scholarly journals Leaf Trait Networks Based on Global Data: Representing Variation and Adaptation in Plants

2021 ◽  
Vol 12 ◽  
Author(s):  
Ying Li ◽  
Congcong Liu ◽  
Li Xu ◽  
Mingxu Li ◽  
Jiahui Zhang ◽  
...  
Keyword(s):  
Plant Traits ◽  
Nitrogen Concentration ◽  
Life Forms ◽  
Leaf Nitrogen ◽  
Economic Traits ◽  
Multiple Traits ◽  
Multiple Functions ◽  
Leaf Trait ◽  
Environmental Selection ◽  
Complex Relationships

The interdependence of multiple traits allows plants to perform multiple functions. Acquiring an accurate representation of the interdependence of plant traits could advance our understanding of the adaptative strategies of plants. However, few studies focus on complex relationships among multiple traits. Here, we proposed use of leaf trait networks (LTNs) to capture the complex relationships among traits, allowing us to visualize all relationships and quantify how they differ through network parameters. We established LTNs using six leaf economic traits. It showed that significant differences in LTNs of different life forms and growth forms. The trait relationships of broad-leaved trees were tighter than conifers; thus, broad-leaved trees could be more efficient than conifers. The trait relationships of shrubs were tighter than trees because shrubs require multiple traits to co-operate efficiently to perform multiple functions for thriving in limited resources. Furthermore, leaf nitrogen concentration and life span had the highest centrality in LTNs; consequently, the environmental selection of these two traits might impact the whole phenotype. In conclusion, LTNs are useful tools for identifying key traits and quantifying the interdependence of multiple traits.

scholarly journals Domestication has altered within-plant trait variability in a crop plant

2020 ◽  
Author(s):  
M. Robinson ◽  
A.L. Schilmiller ◽  
W.C. Wetzel
Keyword(s):  
Plant Traits ◽  
Biotic Interactions ◽  
Ecological Interactions ◽  
Multiple Traits ◽  
Leaf Trait ◽  
Crop Fields ◽  
Plant Trait ◽  
Trait Diversity ◽  
Wild Progenitors ◽  
Trait Variability

AbstractFor over 10,000 years humans have shaped plant traits through domestication. Studies of domestication have focused on changes to trait averages; however, plants also have characteristic levels of trait variability among their repeated parts, which can be heritable and mediate critical ecological interactions. Here, we ask how domestication selection has altered among-leaf trait variability using alfalfa (Medicago sativa), the oldest forage crop in the world. We found that domestication changed variability more than averages for multiple traits. Relative to wild progenitors, domesticates had elevated variability in specific leaf area, trichomes, C:N, and phytochemical concentrations and reduced variability in phytochemical composition among their leaves. Our work shows that within-plant trait variability is a novel facet of the domesticated plant phenotype, constituting a novel frontier of trait diversity within crop fields. As many critical biotic interactions occur at the scale of individual plants, our findings suggest that trait variability and diversity among leaves could act to magnify or counter the depauperate trait diversity often found at higher scales in agroecosystems.

Upscaling plant traits to ecosystem level: blending local biodiversity,  global traits databases, and remote sensing data.

2021 ◽  
Author(s):  
Álvaro Moreno-Martínez ◽  
Jose E. Adsuara ◽  
Jordi Muñoz-Marí ◽  
Emma Izquierdo-Verdiguier ◽  
Jens Katge ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Plant Traits ◽  
Nitrogen Concentration ◽  
Great Influence ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
Leaf Nitrogen ◽  
Global Biodiversity Information Facility ◽  
Ecosystem Level ◽  
Selection Of

<p>Plant functional traits have great influence in how terrestrial ecosystems function. This key information is however generally oversimplified in most Earth system models (ESMs) and is typically represented by a number of static, empirically fixed values assigned to a selection of plant functional types (PFTs). This leads to reducing the diversity of plant communities into a relatively low number of categories and key variability within individual PFTs is lost. Subgrid processes are thus underrepresented and accuracy compromised.</p><p>The TRY global traits database contains the largest set of in-situ trait observations for numerous species around the globe. Despite the large number of species and samples included in trait databases, such as TRY, they are sparse compared to the overall richness and diversity of species globally. We propose the use of the massive geolocated plant occurrence data from the Global Biodiversity Information Facility (GBIF) as ancillary source of information to better capture species distributions, especially in locations where TRY data are missing. </p><p>As a first order approach, GBIF was used to estimate species abundances for a given study area (contiguous United States), and they were further corrected with high resolution, subpixel maps of PFT derived via remote sensing and machine learning upscaling.  This information was used to provide ecosystem level trait estimates for a selection of plant traits (specific leaf area and leaf nitrogen concentration). The proposed approach allows us to link local biodiversity composition from GBIF with a more precise and realistic representation of plant community composition coming from remote sensing information for ecosystem-level trait estimation. Among many possible applications of these data, the addition of the produced trait estimates to improve ESMs estimations could be very valuable to improve the understanding and monitoring of the biosphere.</p>

Variations in the relationship between maximum leaf carboxylation rate and leaf nitrogen concentration

2014 ◽  
Vol 38 (6) ◽  
pp. 640-652 ◽  
Author(s):  
YAN Shuang ◽  
◽  
ZHANG Li ◽  
JING Yuan-Shu ◽  
HE Hong-Lin ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Concentration ◽  
The Relationship

scholarly journals Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

2015 ◽  
Vol 7 (11) ◽  
pp. 14939-14966 ◽  
Author(s):  
Xia Yao ◽  
Yu Huang ◽  
Guiyan Shang ◽  
Chen Zhou ◽  
Tao Cheng ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Leaf Nitrogen Concentration ◽  
Wheat Leaf

Monitoring leaf nitrogen in wheat using canopy reflectance spectra

2006 ◽  
Vol 86 (4) ◽  
pp. 1037-1046 ◽  
Author(s):  
Yan Zhu ◽  
Yingxue Li ◽  
Wei Feng ◽  
Yongchao Tian ◽  
Xia Yao ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Leaf Nitrogen ◽  
Reflectance Spectra ◽  
Nutrition Status ◽  
Nitrogen Accumulation ◽  
Canopy Reflectance ◽  
Leaf Nitrogen Concentration ◽  
Non Destructive ◽  
N Status ◽  
Leaf N

Non-destructive monitoring of leaf nitrogen (N) status can assist in growth diagnosis, N management and productivity forecast in field crops. The objectives of this study were to determine the relationships of leaf nitrogen concentration on a leaf dry weight basis (LNC) and leaf nitrogen accumulation per unit soil area (LNA) to ground-based canopy reflectance spectra, and to derive regression equations for monitoring N nutrition status in wheat (Triticum aestivum L.). Four field experiments were conducted with different N application rates and wheat cultivars across four growing seasons, and time-course measurements were taken on canopy spectral reflectance, LNC and leaf dry weights under the various treatments. In these studies, LNC and LNA in wheat increased with increasing N fertilization rates. The canopy reflectance differed significantly under varied N rates, and the pattern of response was consistent across the different cultivars and years. Overall, an integrated regression equation of LNC to normalized difference index (NDI) of 1220 and 710 nm of canopy reflectance spectra described the dynamic pattern of change in LNC in wheat. The ratios of several near infrared (NIR) bands to visible light were linearly related to LNA, with the ratio index (RI) of the average reflectance over 760, 810, 870, 950 and 1100 nm to 660 nm having the best index for quantitative estimation of LNA in wheat. When independent data were fit to the derived equations, the average root mean square error (RMSE) values for the predicted LNC and LNA relative to the observed values were no more than 15.1 and 15.2%, respectively, indicating a good fit. Our relationships of leaf N status to spectral indices of canopy reflectance can be potentially used for non-destructive and real-time monitoring of leaf N status in wheat. Key words: Wheat, leaf nitrogen concentration, leaf nitrogen accumulation, canopy reflectance, spectral index, nitrogen monitoring

scholarly journals Quantifying Leaf Trait Covariations and Their Relationships with Plant Adaptation Strategies along an Aridity Gradient

Biology ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1066
Author(s):  
Yanzheng Yang ◽  
Le Kang ◽  
Jun Zhao ◽  
Ning Qi ◽  
Ruonan Li ◽  
...  
Keyword(s):  
Dry Mass ◽  
Leaf Nitrogen ◽  
Adaptation Strategies ◽  
Leaf Nitrogen Content ◽  
Moisture Index ◽  
Multivariate Statistical ◽  
Plant Adaptation ◽  
Aridity Gradient ◽  
Leaf Trait ◽  
Leaf Economic Spectrum

A trait-based approach is an effective way to quantify plant adaptation strategies in response to changing environments. Single trait variations have been well depicted before; however, multi-trait covariations and their roles in shaping plant adaptation strategies along aridity gradients remain unclear. The purpose of this study was to reveal multi-trait covariation characteristics, their controls and their relevance to plant adaptation strategies. Using eight relevant plant functional traits and multivariate statistical approaches, we found the following: (1) the eight studied traits show evident covariation characteristics and could be grouped into four functional dimensions linked to plant strategies, namely energy balance, resource acquisition, resource investment and water use efficiency; (2) leaf area (LA) together with traits related to the leaf economic spectrum, including leaf nitrogen content per area (Narea), leaf nitrogen per mass (Nmass) and leaf dry mass per area (LMA), covaried along the aridity gradient (represented by the moisture index, MI) and dominated the trait–environmental change axis; (3) together, climate, soil and family can explain 50.4% of trait covariations; thus, vegetation succession along the aridity gradient cannot be neglected in trait covariations. Our findings provide novel perspectives toward a better understanding of plant adaptations to arid conditions and serve as a reference for vegetation restoration and management programs in arid regions.

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