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24 results for “Life Cycle Analysis”
A comparative analysis testing Werner's theory of complex life cycles
<p>A popular theoretical model for explaining the evolution of complex life cycles was provided by Earl Werner. The theory predicts the size at which an individual should switch stages to maximise growth rate relative to mortality rate across the life history. </p> <p>Werner's theory assumes that body size does not change during the transition from one phase to another (e.g. from larva to adult)—a key assumption that has not been tested systematically but could alter the predictions of the model. </p> <p>We quantified how growth rate and mass change across larval stages and metamorphosis for 105 species of fish, amphibians, insects, crustaceans and molluscs Across all taxonomic groups, we found support for Werner's assumption that growth rates are maintained or increase around transitions. We found that changes in growth and mass were greatest during metamorphosis, and change in growth correlated with development time. Importantly, most species either gained or lost mass when switching to a new stage—a direct contradiction of Werner's assumption. When we explored the consequences of energy loss and gain in a numerical model, we found that individuals should switch stages at a larger and smaller size, respectively, relative to what Werner's standard theory predicts.</p> <p>Our results suggest that while there is support for Werner's assumption regarding growth rates, mass changes profoundly alter the timing of transitions that are predicted to maximise fitness, and therefore the original model omits an important component that may contribute to the evolution of complex life cycles. Future studies should test for conditions that alter the costs of transitions, so that we can have a better understanding of how mass loss or gain affects fitness.</p>
Figure 2 in Life cycle and morphometric analysis of nymphs of Cynodonmiris corpoicanus Ferreira & Barreto, 2013 (Hemiptera: Miridae)
Figure 2. Principal component analysis (PCA) scores for the nymphae of C. corpoicanus. / Valores del análisis de componentes principales (ACP) de ninfas de C. corpoicanus.
Figures 3-7 in Life cycle and morphometric analysis of nymphs of Cynodonmiris corpoicanus Ferreira & Barreto, 2013 (Hemiptera: Miridae)
Figures 3-7. Nymphal stages of C. corpoicanus. 3. Instar I. Scale: 0.5 mm. 4. Instar II. Scale: 0.85 mm. 5. Instar III. Scale: 1.16 mm. 6. Instar IV. Scale: 1.50 mm. 7. Instar V. Scale: 2.18 mm. / 3. Estadio I. Escala: 0,5 mm. 4. Estadio II. Escala: 0,85 mm. 5. Estadio III. Escala: 1,16 mm. 6. Estadio IV. Escala: 1,50 mm. 7. Estadio V. Escala: 2,18 mm.
Fig. 2 in A modelling approach to describe the Anthonomus eugenii (Coleoptera: Curculionidae) life cycle in plant protection: a priori and a posteriori analysis
Fig. 2. Simulation output from the model (1) evaluating the daily average temperature with the Logan development rate function.
Fig. 1 in A modelling approach to describe the Anthonomus eugenii (Coleoptera: Curculionidae) life cycle in plant protection: a priori and a posteriori analysis
Fig. 1. Simulation output from the model (1) evaluating the daily average temperature with the Briére development rate function.
Fig. 3 in A modelling approach to describe the Anthonomus eugenii (Coleoptera: Curculionidae) life cycle in plant protection: a priori and a posteriori analysis
Fig. 3. Briére development rate function compared with life tables point from Toapanta et al. (2005).
Towards alternative solutions for flaring : life cycle assessment and carbon substance flow analysis of associated gas conversion into C3 chemicals
<p>Supplementary material and used data for the publication.</p>
A comparative analysis testing Werner's theory of complex life cycles
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A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment - supporting material
<p>Publication dataset - A critical perspective on uncertainty appraisal and sensitivity analysis in life cycle assessment (<em>accepted for publication in the Journal of Industrial Ecology</em>).</p>
Data from: Genotypic diversity mitigates negative effects of density on plant performance: a field experiment and life-cycle analysis of common evening primrose Oenothera biennis
1.Genotypic diversity in plant populations is known to enhance plant performance and ecosystem function. Nonetheless, the effect of genotypic diversity has rarely been examined across a population's lifecycle despite the expectation that changing conditions, such as population density, will alter the benefits of diversity. 2.We simultaneously manipulated a component of genotypic diversity (richness, the number of genotypes) and density of common evening primrose Oenothera biennis to address the consequences for herbivory and lifetime fitness in a two-year field experiment that spanned seed germination to life-time fruit production. We genotyped >1100 seedlings with microsatellite DNA markers to determine realized diversity and density in plots sown with O.biennis seeds. Our design achieved quantitative variation in plant density and diversity, with one to 44 individuals established in field plots and two to eight genotypes per polyculture plot (based on microsatellite analysis of reproductive plants). 3.We found a strong interaction between seed density and genetic diversity, with germination and establishment being 24% higher in genetic polycultures than monocultures, but only at low seed density. At high seed density, the opposite pattern emerged, with polycultures having 12% fewer individuals established than monocultures. Initial effects of emergence on plot density persisted through to the fruiting stage. 4.Higher plant densities result in increased mortality, decreased probability of reproduction, decreased plant height, and lower levels of life-time fruit production per plant. Increasing genotypic diversity increased the probability of reproduction overall, and showed a significant interaction with plant density mitigating the negative effects of high density on individual height and lifetime fruit production. 5.Synthesis. Plant density and genotypic diversity interacted from the very earliest stages of seed germination and establishment of O. biennis. This effect persisted over the two-year life-cycle of plants, and genotypic diversity buffered against the negative fitness consequences of high plant density. These results imply a dynamic interplay between the long-held paradigm of density effects in plant ecology and the genetic structure of populations.
Critical analysis of life cycle inventory datasets for organic crop production systems (Data sets)
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Life-cycle assessment of hydrogen systems: A systematic review and meta-regression analysis
<p>The high expectations placed on hydrogen as a clean fuel have led to a growing amount of life-cycle assessment (LCA) studies of hydrogen-related systems. The multiple methodological choices and diverse technical characteristics contribute to a broad set of practices, resulting in significant variability even among similar systems. This study sets the basis for the development of harmonised guidelines for LCA of fuel cells and hydrogen (FCH) systems by analysing current LCA practices. The reviewed literature suggests that previous efforts on harmonisation of LCA methodological choices have led to common practices for certain choices, e.g. functional unit. However, an incomplete definition of some parameters hinders the interpretability of LCA results and hides the potential sources of variability in terms of LCA estimates. In this work, in addition to a systematic literature review of LCA of FCH systems to identify current practices and gaps, sources of variability were investigated for the life-cycle greenhouse gas emissions of hydrogen production systems through a meta-regression analysis (MRA). The MRA results show that the variability of LCA estimates in the literature can be explained by a limited set of qualitative (e.g. implementation of CO<sub>2</sub> capture, among other technological choices) and quantitative (e.g. electricity consumption for hydrogen processing) variables. Although a certain progress towards common methodological choices in LCA of FCH systems was identified, further work is still needed to harmonise practices, as well as to extend the application of the proposed MRA approach to other life-cycle indicators for both identification of main drivers and harmonisation of LCA impact scores.</p>
Database of the work "Sensitivity analysis as support for reliable life cycle cost evaluation an application on eleven nearly zero-energy buildings in Europe"
<p>This database reports the detailed results of the work "Sensitivity analysis as support for reliable life cycle cost evaluation an application on eleven nearly zero-energy buildings in Europe" published on the journal Sustainable Cities and Society.</p> <p>In particular, it includes the main sensitivity indices and life cycle cost values of eleven nZEB buildings across Europe as calculated within the H2020 project CRAVEzero (Cost Reduction and market Acceleration for Viable nearly zero-Energy buildings).</p> <p> </p>
Data from: Genotypic diversity mitigates negative effects of density on plant performance: a field experiment and life-cycle analysis of common evening primrose Oenothera biennis
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Figure 1 in Life cycle and morphometric analysis of nymphs of Cynodonmiris corpoicanus Ferreira & Barreto, 2013 (Hemiptera: Miridae)
Figure 1. Damage caused by Cynodonmiris corpoicanus in oat leaves / Daño causado por Cynodonmiris corpoicanus en hojas de avena.
Dynamic mRNA Expression analysis of cells undergoing synchronous life-cycle differentiation in Trypanosoma brucei
GEO Series GSE17026. Trypanosoma brucei. 25 samples. Type: Expression profiling by array.
Genome-wide Transcriptome Analysis of CD36 Overexpression in HepG2.2.15 Cells to Explore Its Regulation Role of Metabolism and HBV Life Cycle
GEO Series GSE83577. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Life cycle analysis of the model organism Rhodopirellula baltica by transcritpome studies
GEO Series GSE19405. Rhodopirellula baltica SH 1; Rhodopirellula baltica. 8 samples. Type: Expression profiling by array.
Regulatory principles of human mitochondrial gene expression revealed by kinetic analysis of the RNA life cycle
GEO Series GSE224662. Homo sapiens. 98 samples. Type: Expression profiling by high throughput sequencing; Other.
Regulatory principles of human mitochondrial gene expression revealed by kinetic analysis of the RNA life cycle.
GEO Series GSE224689. Homo sapiens. 113 samples. Type: Expression profiling by high throughput sequencing; Other.
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