scholarly journals Review on mathematical modeling of honeybee population dynamics

2021 ◽  
Vol 18 (6) ◽  
pp. 9606-9650
Author(s):  
Jun Chen ◽  
◽  
Gloria DeGrandi-Hoffman ◽  
Vardayani Ratti ◽  
Yun Kang ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Population Dynamics ◽  
Mathematical Models ◽  
Computational Models ◽  
Pesticide Exposure ◽  
Colony Growth ◽  
Ecological Communities ◽  
Natural Ecosystems ◽  
Colony Losses ◽  
Honeybee Population

<abstract><p>Honeybees have an irreplaceable position in agricultural production and the stabilization of natural ecosystems. Unfortunately, honeybee populations have been declining globally. Parasites, diseases, poor nutrition, pesticides, and climate changes contribute greatly to the global crisis of honeybee colony losses. Mathematical models have been used to provide useful insights on potential factors and important processes for improving the survival rate of colonies. In this review, we present various mathematical tractable models from different aspects: 1) simple bee-only models with features such as age segmentation, food collection, and nutrient absorption; 2) models of bees with other species such as parasites and/or pathogens; and 3) models of bees affected by pesticide exposure. We aim to review those mathematical models to emphasize the power of mathematical modeling in helping us understand honeybee population dynamics and its related ecological communities. We also provide a review of computational models such as VARROAPOP and BEEHAVE that describe the bee population dynamics in environments that include factors such as temperature, rainfall, light, distance and quality of food, and their effects on colony growth and survival. In addition, we propose a future outlook on important directions regarding mathematical modeling of honeybees. We particularly encourage collaborations between mathematicians and biologists so that mathematical models could be more useful through validation with experimental data.</p></abstract>

scholarly journals Demographic Dynamics in Multitype Populations with Migrations

Mathematics ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 246
Author(s):  
Manuel Molina-Fernández ◽  
Manuel Mota-Medina
Keyword(s):  
Mathematical Modeling ◽  
Population Dynamics ◽  
Branching Process ◽  
Probability Model ◽  
Biological Systems ◽  
Research Work ◽  
General Setting ◽  
Process Theory ◽  
Multitype Branching Process ◽  
Demographic Dynamics

This research work deals with mathematical modeling in complex biological systems in which several types of individuals coexist in various populations. Migratory phenomena among the populations are allowed. We propose a class of mathematical models to describe the demographic dynamics of these type of complex systems. The probability model is defined through a sequence of random matrices in which rows and columns represent the various populations and the several types of individuals, respectively. We prove that this stochastic sequence can be studied under the general setting provided by the multitype branching process theory. Probabilistic properties and limiting results are then established. As application, we present an illustrative example about the population dynamics of biological systems formed by long-lived raptor colonies.

scholarly journals Modeling the ecology of parasitic plasmids

2021 ◽  
Author(s):  
Jaime G. Lopez ◽  
Mohamed S. Donia ◽  
Ned S. Wingreen
Keyword(s):  
Mathematical Modeling ◽  
Population Dynamics ◽  
Tragedy Of The Commons ◽  
Fitness Costs ◽  
Metapopulation Model ◽  
The Commons ◽  
Modern Biotechnology ◽  
Natural Ecology ◽  
Unique Plasmid ◽  
Competing Populations

AbstractPlasmids are autonomous genetic elements that can be exchanged between microorganisms via horizontal gene transfer (HGT). Despite the central role they play in antibiotic resistance and modern biotechnology, our understanding of plasmids’ natural ecology is limited. Recent experiments have shown that plasmids can spread even when they are a burden to the cell, suggesting that natural plasmids may exist as parasites. Here, we use mathematical modeling to explore the ecology of such parasitic plasmids. We first develop models of single plasmids and find that a plasmid’s population dynamics and optimal infection strategy are strongly determined by the plasmid’s HGT mechanism. We then analyze models of co-infecting plasmids and show that parasitic plasmids are prone to a “tragedy of the commons” in which runaway plasmid invasion severely reduces host fitness. We propose that this tragedy of the commons is averted by selection between competing populations and demonstrate this effect in a metapopulation model. We derive predicted distributions of unique plasmid types in genomes—comparison to the distribution of plasmids in a collection of 17,725 genomes supports a model of parasitic plasmids with positive plasmid–plasmid interactions that ameliorate plasmid fitness costs or promote the invasion of new plasmids.

scholarly journals A Tool for the Analysis and Characterization of School Mathematical Models

Mathematics ◽  
2021 ◽  
Vol 9 (13) ◽  
pp. 1569
Author(s):  
Jesús Montejo-Gámez ◽  
Elvira Fernández-Ahumada ◽  
Natividad Adamuz-Povedano
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Educational Research ◽  
Analysis Procedure ◽  
Real System ◽  
The Real ◽  
School Model ◽  
Corresponding Analysis ◽  
Three Components

This paper shows a tool for the analysis of written productions that allows for the characterization of the mathematical models that students develop when solving modeling tasks. For this purpose, different conceptualizations of mathematical models in education are discussed, paying special attention to the evidence that characterizes a school model. The discussion leads to the consideration of three components, which constitute the main categories of the proposed tool: the real system to be modeled, its mathematization and the representations used to express both. These categories and the corresponding analysis procedure are explained and illustrated through two working examples, which expose the value of the tool in establishing the foci of analysis when investigating school models, and thus, suggest modeling skills. The connection of this tool with other approaches to educational research on mathematical modeling is also discussed.

scholarly journals Sublethal concentrations of clothianidin affect honey bee colony growth and hive CO2 concentration

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
William G. Meikle ◽  
John J. Adamczyk ◽  
Milagra Weiss ◽  
Janie Ross ◽  
Chris Werle ◽  
...  
Keyword(s):  
Honey Bee ◽  
Pesticide Exposure ◽  
Co2 Concentration ◽  
Colony Growth ◽  
Control Group ◽  
Brood Production ◽  
Weight Losses ◽  
Bee Colony ◽  
The Impact ◽  
Bee Colonies

AbstractThe effects of agricultural pesticide exposure upon honey bee colonies is of increasing interest to beekeepers and researchers, and the impact of neonicotinoid pesticides in particular has come under intense scrutiny. To explore potential colony-level effects of a neonicotinoid pesticide at field-relevant concentrations, honey bee colonies were fed 5- and 20-ppb concentrations of clothianidin in sugar syrup while control colonies were fed unadulterated syrup. Two experiments were conducted in successive years at the same site in southern Arizona, and one in the high rainfall environment of Mississippi. Across all three experiments, adult bee masses were about 21% lower among colonies fed 20-ppb clothianidin than the untreated control group, but no effects of treatment on brood production were observed. Average daily hive weight losses per day in the 5-ppb clothianidin colonies were about 39% lower post-treatment than in the 20-ppb clothianidin colonies, indicating lower consumption and/or better foraging, but the dry weights of newly-emerged adult bees were on average 6–7% lower in the 5-ppb group compared to the other groups, suggesting a nutritional problem in the 5-ppb group. Internal hive CO2 concentration was higher on average in colonies fed 20-ppb clothianidin, which could have resulted from greater CO2 production and/or reduced ventilating activity. Hive temperature average and daily variability were not affected by clothianidin exposure but did differ significantly among trials. Clothianidin was found to be, like imidacloprid, highly stable in honey in the hive environment over several months.

Call for Manuscripts for the 2015 Focus Issue: Mathematical Modeling

2013 ◽  
Vol 18 (9) ◽  
pp. 571
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Real World ◽  
The Real

This call for manuscripts is requesting articles that address how to use mathematical models to analyze, predict, and resolve issues arising in the real world.

Current trends in mathematical modeling of tumor–microenvironment interactions: a survey of tools and applications

2010 ◽  
Vol 235 (4) ◽  
pp. 411-423 ◽  
Author(s):  
Katarzyna A Rejniak ◽  
Lisa J McCawley
Keyword(s):  
Mathematical Modeling ◽  
Protein Interactions ◽  
Computational Models ◽  
Chemical Species ◽  
Tumor Stroma ◽  
Cellular Functions ◽  
Complex Interactions ◽  
Expanding Population

In its simplest description, a tumor is comprised of an expanding population of transformed cells supported by a surrounding microenvironment termed the tumor stroma. The tumor microcroenvironment has a very complex composition, including multiple types of stromal cells, a dense network of various extracellular matrix (ECM) fibers interpenetrated by the interstitial fluid and gradients of several chemical species that either are dissolved in the fluid or are bound to the ECM structure. In order to study experimentally such complex interactions between multiple players, cancer is dissected and considered at different scales of complexity, such as protein interactions, biochemical pathways, cellular functions or whole organism studies. However, the integration of information acquired from these studies into a common description is as difficult as the disease itself. Computational models of cancer can provide cancer researchers with invaluable tools that are capable of integrating the complexity into organizing principles as well as suggesting testable hypotheses. We will focus in this Minireview on mathematical models in which the whole cell is a main modeling unit. We will present a current stage of such cell-focused mathematical modeling incorporating different stromal components and their interactions with growing tumors, and discuss what modeling approaches can be undertaken to complement the in vivo and in vitro experimentation.

Mathematical Alternatives to Standard Probability that Provide Selectable Degrees of Precision

2017 ◽  
Author(s):  
Terrence Fine
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Models ◽  
Theory Of Measurement ◽  
Belief Functions ◽  
Standard Practice ◽  
Mathematical Probability ◽  
Comparative Probability ◽  
The Relationship ◽  
Standard Probability

This chapter challenges the nearly universal reliance upon standard mathematical probability for mathematical modeling of chance and uncertain phenomena, and offers four alternatives. In standard practice, precise assignments are made inappropriately, even to the occurrences of events that may be unobservable in principle. Four familiar examples of chance or uncertain phenomena are discussed, about which this is true. The theory of measurement provides an understanding of the relationship between the accuracy of information and the precision with which the phenomenon under examination should be modeled mathematically. The model of modal or classificatory probability offers the least precision. Comparative probability, plausibility/belief functions and upper/lower probabilities are carefully considered. The selectable precision of these alternative mathematical models of chance and uncertainty makes for an improved range of levels of accuracy in modeling the empirical domain phenomena of chance, uncertainty, and indeterminacy. Knowledge of such models encourages further thought in this direction.

scholarly journals Population Dynamics Based on Resource Availability & Founding Effects: Live & Computational Models

2016 ◽  
Vol 78 (5) ◽  
pp. 396-403 ◽  
Author(s):  
Samuel Potter ◽  
Rebecca M. Krall ◽  
Susan Mayo ◽  
Diane Johnson ◽  
Kim Zeidler-Watters ◽  
...  
Keyword(s):  
Population Dynamics ◽  
Computer Literacy ◽  
Computational Models ◽  
Computational Simulation ◽  
School Science ◽  
Simulation Software ◽  
High School Science ◽  
Initial Population ◽  
College Level ◽  
Factors Affecting

With the looming global population crisis, it is more important now than ever that students understand what factors influence population dynamics. We present three learning modules with authentic, student-centered investigations that explore rates of population growth and the importance of resources. These interdisciplinary modules integrate biology, mathematics, and computer-literacy concepts aligned with the Next Generation Science Standards. The activities are appropriate for middle and high school science classes and for introductory college-level biology courses. The modules incorporate experimentation, data collection and analysis, drawing conclusions, and application of studied principles to explore factors affecting population dynamics in fruit flies. The variables explored include initial population structure, food availability, and space of the enclosed population. In addition, we present a computational simulation in which students can alter the same variables explored in the live experimental modules to test predictions on the consequences of altering the variables. Free web-based graphing (Joinpoint) and simulation software (NetLogo) allows students to work at home or at school.

scholarly journals Mathematical Modelling and Prediction in Infectious Disease Epidemiology

2019 ◽  
Vol 23 (3) ◽  
pp. 328-334
Author(s):  
E. Ya. Yanchevskaya ◽  
O. A. Mesnyankina
Keyword(s):  
Mathematical Modeling ◽  
Infectious Disease ◽  
Mathematical Modelling ◽  
Mathematical Models ◽  
Modern World ◽  
Urgent Problem ◽  
Stages Of Development ◽  
Historical Aspects ◽  
Disease Epidemiology ◽  
Infectious Disease Epidemiology

Mathematical modeling of diseases is an urgent problem in the modern world. More and more researchers are turning to mathematical models to predict a particular disease, as they help the most correct and accurate study of changes in certain processes occurring in society. Mathematical modeling is indispensable in certain areas of medicine, where real experiments are impossible or difficult, for example, in epidemiology. The article is devoted to the historical aspects of studying the possibilities of mathematical modeling in medicine. The review demonstrates the main stages of development, achievements and prospects of this direction.

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