A functional-structural model of ephemeral seagrass growth influenced by environment.
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ABSTRACT:
SUBMITTER: Whitehead S
PROVIDER: S-EPMC5906912 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background and aimsTurgor pressure within a plant cell represents the key to the mechanistical descriptiion of plant growth, combining the effects of both water and carbon availability. The high level of spatio-temporal variation and diurnal dynamics in turgor pressure within a single plant make it a challenge to model these on the fine spatial scale required for functional-structural plant models (FSPMs). A conceptual model for turgor-driven growth in FSPMs has been established previously, but its practical use has not yet been explored.MethodsA turgor-driven growth model was incorporated in a newly established FSPM for soybean. The FSPM simulates dynamics in photosynthesis, transpiration and turgor pressure in direct relation to plant growth. Comparisons of simulations with field data were used to evaluate the potential and shortcomings of the modelling approach.Key resultsModel simulations revealed the need to include an initial seed carbon contribution, a more realistic sink function, an estimation of respiration, and the distinction between osmotic and structural sugars, in order to achieve a realistic model of plant growth. However, differences between simulations and observations remained in individual organ growth patterns and under different environmental conditions. This exposed the need to further investigate the assumptions of developmental and environmental (in)sensitivity of the parameters, which represent physiological and biophysical organ properties in the model, in future research.ConclusionsThe model in its current form is primarily a diagnostic tool, to better understand and model the behaviour of water relations on the scale of individual plant organs throughout the plant life cycle. Potential future applications include its use as a phenotyping tool to capture differences in plant performance between genotypes and growing environments in terms of specific plant characteristics. Additionally, focused experiments can be used to further improve the model mechanisms to lead to better predictive FSPMs, including scenarios of water deficit.
| S-EPMC7489068 | biostudies-literature
Project description:In this article, we introduce a functional structural equation model for estimating directional relations from multivariate functional data. We decouple the estimation into two major steps: directional order determination and selection through sparse functional regression. We first propose a score function at the linear operator level, and show that its minimization can recover the true directional order when the relation between each function and its parental functions is nonlinear. We then develop a sparse functional additive regression, where both the response and the multivariate predictors are functions and the regression relation is additive and nonlinear. We also propose strategies to speed up the computation and scale up our method. In theory, we establish the consistencies of order determination, sparse functional additive regression, and directed acyclic graph estimation, while allowing both the dimension of the Karhunen-Loéve expansion coefficients and the number of random functions to diverge with the sample size. We illustrate the efficacy of our method through simulations, and an application to brain effective connectivity analysis.
| S-EPMC9017995 | biostudies-literature
Project description:BACKGROUND AND AIMS:Mango (Mangifera indica L.) is the fifth most widely produced fruit in the world. Its cultivation, mainly in tropical and sub-tropical regions, raises a number of issues such as the irregular fruit production across years, phenological asynchronisms that lead to long periods of pest and disease susceptibility, and the heterogeneity of fruit quality and maturity at harvest. To address these issues, we developed an integrative functional-structural plant model that synthesizes knowledge about the vegetative and reproductive development of the mango tree and opens up the possible simulation of cultivation practices. METHODS:We designed a model of architectural development in order to precisely characterize the intricate developmental processes of the mango tree. The appearance of botanical entities was decomposed into elementary stochastic events describing occurrence, intensity and timing of development. These events were determined by structural (position and fate of botanical entities) and temporal (appearance dates) factors. Daily growth and development of growth units and inflorescences were modelled using empirical distributions and thermal time. Fruit growth was determined using an ecophysiological model that simulated carbon- and water-related processes at the fruiting branch scale. KEY RESULTS:The model simulates the dynamics of the population of growth units, inflorescences and fruits at the tree scale during a growing cycle. Modelling the effects of structural and temporal factors makes it possible to simulate satisfactorily the complex interplays between vegetative and reproductive development. The model allowed the characterization of the susceptibility of mango tree to pests and the investigatation of the influence of tree architecture on fruit growth. CONCLUSIONS:This integrative functional-structural model simulates mango tree vegetative and reproductive development over successive growing cycles, allowing a precise characterization of tree phenology and fruit growth and production. The next step is to integrate the effects of cultivation practices, such as pruning, into the model.
| S-EPMC7489065 | biostudies-literature
Project description:In general, the quality of fruits depends on local conditions experienced by the fruit during its development. In cotton, fruit quality, and more specifically the quality of the fibre in the fruit, depends on interactions between fruit position in the plant architecture, temperature and agronomic practices, such as sowing time, mulching with plastic film and topping of the plant's main stem and branches. To quantify this response of cotton fibre quality to environment and management, we developed a simulation model of cotton growth and development, CottonXL. Simulation of cotton fibre quality (strength, length and micronaire) was implemented at the level of each individual fruit, in relation to thermal time (represented by physiological age of the fruit) and prevailing temperature during development of each fruit. Field experiments were conducted in China in 2007 to determine model parameters, and independent data on cotton fibre quality in three cotton producing regions in China were used for model validation. Simulated values for fibre quality closely corresponded to experimental data. Scenario studies simulating a range of management practices predicted that delaying topping times can significantly decrease fibre quality, while sowing date and film mulching had no significant effect. We conclude that CottonXL may be used to explore options for optimizing cotton fibre quality by matching cotton management to the environment, taking into account responses at the level of individual fruits. The model may be used at plant, crop and regional levels to address climate and land-use change scenarios.
| S-EPMC4224667 | biostudies-literature
Project description:For diseases that infect humans or livestock, transmission dynamics are at least partially dependent on human activity and therefore human behaviour. However, the impact of human behaviour on disease transmission is relatively understudied, especially in the context of heterogeneous contact structures such as described by a social network. Here, we use a strategic game, coupled with a simple disease model, to investigate how strategic agent choices impact the spread of disease over a contact network. Using beliefs that are based on disease status and that build up over time, agents choose actions that stochastically determine disease spread on the network. An agent's disease status is therefore a function of both his own and his neighbours actions. The effect of disease on agents is modelled by a heterogeneous payoff structure. We find that the combination of network shape and distribution of payoffs has a non-trivial impact on disease prevalence, even if the mean payoff remains the same. An important scenario occurs when a small percentage (called noncooperators) have little incentive to avoid disease. For diseases that are easily acquired when taking a risk, then even when good behavior can lead to disease eradication, a small increase in the percentage of noncooperators (less than 5%) can yield a large (up to 25%) increase in prevalence.
| S-EPMC4348343 | biostudies-literature
Project description:The structure and function of the microbiome inhabiting the rumen are, amongst other factors, mainly shaped by the animal's feed intake. Describing the influence of different diets on the inherent community arrangement and associated metabolic activities of the most active ruminal fractions (bacteria and archaea) is of great interest for animal nutrition, biotechnology, and climatology. Samples were obtained from three fistulated Jersey cows rotationally fed with corn silage, grass silage or grass hay, each supplemented with a concentrate mixture. Samples were fractionated into ruminal fluid, particle-associated rumen liquid, and solid matter. DNA, proteins and metabolites were analyzed subsequently. DNA extracts were used for Illumina sequencing of the 16S rRNA gene and the metabolomes of rumen fluids were determined by 500 MHz-NMR spectroscopy. Tryptic peptides derived from protein extracts were measured by LC-ESI-MS/MS and spectra were processed by a two-step database search for quantitative metaproteome characterization. Data are available via ProteomeXchange with the identifier PXD006070. Protein- and DNA-based datasets revealed significant differences between sample fractions and diets and affirmed similar trends concerning shifts in phylogenetic composition. Ribosomal genes and proteins belonging to the phylum of Proteobacteria, particularly Succinivibrionaceae, exhibited a higher abundance in corn silage-based samples while fiber-degraders of the Lachnospiraceae family emerged in great quantities throughout the solid phase fractions. The analysis of 8163 quantified bacterial proteins revealed the presence of 166 carbohydrate active enzymes in varying abundance. Cellulosome affiliated proteins were less expressed in the grass silage, glycoside hydrolases appeared in slightest numbers in the corn silage. Most expressed glycoside hydrolases belonged to families 57 and 2. Enzymes analogous to ABC transporters for amino acids and monosaccharides were more abundant in the corn silage whereas oligosaccharide transporters showed a higher abundance in the fiber-rich diets. Proteins involved in carbon metabolism were detected in high numbers and identification of metabolites like short-chain fatty acids, methylamines and phenylpropionate by NMR enabled linkage between producers and products. This study forms a solid basis to retrieve deeper insight into the complex network of microbial adaptation in the rumen.
| S-EPMC5573736 | biostudies-literature
Project description:Coastal vegetative ecosystems are among the most threatened in the world, facing multiple anthropogenic stressors. A good example of this is seagrass, which supports carbon capture, coastal stabilization, and biodiversity, but is declining globally at an alarming rate. To understand the causes and consequences of changes to these ecosystems, we need to determine the linkages between different biotic and abiotic components. We used data on the seagrass, Zostera marina, collected by citizen scientists across 300 km of the south coast of the United Kingdom as a case study. We assembled data on seagrass genotype, phenotype, infauna, and associated bathymetry, light, sea surface temperature, and wave and current energy to test hypotheses on the distribution and diversity of this temperate sub-tidal ecosystem. We found spatial structure in population genetics, evident through local assortment of genotypes and isolation by distance across a broader geographic scale. By integrating our molecular data with information on seagrass phenotype and infauna, we demonstrate that these ecosystem components are primarily linked indirectly through the effects of shared environmental factors. It is unusual to examine genotypic, phenotypic, and environmental data in a single study, but this approach can inform both conservation and restoration of seagrass, as well as giving new insights into a widespread and important ecosystem.
| S-EPMC9386313 | biostudies-literature
Project description:Background and aimsThe structure of a forest stand, i.e. the distribution of tree size features, has strong effects on its functioning. The management of the structure is therefore an important tool in mitigating the impact of predicted changes in climate on forests, especially with respect to drought. Here, a new functional-structural model is presented and is used to assess the effects of management on forest functioning at a national scale.MethodsThe stand process-based model (PBM) CASTANEA was coupled to a stand structure module (SSM) based on empirical tree-to-tree competition rules. The calibration of the SSM was based on a thorough analysis of intersite and interannual variability of competition asymmetry. The coupled CASTANEA-SSM model was evaluated across France using forest inventory data, and used to compare the effect of contrasted silvicultural practices on simulated stand carbon fluxes and growth.Key resultsThe asymmetry of competition varied consistently with stand productivity at both spatial and temporal scales. The modelling of the competition rules enabled efficient prediction of changes in stand structure within the CASTANEA PBM. The coupled model predicted an increase in net primary productivity (NPP) with management intensity, resulting in higher growth. This positive effect of management was found to vary at a national scale across France: the highest increases in NPP were attained in forests facing moderate to high water stress; however, the absolute effect of management on simulated stand growth remained moderate to low because stand thinning involved changes in carbon allocation at the tree scale.ConclusionsThis modelling approach helps to identify the areas where management efforts should be concentrated in order to mitigate near-future drought impact on national forest productivity. Around a quarter of the French temperate oak and beech forests are currently in zones of high vulnerability, where management could thus mitigate the influence of climate change on forest yield.
| S-EPMC4217684 | biostudies-literature
Project description:BackgroundGrowth Mixture Modeling (GMM) is commonly used to group individuals on their development over time, but convergence issues and impossible values are common. This can result in unreliable model estimates. Constraining variance parameters across classes or over time can solve these issues, but can also seriously bias estimates if variances differ. We aimed to determine which variance parameters can best be constrained in Growth Mixture Modeling.MethodsTo identify the variance constraints that lead to the best performance for different sample sizes, we conducted a simulation study and next verified our results with the TRacking Adolescent Individuals' Lives Survey (TRAILS) cohort.ResultsIf variance parameters differed across classes and over time, fitting a model without constraints led to the best results. No constrained model consistently performed well. However, the model that constrained the random effect variance and residual variances across classes consistently performed very poorly. For a small sample size (N = 100) all models showed issues. In TRAILS, the same model showed substantially different results from the other models and performed poorly in terms of model fit.ConclusionsIf possible, a Growth Mixture Model should be fit without any constraints on variance parameters. If not, we recommend to try different variance specifications and to not solely rely on the default model, which constrains random effect variances and residual variances across classes. The variance structure must always be reported Researchers should carefully follow the GRoLTS-Checklist when analyzing and reporting trajectory analyses.
| S-EPMC7659099 | biostudies-literature
Project description:The appearance of animal colour signals depends jointly upon the ambient light spectrum and the signal's reflectance spectra. Light environment heterogeneity might, therefore, allow individuals to enhance their signal by signalling in an environment that increases signal efficacy. We tested this hypothesis by providing male guppies (Poecilia reticulata), a choice of three light environments in which to display their colour signal to females: green, lilac, and clear. We paired males with both receptive and non-receptive females to test whether female response might affect male behavioural decisions. Males preferred the clear environment in all trials and this environment also resulted in males having the highest average visual contrast. Sexual behaviour was influenced by complex interactions between female receptivity, light environment, and male colour pattern contrast. Males spent significantly more time in the environment in which their colour signal had the highest contrast, but only when paired with receptive females. Significant interactions between light environment and individual male colour components were also seen only in receptive trials. Our results suggest that males use light environment to enhance their colour pattern, but only in the presence of receptive females.
| S-EPMC5046890 | biostudies-literature