Добірка наукової літератури з теми "Plant traits (PTs)"

An expressed sequence tag (EST) highly similar to BdPT1–4, a phosphate transporter (PT) gene in Brachypodium distachyon, was obtained in a wheat root cDNA subtractive suppression library containing genes that respond to low-phosphate (Pi) stress. The DNA sequence covering this EST (designated as TaPht1;4) was determined based on screening a wheat DNA library. TaPht1;4 consists of two exons and one intron and encodes a 555 amino acid (aa) polypeptide with a molecular weight of 60.85 kDa and an isoelectric point of 7.60. TaPht1;4 contains 12 conserved membrane-spanning domains similar to previously reported PTs in diverse plant species. Yeast complement analysis in low-Pi medium confirmed that TaPht1;4 confers the capacity to uptake Pi to MB192, a yeast strain with a defective high-affinity PT; with an apparent Km of 35.3 μM. The TaPht1;4 transcripts were specifically detected in the root and were highly induced under Pi deficiency. TaPht1;4 was also expressed following a diurnal pattern, i.e. high levels during daytime and low levels during night-time. TaPht1;4 overexpression and downregulation dramatically altered the plant phenotypic features under low-Pi conditions. Samples that overexpressed TaPht1;4 had significantly improved growth traits and accumulated more Pi than the wild-type plant and those with downregulated TaPht1;4 expression. Therefore, TaPht1;4 is a high-affinity PT gene that plays a critical role in wheat Pi acquisition under Pi deprivation.

Abstract. Plant functional traits determine vegetation responses to environmental variation, but variation in trait values is large, even within a single site. Likewise, uncertainty in how these traits map to Earth system feedbacks is large. We use a vegetation demographic model (VDM), the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), to explore parameter sensitivity of model predictions, and comparison to observations, at a tropical forest site: Barro Colorado Island in Panama. We define a single 12-dimensional distribution of plant trait variation, derived primarily from observations in Panama, and define plant functional types (PFTs) as random draws from this distribution. We compare several model ensembles, where individual ensemble members vary only in the plant traits that define PFTs, and separate ensembles differ from each other based on either model structural assumptions or non-trait, ecosystem-level parameters, which include (a) the number of competing PFTs present in any simulation and (b) parameters that govern disturbance and height-based light competition. While single-PFT simulations are roughly consistent with observations of productivity at Barro Colorado Island, increasing the number of competing PFTs strongly shifts model predictions towards higher productivity and biomass forests. Different ecosystem variables show greater sensitivity than others to the number of competing PFTs, with the predictions that are most dominated by large trees, such as biomass, being the most sensitive. Changing disturbance and height-sorting parameters, i.e., the rules of competitive trait filtering, shifts regimes of dominance or coexistence between early- and late-successional PFTs in the model. Increases to the extent or severity of disturbance, or to the degree of determinism in height-based light competition, all act to shift the community towards early-successional PFTs. In turn, these shifts in competitive outcomes alter predictions of ecosystem states and fluxes, with more early-successional-dominated forests having lower biomass. It is thus crucial to differentiate between plant traits, which are under competitive pressure in VDMs, from those model parameters that are not and to better understand the relationships between these two types of model parameters to quantify sources of uncertainty in VDMs.

Abstract. In current dynamic global vegetation models (DGVMs), including those incorporated into Earth System Models (ESMs), terrestrial vegetation is represented by a small number of plant functional types (PFTs), each with fixed properties irrespective of their predicted occurrence. This contrasts with natural vegetation, in which many plant traits vary systematically along geographic and environmental gradients. In the JSBACH DGVM, which is part of the MPI-ESM, we allowed three traits (specific leaf area (SLA), maximum carboxylation rate at 25 °C (Vcmax25) and maximum electron transport rate (Jmax25)) to vary within PFTs via trait-climate relationships based on a large trait database. For all three traits, the means of observed natural trait values strongly deviated from values used in the default model, with mean differences of 32.3% for Vcmax25, 26.8% for Jmax25 and 17.3% for SLA. Compared to the default simulation, allowing trait variation within PFTs resulted in GPP differences up to 50% in the tropics, in > 35% different dominant vegetation cover, and a closer match with a natural vegetation map. The discrepancy between default trait values and natural trait variation, combined with the substantial changes in simulated vegetation properties, together emphasize that incorporating observational data based on the ecological concepts of environmental filtering will improve the modeling of vegetation behavior in DGVMs and as such will enable more reliable projections in unknown climates.

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Abstract. Dynamic global vegetation models (DGVMs) typically rely on plant functional types (PFTs), which are assigned distinct environmental tolerances and replace one another progressively along environmental gradients. Fixed values of traits are assigned to each PFT; modelled trait variation along gradients is thus driven by PFT replacement. But empirical studies have revealed "universal" scaling relationships (quantitative trait variations with climate that are similar within and between species, PFTs and communities); and continuous, adaptive trait variation has been proposed to replace PFTs as the basis for next-generation DGVMs. Here we analyse quantitative leaf-trait variation on long temperature and moisture gradients in China with a view to understanding the relative importance of PFT replacement vs. continuous adaptive variation within PFTs. Leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and nitrogen content of dry matter were measured on all species at 80 sites ranging from temperate to tropical climates and from dense forests to deserts. Chlorophyll fluorescence traits and carbon, phosphorus and potassium contents were measured at 47 sites. Generalized linear models were used to relate log-transformed trait values to growing-season temperature and moisture indices, with or without PFT identity as a predictor, and to test for differences in trait responses among PFTs. Continuous trait variation was found to be ubiquitous. Responses to moisture availability were generally similar within and between PFTs, but biophysical traits (LA, SLA and LDMC) of forbs and grasses responded differently from woody plants. SLA and LDMC responses to temperature were dominated by the prevalence of evergreen PFTs with thick, dense leaves at the warm end of the gradient. Nutrient (N, P and K) responses to climate gradients were generally similar within all PFTs. Area-based nutrients generally declined with moisture; Narea and Karea declined with temperature, but Parea increased with temperature. Although the adaptive nature of many of these trait–climate relationships is understood qualitatively, a key challenge for modelling is to predict them quantitatively. Models must also take into account that community-level responses to climatic gradients can be influenced by shifts in PFT composition, such as the replacement of deciduous by evergreen trees, which may run either parallel or counter to trait variation within PFTs. The importance of PFT shifts varies among traits, being important for biophysical traits but less so for physiological and chemical traits.

Abstract. Dynamic global vegetation models (DGVMs) typically rely on plant functional types (PFTs), which are assigned distinct environmental tolerances and replace one another progressively along environmental gradients. Fixed values of traits are assigned to each PFT; modelled trait variation along gradients is thus driven by PFT replacement. But empirical studies have revealed "universal" scaling relationships (quantitative trait variations with climate that are similar within and between species, PFTs and communities); and continuous, adaptive trait variation has been proposed to replace PFTs as the basis for next-generation DGVMs. Here we analyse quantitative leaf-trait variation on long temperature and moisture gradients in China with a view to understanding the relative importance of PFT replacement vs. continuous adaptive variation within PFTs. Leaf area (LA), specific leaf area (SLA), leaf dry matter content (LDMC) and nitrogen content of dry matter were measured on all species at 80 sites ranging from temperate to tropical climates and from dense forests to deserts. Chlorophyll fluorescence traits and carbon, phosphorus and potassium contents were measured at 47 sites. Generalized linear models were used to relate log-transformed trait values to growing-season temperature and moisture indices, with or without PFT identity as a predictor, and to test for differences in trait responses among PFTs. Continuous trait variation was found to be ubiquitous. Responses to moisture availability were generally similar within and between PFTs, but biophysical traits (LA, SLA and LDMC) of forbs and grasses responded differently from woody plants. SLA and LDMC responses to temperature were dominated by the prevalence of evergreen PFTs with thick, dense leaves at the warm end of the gradient. Nutrient (N, P and K) responses to climate gradients were generally similar within all PFTs. Area-based nutrients generally declined with moisture; Narea and Karea declined with temperature, but Parea increased with temperature. Although the adaptive nature of many of these trait-climate relationships is understood qualitatively, a key challenge for modelling is to predict them quantitatively. Models must take into account that community-level responses to climatic gradients can be influenced by shifts in PFT composition, such as the replacement of deciduous by evergreen trees, which may run either parallel or counter to trait variation within PFTs. The importance of PFT shifts varies among traits, being important for biophysical traits but less so for physiological and chemical traits. Finally, models should take account of the diversity of trait values that is found in all sites and PFTs, representing the "pool" of variation that is locally available for the natural adaptation of ecosystem function to environmental change.

Abstract. In many current dynamic global vegetation models (DGVMs), including those incorporated into Earth system models (ESMs), terrestrial vegetation is represented by a small number of plant functional types (PFTs), each with fixed properties irrespective of their predicted occurrence. This contrasts with natural vegetation, in which many plant traits vary systematically along geographic and environmental gradients. In the JSBACH DGVM, which is part of the MPI-ESM, we allowed three traits (specific leaf area (SLA), maximum carboxylation rate at 25 °C (Vcmax25) and maximum electron transport rate at 25 °C (Jmax25)) to vary within PFTs via trait–climate relationships based on a large trait database. The R2adjusted of these relationships were up to 0.83 and 0.71 for Vcmax25 and Jmax25, respectively. For SLA, more variance remained unexplained, with a maximum R2adjusted of 0.40. Compared to the default simulation, allowing trait variation within PFTs resulted in gross primary productivity differences of up to 50% in the tropics, in > 35% different dominant vegetation cover, and a closer match with a natural vegetation map. The discrepancy between default trait values and natural trait variation, combined with the substantial changes in simulated vegetation properties, together emphasize that incorporating climate-driven trait variation, calibrated on observational data and based on ecological concepts, allows more variation in vegetation responses in DGVMs and as such is likely to enable more reliable projections in unknown climates.

Both arbuscular mycorrhizal fungi (AMF) and phosphorus (P) collectively influence the root system architecture (RSA), but whether the combination of the two affects RSA, particularly lateral root formation, is unknown. In the present study, a pot experiment was conducted to evaluate the effects of an arbuscular mycorrhizal fungus (Rhizophagus intraradices) on the RSA of lemon (Citrus limon L.) seedlings under 0 (P0) and 50 mg/kg (P50) P levels. Moreover, P and carbohydrate content; acid phosphatase activity; and the expression of P transporter genes (PTs), phosphatase genes (PAPs), and lateral-root-related genes; were determined. Our results show that root mycorrhizal colonization and mycorrhizal dependency of lemon plants are significantly higher under P0 than under P50 conditions. AMF significantly promoted the plant growth performance of lemon, irrespective of substrate P levels. The RSA parameters of AMF plants, including total root length, projected area, surface area, average diameter, volume, and second- and third-order lateral root numbers, were distinctly increased under the two P levels compared to those of non-AMF plants. Mycorrhizal treatment also induced higher carbohydrate (sucrose, glucose, and fructose) and P contents, along with a higher activity of root acid phosphatase. The expression of P-related genes, including ClPAP1, ClPT1, ClPT3, ClPT5, and ClPT7, as well as the expression of lateral-root-related genes (ClKRP6, ClPSK6, and ClRSI-1), was dramatically upregulated by AMF inoculation, irrespective of substrate P levels. Principal component analysis showed that root P and carbohydrate contents, as well as the expression of ClKRP6 and ClPSK6, were positively correlated with RSA traits and lateral root development. Our study demonstrates that mycorrhizas accelerate the P acquisition and carbohydrate accumulation of lemon plants by upregulating the expression of lateral-root-related genes, thereby positively improving the RSA. Furthermore, AMF had a greater impact on the RSA of lemon than substrate P levels.

Abstract. Plant community composition influences carbon, water, and energy fluxes at regional to global scales. Vegetation demographic models (VDMs) allow investigation of the effects of changing climate and disturbance regimes on vegetation composition and fluxes. Such investigation requires that the models can accurately resolve these feedbacks to simulate realistic composition. Vegetation in VDMs is composed of plant functional types (PFTs), which are specified according to plant traits. Defining PFTs is challenging due to large variability in trait observations within and between plant types and a lack of understanding of model sensitivity to these traits. Here we present an approach for developing PFT parameterizations that are connected to the underlying ecological processes determining forest composition in the mixed-conifer forest of the Sierra Nevada of California, USA. We constrain multiple relative trait values between PFTs, as opposed to randomly sampling within the range of observations. An ensemble of PFT parameterizations are then filtered based on emergent forest properties meeting observation-based ecological criteria under alternate disturbance scenarios. A small ensemble of alternate PFT parameterizations is identified that produces plausible forest composition and demonstrates variability in response to disturbance frequency and regional environmental variation. Retaining multiple PFT parameterizations allows us to quantify the uncertainty in forest responses due to variability in trait observations. Vegetation composition is a key emergent outcome from VDMs and our methodology provides a foundation for robust PFT parameterization across ecosystems.

Eggplant is an essential widespread year-round fruit vegetable. This study was conducted using 130 local germplasm of brinjal to select diverse parents based on the multiple traits selection index for the future breeding program. This selection was performed focusing on 14 qualitative and 10 quantitative traits variation and genetic parameters namely, phenotypic and genotypic variance (PV and GV) and genotypic and phenotypic coefficients of variation (GCV and PCV), broad-sense heritability (hBS), genetic advance, traits association, genotype by trait biplot (G × T), heatmap analysis and multi-trait index based on factor analysis and genotype-ideotype distance (MGIDI). Descriptive statistics and analysis of variance revealed a wide range of variability for morpho-physiological traits. Estimated hBS for all the measured traits ranged from 10.6% to 93%, indicating that all the traits were highly inheritable. Genetic variances were low to high for most morpho-physiological traits, indicating complex genetic architecture. Yield per plant was significantly correlated with fruit diameter, fruits per plant, percent fruits infestation by brinjal shoot and fruit borer, and fruit weight traits indicating that direct selection based on fruit number and fruit weight might be sufficient for improvement of other traits. The first two principal components (PCs) explained about 81.27% of the total variation among lines for 38 brinjal morpho-physiological traits. Genotype by trait (G × T) biplot revealed superior genotypes with combinations of favorable traits. The average genetic distance was 3.53, ranging from 0.25 to 20.01, indicating high levels of variability among the germplasm. The heat map was also used to know the relationship matrix among all the brinjal genotypes. MGIDI is an appropriate method of selection based on multiple trait information. Based on the fourteen qualitative and ten quantitative traits and evaluation of various genetic parameters, the germplasm G80, G54, G66, and G120 might be considered as best parents for the future breeding program for eggplant improvement.