scholarly journals CONTRIBUTION OF MODELING FOR A BETTER UNDERSTANDING OF MICROBIAL ECOSYSTEMS

2019 ◽  
pp. 14-22
Author(s):  
Jérôme Harmand
Keyword(s):  
Mass Balance ◽  
Large Class ◽  
Probabilistic Approach ◽  
Microbial Interactions ◽  
Mathematical Ecology ◽  
Community Assemblage ◽  
Competition Theory ◽  
Model Based ◽  
Microbial Ecosystems ◽  
Experimental Parameters

This paper aims at presenting how a number of recent modeling approaches can be used for better understanding microbial ecosystems dynamics. In first part, an important question – the ability of certain ecosystems to exhibit overyielding – is investigated using a model-based approach. It is shown that classical competition theory cannot explain such phenomenon, thus invalidating a large class of classical mass-balance-based models, Rapaport et al. (2019). In second part, we show how new combinatorial approaches can be used to find the best combination of species of a functional ecosystem with limited complexity. More precisely classification approaches inspired from the work by Jaillard et al. (2018) are used and illustrated with simulations. Their robustness with respect to a number of experimental parameters (investigated in simulation) is studied. For ecosystems with higher richness, we show how another probabilistic approach proposed by Jaillard et al. (2014) may be useful. Keywords: Modeling, microbial ecosystems, microbial interactions, mathematical ecology, diversity, community assemblage.

scholarly journals The impact of interactions on invasion and colonization resistance in microbial communities

2020 ◽  
Author(s):  
Helen Kurkjian ◽  
M. Javad Akbari ◽  
Babak Momeni
Keyword(s):  
Microbial Communities ◽  
Human Health ◽  
Microbial Interactions ◽  
Community Resident ◽  
Colonization Resistance ◽  
Community Or ◽  
Microbial Species ◽  
Model Based ◽  
Human Microbiota ◽  
The Impact

AbstractIn human microbiota, the prevention or promotion of invasions can be crucial to human health. Invasion outcomes, in turn, are impacted by the composition of resident communities and interactions among resident microbes. Microbial communities differ from communities composed of other types of organisms in that many microbial interactions are mediated by chemicals that are released into or consumed from the environment. We ask what determines invasion outcomes in such microbial communities. Here, we use a model based on chemical-mediated interactions among microbial species to assess the impact of positive and negative interactions on invasion outcomes. We classified invasion outcomes as resistance, augmentation, displacement, or disruption depending on whether the richness of the resident community was maintained or dropped and whether the invader was maintained in the community or went extinct. We found that as the number of invaders increased relative to size of the resident community, resident communities were increasingly disrupted. As facilitation of the invader by the resident community increased, resistance outcomes were replaced by displacement and augmentation. By contrast, as facilitation increased among residents, displacement outcomes shifted to resistance. When facilitation of the resident community by the invader was eliminated, augmentation outcomes were replaced by displacement outcomes, while when inhibition of residents by invaders was eliminated, there was little change in the frequency of invasion outcomes. These results suggest that a better understanding of the chemical-mediated interactions within resident communities and between residents and invaders is crucial to predicting the success of invasions into microbial communities.

scholarly journals A Computationally-Efficient Probabilistic Approach to Model-Based Damage Diagnosis

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
James E. Warner ◽  
Geoffrey F. Bomarito ◽  
Jacob D. Hochhalter ◽  
William P. Leser ◽  
Patrick E. Leser ◽  
...  
Keyword(s):  
Probability Distributions ◽  
Probabilistic Approach ◽  
Measurement Data ◽  
Image Correlation ◽  
Sensor Data ◽  
Fe Model ◽  
Computationally Efficient ◽  
Damage Diagnosis ◽  
Full Field ◽  
Model Based

This work presents a computationally-efficient, probabilistic approach to model-based damage diagnosis. Given measurement data, probability distributions of unknown damage parameters are estimated using Bayesian inference and Markov chain Monte Carlo (MCMC) sampling. Substantial computational speedup is obtained by replacing a three-dimensional finite element (FE) model with an efficient surrogate model. While the formulation is general for arbitrary component geometry, damage type, and sensor data, it is applied to the problem of strain-based crack characterization and experimentally validated using full-field strain data from digital image correlation (DIC). Access to full-field DIC data facilitates the study of the effectiveness of strain-based diagnosis as the distance between the location of damage and strain measurements is varied. The ability of the framework to accurately estimate the crack parameters and effectively capture the uncertainty due to measurement proximity and experimental error is demonstrated. Furthermore, surrogate modeling is shown to enable diagnoses on the order of seconds and minutes rather than several days required with the FE model.

scholarly journals Glacier surface mass balance modeling in the inner tropics using a positive degree-day approach

2016 ◽  
Author(s):  
L. Maisincho ◽  
V. Favier ◽  
P. Wagnon ◽  
V. Jomelli ◽  
R. Basantes Serrano ◽  
...  
Keyword(s):  
Wind Speed ◽  
Mass Balance ◽  
Shortwave Radiation ◽  
Surface Mass Balance ◽  
Glacier Surface ◽  
Surface Mass ◽  
Model Based ◽  
Degree Day ◽  
Positive Degree ◽  
Snow And Ice

Abstract. We present a basic ablation model combining a positive degree-day approach to calculate melting and a simple equation based on wind speed to compute sublimation. The model was calibrated at point scale (4900 m a.s.l.) on Antizana Glacier 15 (0.28 km2; 0°28' S, 78°09' W) with data from March 2002 to August 2003 and validated with data from January to November 2005. Cross validation was performed by interchanging the calibration and validation periods. Optimization of the model based on the calculated surface energy balance allowed degree-day factors to be retrieved for snow and ice, and suggests that melting started when daily air temperature was still below 0 °C, because incoming shortwave radiation was intense around noon and resulted in positive temperatures for a few hours a day. The model was then distributed over the glacier and applied to the 2000–2008 period using meteorological inputs measured on the glacier foreland to assess to what extent this approach is suitable for quantifying glacier surface mass balance in Ecuador. Results showed that a model based on temperature, wind speed, and precipitation is able to reproduce a large part of surface mass-balance variability of Antizana Glacier 15 even though the melting factors for snow and ice may vary with time. The model performed well because temperatures were significantly correlated with albedo and net shortwave radiation. Because this relationship disappeared when strong winds result in mixed air in the surface boundary layer, this model should not be extrapolated to other tropical regions where sublimation increases during a pronounced dry season or where glaciers are located above the mean freezing level.

Sign in / Sign up

Export Citation Format

Share Document