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30 results for “plant growth rate”
Figure 7 in Conditioning of desert sandy soil and investigation of the ameliorative effects of poultry manure and bentonite treatment rate on plant growth
Figure 7. Principal component analysis (PCA) showing loading of each studied attribute (arrow) and arrow lengths approximate their variance whereas the angles between them represent their correlation.The abbreviations are Poultry Manure (PM), Bentonite (B), Plant Height (PH), Number of Leaves (L), Root Length (RL), Dry Shoot Weight (DS), Dry Root Length (DR), Root to Shoot Ratio (RS), Number of Secondary Branches (SB), Max Branch Length (LPB), Chlorophyll contents (SPAD), Chlorophyll a* (CHL a), Chlorophyll b* (CHL b).
Figure 5 in Conditioning of desert sandy soil and investigation of the ameliorative effects of poultry manure and bentonite treatment rate on plant growth
Figure 5. Corrplot (Correlation plot) represents correlation matrix among different attributes of Bougainvillea followed by treatments as (1) L100 (2) L95A (3) L95B (4) L90A (5) L90B. L100 substrate contains 100% Lehbab Sandy Soil without any addition of Bentonite and Poultry Manure. L95A substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 10% Poultry Manure.L95B substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 15% Poultry Manure. L90A substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 10% Poultry Manure. L90B substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 15% Poultry Manure. The dark blue color shows a high positive correlation while light blue and sky blue represent less association among measured parameters. The color legend on the right-hand side of corrplot shows the correlation coefficient and corresponding colors. The abbreviations are Lehbab Sandy Soil (LS) Poultry Manure (PM), Bentonite (B), Plant Height (PH), Number of Leaves (L), Root Length (RL), Dry Shoot Weight (DS), Dry Root Length (DR), Root to Shoot Ratio (RS), Number of Secondary Branches (SB), Max Branch Length (LPB), Chlorophyll contents (SPAD), Chlorophyll a* (CHL a), Chlorophyll b* (CHL b).
Figure 3 in Conditioning of desert sandy soil and investigation of the ameliorative effects of poultry manure and bentonite treatment rate on plant growth
Figure 3. Comparison of Dry Shoot Weight, Dry Root Weight, and Root/Shoot ratio for Bougainvillea plants grown in different substrates (Treatment L100, L95A, L95B, L90A, and L90B. L100 substrate contains 100% Lehbab Sandy Soil without any addition of Bentonite and Poultry Manure. L95A substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 10% Poultry Manure. L95B substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 15% Poultry Manure. L90A substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 10% Poultry Manure. L90B substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 15% Poultry Manure. Statistical letters a, b, c, d, show significant difference between plants attributes grown on different substrates, while the ab, bc, cd etc. show there is no significant difference between plants attributes.
Figure 2 in Conditioning of desert sandy soil and investigation of the ameliorative effects of poultry manure and bentonite treatment rate on plant growth
Figure 2. Comparison of Number of Secondary Branches per Plant and Number of Leaves per Plant for Bougainvillea grown in different substrates (Treatment L100, L95A, L95B, L90A, and L90B. L100 substrate contains 100% Lehbab Sandy Soil without any addition of Bentonite and Poultry Manure. L95A substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 10% Poultry Manure. L95B substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 15% Poultry Manure. L90A substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 10% Poultry Manure. L90B substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 15% Poultry Manure. Statistical letters a, b, c, d, show significant difference between plants attributes grown on different substrates, while the ab, bc, cd etc. show there is no significant difference between plants attributes.
Figure 1 in Conditioning of desert sandy soil and investigation of the ameliorative effects of poultry manure and bentonite treatment rate on plant growth
Figure 1. Comparison of Plant Height, Root Length, and Maximum Branch Length for Bougainvillea grown in different substrates (Treatment L100, L95A, L95B, L90A, and L90B. L100 substrate contains 100% Lehbab Sandy Soil without any addition of Bentonite and Poultry Manure. L95A substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 10% Poultry Manure.L95B substrate contains 95% Lehbab Sandy Soil, 5% Bentonite, and 15% Poultry Manure.L90A substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 10% Poultry Manure. L90B substrate contains 90% Lehbab Sandy Soil, 10% Bentonite, and 15% Poultry Manure. Statistical letters a, b, c, d, show significant difference between plants attributes grown on different substrates, while the ab, bc, cd etc. show there is no significant difference between plants attributes.
Fast and furious: Early differences in growth rate drive short-term plant dominance and exclusion under eutrophication
<p>1. The reduction of plant diversity following eutrophication threatens many ecosystems worldwide. Yet, the mechanisms by which species are lost following nutrient enrichment are still not completely understood, nor are the details of when such mechanisms act during the growing season, which hampers understanding and the development of mitigation strategies.</p> <p>2. Using a common garden competition experiment, we found that early-season differences in growth rates among five perennial grass species measured in monoculture predicted short-term competitive dominance in pairwise combinations and that the proportion of variance explained was particularly greater under a fertilisation treatment.</p> <p>3. We also examined the role of early-season growth rate in determining the outcome of competition along an experimental nutrient gradient in an alpine meadow. Early differences in growth rate between species predicted short-term competitive dominance under both ambient and fertilized conditions and competitive exclusion under fertilized conditions.</p> <p>4. The results of these two studies suggests that plant species growing faster during the early stage of the growing season gain a competitive advantage over species that initially grow more slowly, and that this advantage is magnified under fertilisation. This finding is consistent with the theory of asymmetric competition for light in which fast-growing species can intercept incident light and hence outcompete and exclude slower-growing (and hence shorter) species. We predict that the current chronic nutrient inputs into many terrestrial ecosystems worldwide will reduce plant diversity and maintain a low biodiversity state by continuously favouring fast-growing species. Biodiversity management strategies should focus on controlling nutrient inputs and reducing the growth of fast-growing species early in the season.</p>
Wood density and leaf size jointly predict woody plant growth rates across (but not within) species along a steep precipitation gradient
<p>1. Functional traits have been proposed to define key dimensions of plant ecological strategies, but we lack consensus on whether traits can accurately predict plant demography. Despite theoretical expectations, it has been challenging to find consistent relationships between functional traits and growth. 2. In this study, we quantified inter- and intraspecific trait variation and individual growth rates of woody plants across a steep moisture gradient that varies 10-fold in annual precipitation (350–3,700 mm) in southern Chile and used a hierarchical Bayesian model to predict growth as a function of trait values. 3. We show that large-leaved species with lower stem tissue density exhibited the fastest growth rates, and these two traits exhibited the highest proportion of interspecific variation. Predictions of growth improved considerably (R2 of the best model increased from 0.28 to 0.49) when species-level multiple traits and their interactions were considered. The inclusion of intraspecific trait variation (ITV), however, did not improve models of growth rate. 4. We found that trait-growth rate relationships were not always consistent across levels of biological organization; relationships observed at the interspecific level did not necessarily hold at the intraspecific level. We found that the relationships between wood density or leaf size and growth were consistent in direction across the precipitation gradient, and the relationships between leaf economics traits and growth were weak and site-specific. 5. Synthesis. Although using more than one functional trait considerably improved growth predictions, wood density and leaf size successfully predicted growth rates across (not within) species, which is consistent with a whole-plant carbon economy. We assert that these two traits are intimately linked and ultimately describe a continuum of plant architecture and carbon economy that covers multiple trait syndromes.</p>
Growth rate and life history shape plant resistance.
<p>Premise: Plant defenses are shaped by many factors, including herbivory, lifespan, and mating system. Predictions about plant defense and resistance are often based on resource allocation trade‐offs with plant growth and reproduction. Additionally, two types of plant resistance, constitutive and induced resistance, are predicted to be evolutionary alternatives or redundant strategies. Given the variety of plant trait combinations and non‐mutually exclusive predictions, examining resistance strategies in related species with different combinations of growth and reproductive traits is important to tease apart roles of plant traits and evolutionary history on plant resistance.</p> <p>Methods: Phylogenetic comparative methods were used to examine the potentially interacting influences of life history (annual/perennial), mating system (self‐compatible/self-incompatible), and species growth rates on constitutive resistance and inducibility (additional resistance following damage) across Physalis species (Solanaceae).</p> <p>Results: Resistance was evolutionarily labile, and there was no correlation between constitutive resistance and inducibility. Annual species with fast growth rates displayed higher constitutive resistance, but growth rate did not affect constitutive resistance in perennials. In contrast, inducibility was negatively associated with species growth rate regardless of life history or mating system.</p> <p>Conclusions: The different effects of plant life history and growth rate on constitutive resistance and inducibility indicate that defensive evolution is unconstrained by a trade‐off between resistance types. The interactions among plant life history, growth, and herbivore resistance show that plant defense is shaped not only by herbivore environment, but also by plant traits that reflect a plant's evolutionary history and local selective pressures.</p>
Data from: Assessing the effect of tissue and fire-response traits on plant growth rates post-disturbance in Eastern Australia
<p>Here is the necessary code and data to reproduce results published in 'Assessing the effect of tissue and fire-response traits on plant growth rates post-disturbance in Eastern Australia'.</p>
Data from: Importance of accounting for imperfect detection of plants in the estimation of population growth rates
Open the record for dataset details and reuse information.
Fast and furious: Early differences in growth rate drive short-term plant dominance and exclusion under eutrophication
Open the record for dataset details and reuse information.
Wood density and leaf size jointly predict woody plant growth rates across (but not within) species along a steep precipitation gradient
Open the record for dataset details and reuse information.
Fast and furious: Early differences in growth rate drive short-term plant dominance and exclusion under eutrophication
<p>1. The reduction of plant diversity following eutrophication threatens many ecosystems worldwide. Yet, the mechanisms by which species are lost following nutrient enrichment are still not completely understood, nor are the details of when such mechanisms act during the growing season, which hampers understanding and the development of mitigation strategies.</p> <p>2. Using a common garden competition experiment, we found that early-season differences in growth rates among five perennial grass species measured in monoculture predicted short-term competitive dominance in pairwise combinations and that the proportion of variance explained was particularly greater under a fertilisation treatment.</p> <p>3. We also examined the role of early-season growth rate in determining the outcome of competition along an experimental nutrient gradient in an alpine meadow. Early differences in growth rate between species predicted short-term competitive dominance under both ambient and fertilized conditions and competitive exclusion under fertilized conditions.</p> <p>4. The results of these two studies suggests that plant species growing faster during the early stage of the growing season gain a competitive advantage over species that initially grow more slowly, and that this advantage is magnified under fertilisation. This finding is consistent with the theory of asymmetric competition for light in which fast-growing species can intercept incident light and hence outcompete and exclude slower-growing (and hence shorter) species. We predict that the current chronic nutrient inputs into many terrestrial ecosystems worldwide will reduce plant diversity and maintain a low biodiversity state by continuously favouring fast-growing species. Biodiversity management strategies should focus on controlling nutrient inputs and reducing the growth of fast-growing species early in the season.</p>
Data from: Contemporary evolution of plant growth rate following experimental removal of herbivores
Herbivores are credited with driving the evolutionary diversification of plant defensive strategies over macroevolutionary time. For this to be true, herbivores must also cause short-term evolution within plant populations, but few studies have experimentally tested this prediction. We addressed this gap using a long-term manipulative field experiment where exclosures protected 22 plant populations from natural rabbit herbivory for <1 to 26 years. We collected seeds of Rumex acetosa L. (Polygonaceae) from our plots and grew them in a common greenhouse environment to quantify evolved differences among populations in individual plant growth rate, tolerance to herbivory, competitive ability, and the concentration of secondary metabolites (tannins and oxalate) implicated in defense against herbivores. In 26 years without rabbit herbivory, plant growth rate decreased linearly by 30%. We argue that plant growth rate has evolved as a defense against intense rabbit herbivory. In contrast, we found no change in tolerance to herbivory or concentrations of secondary metabolites. We also found no change in competitive ability, suggesting that contemporary evolution may not feed back to alter ecological interactions within this plant community. Our results combined with those of other studies show that the evolution of gross morphological traits such as growth rate in response to herbivory may be common, which calls into question assumptions about some of the most popular theories of plant defense.
Fig. 3 in Overexpression of DsEXLA2 gene from Dendrocalamus sinicus accelerates the plant growth rate of Arabidopsis
Fig. 3. Phylogenetic tree of expansin-like A genes from different plant species. Numbers such as 100, 99 and 97 represent the percentage bootstrap values. The accession numbers corresponding to expansin-like A proteins are as listed in Supplemental Table S2.
Fig. 8 in Overexpression of DsEXLA2 gene from Dendrocalamus sinicus accelerates the plant growth rate of Arabidopsis
Fig. 8. Effect of DsEXLA2 on cell wall thickness. A: Micrographs of stem vessel. B: Cellulose content. C. Cell wall thickness. Line 3, 9 and 10 represent the three independent transgenic lines selected. *p <0.05; **p <0.01 (Student's t-test). Bars represent ST. The scale represents 50 μm.
Fig. 7 in Overexpression of DsEXLA2 gene from Dendrocalamus sinicus accelerates the plant growth rate of Arabidopsis
Fig. 7. Changes of plant height at different growth stages. * represents the differences between the transgenic plants and Col-0 at the same growth stage at p ≤ 0.05 according to the Duncan test. Bars represent SE. Line 3, 9 and 10 represent the three independent transgenic lines selected.
Fig. 2 in Overexpression of DsEXLA2 gene from Dendrocalamus sinicus accelerates the plant growth rate of Arabidopsis
Fig. 2. Motif distribution in the EXLA2 of different plant species. A: Motifs investigated with the MEME web server. Different motifs are represented in different colors. B: Detailed information on each motif. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Overexpression of DsEXLA2 gene from Dendrocalamus sinicus accelerates the plant growth rate of Arabidopsis
Fig. 1. Sequence alignment of the EXLA2 from different plant species. Blue box indicates signal peptide. Red box indicates the CDRC motif. Asterisk indicates cysteine. Plus indicates tryptophan. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 10 in Overexpression of DsEXLA2 gene from Dendrocalamus sinicus accelerates the plant growth rate of Arabidopsis
Fig. 10. The expression patterns of 20 selected DEGs compared for RNA-seq and qRT-PCR. *p <0.05 based on Student's t-test. Bars represent SE; n = 3. EXLA2: Expansin-like A2; EXLA3: Expansin-like A3; EXLB1: Expansin-like B1; EXPB3: Expansin-B3; COBL8: COBRA-like protein 8; CSLD3: Cellulose synthase-like protein D3; CCR2: Cinnamoyl-CoA reductase 2; XTH11: Xyloglucan endotransglucosylase/hydrolase protein 11; RRTF1: Redox responsive transcription factor 1; LOG5: Lonely guy 5; CYP707A3: Cytochrome P450 707A3; GA2OX6: gibberellin 2-oxidase 6; WRKY40: WRKY DNA-binding protein 40; SUS3: Sucrose synthase 3; LOX3: Lipoxygenase 3; TAT3: Tyrosine aminotransferase 3; FBA5: Fructose-bisphosphate aldolase 5; LHCB2.3: Photosystem II light-harvesting complex gene 2.3; LHB1B1: Light-harvesting chlorophyll-protein complex II subunit B1; RBCS2B: Ribulose bisphosphate carboxylase small chain 2B.
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