoids (FLAV) and nitrogen balance index (NBI), and estimated the genetic variation of these leaf traits based on the spectroscopic predicted data. Fresh leaves of Sassafras tzumu were selected for spectral collection and ANTH, FLAV and NBI concentrations measurement by standard analytical methods. Partial least squares regression (PLSR), five spectra pre-processing methods, and four variable selection algorisms were conducted for the optimal model selection. Each trait model was simulated 200 times for error estimation. Results The standard normal variate (SNV) to the ANTH model and 1st derivatives to the FLAV and NBI models, combined with significant Multivariate Correlation (sMC) algorithm variable selection are finally regarded as the best performance models. The ANTH model produced the highest accuracy of prediction with a mean R2 of 0.72 and mean RMSE of 0.10%, followed by FLAV and NBI model (mean R2 of 0.58, mean RMSE of 0.11% and mean R2 of 0.44, mean RMSE of 0.04%). High heritability was found for ANTH, FLAV and NBI with h2 of 0.78, 0.58 and 0.61 respectively. It shows that it is beneficial and possible for breeding selection to the improvement of leaf nutrition traits. Conclusions Spectroscopy can successfully characterize the leaf nutrition traits in living tree leaves and the ability to simultaneous multiple plant traits provides a promising and high-throughput tool for the quick analysis of large size samples and serves for genetic breeding program.

scholarly journals Association of Spectroscopically Determined Leaf Nutrition Related Traits and Breeding Selection in Sassafras Tzumu

2020 ◽  
Author(s):  
Yanjie Li ◽  
Wenjian Liu ◽  
Zifeng Tan ◽  
Jingmin Jiang ◽  
Jun Liu
Keyword(s):  
Variable Selection ◽  
Plant Traits ◽  
Leaf Traits ◽  
Tree Breeding ◽  
Performance Model ◽  
Least Squares Regression ◽  
Living Tree ◽  
High Heritability ◽  
Breeding Selection ◽  
Leaf Nutrition

Abstract Background: The nutrition related to traits is an influential role in tree growth, tree production and nutrient cycling. Therefore, the influence of genetic parameters on leaf nutrition traits ought to take account of optimal tree breeding selection. However, the measurement methods are seriously affected by the progress of breeding selection program. In this study, we tested the ability of spectroscopy to quantify the specific leaf nutrition traits including Anthocyanins (ANTH), flavonoids (FLAV) and Nitrogen balance index (NBI), and estimated the genetic variation of these leaf traits based on the spectroscopic predicted data. Live fresh leaves of Sassafras tzumu were selected for spectral collection, after which concentrations of ANTH, FLAV and NBI were analyzed by standard analytical methods. Partial least squares regression (PLSR), five spectra pre-processing methods, and four variable selection algorisms were conducted for the optimal prediction model selection. Each trait model was simulated 200 times for error estimation.Results: The stander normal variation (SNV) to the ANTH model and 1st derivatives to the FLAV and NBI models, combined with significant Multivariate Correlation (sMC) algorithm variable selection are finally regarded as the best performance model. The ANTH model produced the highest accuracy of prediction with a mean R2 of 0.72 and mean RMSE of 0.10 %, followed by FLAV and NBI model (mean R2 =0.58, mean RMSE = 0.11 % and mean R2 =0.44, mean RMSE = 0.04 %). High heritability was found of ANTH FLAV and NBI with h2 of 0.78, 0.58 and 0.61 respectively. It shows that it is benefitting and possible of breeding selection for the improvement of leaf nutrition traits.Conclusions: Spectroscopy can successfully characterize the leaf nutrition traits in living tree leaves and the ability to simultaneous multiple plant traits provides a promising and high-throughput tool for the quick analysis of large size samples and serves for genetic breeding program.

scholarly journals Linking hydraulic traits to tropical forest function in a size-structured and trait-driven model (TFS v.1-Hydro)

2016 ◽  
Vol 9 (11) ◽  
pp. 4227-4255 ◽  
Author(s):  
Bradley O. Christoffersen ◽  
Manuel Gloor ◽  
Sophie Fauset ◽  
Nikolaos M. Fyllas ◽  
David R. Galbraith ◽  
...  
Keyword(s):  
Tropical Forest ◽  
Water Potential ◽  
Plant Traits ◽  
Coupled Model ◽  
Leaf Traits ◽  
Tree Size ◽  
Leaf Mass Per Area ◽  
Stem Water Potential ◽  
Plant Hydraulics ◽  
Individual Trees

Abstract. Forest ecosystem models based on heuristic water stress functions poorly predict tropical forest response to drought partly because they do not capture the diversity of hydraulic traits (including variation in tree size) observed in tropical forests. We developed a continuous porous media approach to modeling plant hydraulics in which all parameters of the constitutive equations are biologically interpretable and measurable plant hydraulic traits (e.g., turgor loss point πtlp, bulk elastic modulus ε, hydraulic capacitance Cft, xylem hydraulic conductivity ks,max, water potential at 50 % loss of conductivity for both xylem (P50,x) and stomata (P50,gs), and the leaf : sapwood area ratio Al : As). We embedded this plant hydraulics model within a trait forest simulator (TFS) that models light environments of individual trees and their upper boundary conditions (transpiration), as well as providing a means for parameterizing variation in hydraulic traits among individuals. We synthesized literature and existing databases to parameterize all hydraulic traits as a function of stem and leaf traits, including wood density (WD), leaf mass per area (LMA), and photosynthetic capacity (Amax), and evaluated the coupled model (called TFS v.1-Hydro) predictions, against observed diurnal and seasonal variability in stem and leaf water potential as well as stand-scaled sap flux. Our hydraulic trait synthesis revealed coordination among leaf and xylem hydraulic traits and statistically significant relationships of most hydraulic traits with more easily measured plant traits. Using the most informative empirical trait–trait relationships derived from this synthesis, TFS v.1-Hydro successfully captured individual variation in leaf and stem water potential due to increasing tree size and light environment, with model representation of hydraulic architecture and plant traits exerting primary and secondary controls, respectively, on the fidelity of model predictions. The plant hydraulics model made substantial improvements to simulations of total ecosystem transpiration. Remaining uncertainties and limitations of the trait paradigm for plant hydraulics modeling are highlighted.

How well do seed production traits correlate with leaf traits, whole-plant traits and plant ecological strategies?

Plant Ecology ◽  
2014 ◽  
Vol 215 (11) ◽  
pp. 1351-1359 ◽  
Author(s):  
Simon Pierce ◽  
Arianna Bottinelli ◽  
Ilaria Bassani ◽  
Roberta M. Ceriani ◽  
Bruno E. L. Cerabolini
Keyword(s):  
Seed Production ◽  
Plant Traits ◽  
Leaf Traits ◽  
Production Traits ◽  
Ecological Strategies ◽  
Whole Plant ◽  
Plant Ecological

scholarly journals PREDIKSI TOTAL PADATAN TERLARUT BUAH MELON GOLDEN (Cucumis melo L.) MENGGUNAKAN VIS-SWNIRS DAN ANALISIS MULTIVARIAT

2020 ◽  
Vol 25 (2) ◽  
Author(s):  
Yuda Hadiwijaya ◽  
Kusumiyati Kusumiyati ◽  
Agus Arip Munawar
Keyword(s):  
Cucumis Melo ◽  
Partial Least Squares Regression ◽  
Scatter Correction ◽  
Least Squares Regression ◽  
Orthogonal Signal ◽  
Standard Normal Variate ◽  
Orthogonal Signal Correction ◽  
Signal Correction ◽  
Standard Normal ◽  
Cucumis Melo L

Penelitian ini bertujuan memprediksi total padatan terlarut buah melon golden menggunakan Vis-SWNIRS dan analisis multivariat. 82 sampel buah melon golden dipanen untuk dianalisis di Laboratorium Hortikultura, Fakultas Pertanian, Universitas Padjadjaran. Nirvana AG410 spectrometer dengan rentang panjang gelombang 300 sampai 1050 nm digunakan untuk pengambilan data spektra pada sampel buah melon utuh. Metode koreksi spektra yang digunakan yaitu standard normal variate (SNV), multiplicative scatter correction (MSC), dan orthogonal signal correction (OSC). Pemodelan kalibrasi dilakukan menggunakan partial least squares regression (PLSR). Hasil penelitian menunjukkan bahwa penggunaan metode koreksi spektra OSC menampikan model kalibrasi terbaik dibandingkan spektra original dan 2 spektra lainnya yang telah dikoreksi. Koefisien determinasi pada spektra OSC memperlihatkan nilai R2 tertinggi yaitu 0.99, disamping itu nilai ratio performance to deviation (RPD) yang diperoleh sebesar 3.40. Hal ini membuktikan bahwa total padatan terlarut buah melon golden dapat diprediksi dengan akurasi yang tinggi menggunakan Vis-SWNIRS dan analisis multivariat.

scholarly journals Data on Herbivore Performance and Plant Herbivore Damage Identify the Same Plant Traits as the Key Drivers of Plant–Herbivore Interaction

Insects ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 865
Author(s):  
Zuzana Münzbergová ◽  
Jiří Skuhrovec
Keyword(s):  
Plant Species ◽  
Plant Traits ◽  
Leaf Traits ◽  
Leaf Damage ◽  
Herbivore Damage ◽  
Herbivore Performance ◽  
Physical Defense ◽  
Wide Range ◽  
Herbivore Interactions ◽  
Plant Herbivore

Data on plant herbivore damage as well as on herbivore performance have been previously used to identify key plant traits driving plant–herbivore interactions. The extent to which the two approaches lead to similar conclusions remains to be explored. We determined the effect of a free-living leaf-chewing generalist caterpillar, Spodoptera littoralis (Lepidoptera: Noctuidae), on leaf damage of 24 closely related plant species from the Carduoideae subfamily and the effect of these plant species on caterpillar growth. We used a wide range of physical defense leaf traits and leaf nutrient contents as the plant traits. Herbivore performance and leaf damage were affected by similar plant traits. Traits related to higher caterpillar mortality (higher leaf dissection, number, length and toughness of spines and lower trichome density) also led to higher leaf damage. This fits with the fact that each caterpillar was feeding on a single plant and, thus, had to consume more biomass of the less suitable plants to obtain the same amount of nutrients. The key plant traits driving plant–herbivore interactions identified based on data on herbivore performance largely corresponded to the traits identified as important based on data on leaf damage. This suggests that both types of data may be used to identify the key plant traits determining plant–herbivore interactions. It is, however, important to carefully distinguish whether the data on leaf damage were obtained in the field or in a controlled feeding experiment, as the patterns expected in the two environments may go in opposite directions.

scholarly journals Relative importance of transpiration rate and leaf morphological traits for the regulation of leaf temperature

2016 ◽  
Vol 64 (1) ◽  
pp. 32 ◽  
Author(s):  
Madalena Vaz Monteiro ◽  
Tijana Blanuša ◽  
Anne Verhoef ◽  
Paul Hadley ◽  
Ross W. F. Cameron
Keyword(s):  
Water Loss ◽  
Morphological Traits ◽  
Infrared Imaging ◽  
Plant Traits ◽  
Leaf Traits ◽  
Green Roofs ◽  
Leaf Temperature ◽  
Relative Importance ◽  
Plant Structure ◽  
Leaf Colour

Urban greening solutions such as green roofs help improve residents’ thermal comfort and building insulation. However, not all plants provide the same level of cooling. This is partially due to differences in plant structure and function, including different mechanisms that plants employ to regulate leaf temperature. Ranking of multiple leaf and plant traits involved in the regulation of leaf temperature (and, consequently, plants’ cooling ‘service’) is not well understood. We, therefore, investigated the relative importance of water loss, leaf colour, thickness and extent of pubescence for the regulation of leaf temperature, in the context of species for semi-extensive green roofs. Leaf temperature was measured with an infrared imaging camera in a range of contrasting genotypes within three plant genera (Heuchera, Salvia and Sempervivum). In three glasshouse experiments (each evaluating three or four genotypes of each genus), we varied water availability to the plants and assessed how leaf temperature altered depending on water loss and specific leaf traits. Greatest reductions in leaf temperature were closely associated with higher water loss. Additionally, in non-succulents (Heuchera, Salvia), lighter leaf colour and longer hair length (on pubescent leaves) both contributed to reduced leaf temperature. However, in succulent Sempervivum, colour and pubescence made no significant contribution; leaf thickness and rate of water loss were the key regulating factors. We propose that this can lead to different plant types having significantly different potentials for cooling. We suggest that maintaining transpirational water loss by sustainable irrigation and selecting urban plants with favourable morphological traits are the key to maximising thermal benefits provided by applications such as green roofs.

scholarly journals New handbook for standardised measurement of plant functional traits worldwide

2013 ◽  
Vol 61 (3) ◽  
pp. 167 ◽  
Author(s):  
N. Pérez-Harguindeguy ◽  
S. Díaz ◽  
E. Garnier ◽  
S. Lavorel ◽  
H. Poorter ◽  
...  
Keyword(s):  
Functional Traits ◽  
Environmental Changes ◽  
Plant Traits ◽  
Leaf Traits ◽  
Plant Functional Traits ◽  
Trophic Levels ◽  
Evolutionary Patterns ◽  
Ecosystem Properties ◽  
Species Invasions ◽  
Wide Range

Plant functional traits are the features (morphological, physiological, phenological) that represent ecological strategies and determine how plants respond to environmental factors, affect other trophic levels and influence ecosystem properties. Variation in plant functional traits, and trait syndromes, has proven useful for tackling many important ecological questions at a range of scales, giving rise to a demand for standardised ways to measure ecologically meaningful plant traits. This line of research has been among the most fruitful avenues for understanding ecological and evolutionary patterns and processes. It also has the potential both to build a predictive set of local, regional and global relationships between plants and environment and to quantify a wide range of natural and human-driven processes, including changes in biodiversity, the impacts of species invasions, alterations in biogeochemical processes and vegetation–atmosphere interactions. The importance of these topics dictates the urgent need for more and better data, and increases the value of standardised protocols for quantifying trait variation of different species, in particular for traits with power to predict plant- and ecosystem-level processes, and for traits that can be measured relatively easily. Updated and expanded from the widely used previous version, this handbook retains the focus on clearly presented, widely applicable, step-by-step recipes, with a minimum of text on theory, and not only includes updated methods for the traits previously covered, but also introduces many new protocols for further traits. This new handbook has a better balance between whole-plant traits, leaf traits, root and stem traits and regenerative traits, and puts particular emphasis on traits important for predicting species’ effects on key ecosystem properties. We hope this new handbook becomes a standard companion in local and global efforts to learn about the responses and impacts of different plant species with respect to environmental changes in the present, past and future.

scholarly journals Determination of Total Flavonoids for Paris Polyphylla Var. Yunnanensis in Different Geographical Origins Using UV and FT-IR Spectroscopy

2019 ◽  
Vol 102 (2) ◽  
pp. 457-464
Author(s):  
Yuangui Yang ◽  
Yanli Zhao ◽  
Zhitian Zuo ◽  
Yuanzhong Wang
Keyword(s):  
Uv Spectrophotometry ◽  
Total Flavonoids ◽  
Least Squares Regression ◽  
Standard Normal Variate ◽  
Paris Polyphylla ◽  
Orthogonal Signal Correction ◽  
Relevant Variables ◽  
Standard Normal ◽  
Ft Ir

Abstract Background: Paris polyphylla var. Yunnanensis (PPY) is used in the clinical treatment of tumors, hemorrhages, and anthelmintic. Objective: The aim of this study is to determine total flavonoids of PPY in the Yunnan and Guizhou Provinces, China. Methods: In this study, total flavonoids were determined by UV spectrophotometry at first. Then, Fourier transform mid-infrared (FT-IR) based on various pretreatments include standard normal variate (SNV), first derivative (FD), second derivative (SD), Savitzky-Golay (SG), and orthogonal signal correction (OSC) were investigated. In addition, several relevant variables were screened by competitive adaptive reweighted sampling (CARS). The contentof total flavonoids and selected variables of FT-IRwere used to establish a partial least squares regression for PPY in different regions. Results: The results indicated that CARS was an effective method for decreasing the variable of thedatabase and improving the prediction of the model.FT-IR with pretreatment SNV + OSC + FD + SG had thebest performance, with R2 > 0.9 and residual predictive deviation = 3.3515, which could be used forthe predictive model of total flavonoids. Conclusions: Those results would provide a fast and robust strategy for the determination of total flavonoids of PPY in different geographical origin. Highlights: Various pretreatments, including SNV, FD, SD, SG, and OSC, were compared; several relevant variables were selected by CARS; and the content of total flavonoids and selected variable were used to establish a partial leastsquares regression for PPY in different regions.

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