scholarly journals Effects of a Fluctuating Carrying Capacity on the Generalized Malthus-Verhulst Model

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Héctor Calisto ◽  
Kristopher J. Chandía ◽  
Mauro Bologna
Keyword(s):  
Correlation Function ◽  
Population Growth ◽  
Carrying Capacity ◽  
Analytical Expression ◽  
Constant Quantity ◽  
Verhulst Model ◽  
Exponential Correlation Function ◽  
Number Of Individuals

We consider a generalized Malthus-Verhulst model with a fluctuating carrying capacity and we study its effects on population growth. The carrying capacity fluctuations are described by a Poissonian process with an exponential correlation function. We will find an analytical expression for the average of a number of individuals and show that even in presence of a fluctuating carrying capacity the average tends asymptotically to a constant quantity.

ABOUT THE CAPACITY OF THE RECEIVING HOPPER OF THE QUARRY CRUSHING PLANT

ASJ. ◽  
2021 ◽  
Vol 1 (49) ◽  
pp. 49-51
Author(s):  
I. Zharikov
Keyword(s):  
Carrying Capacity ◽  
Open Pit ◽  
Analytical Expression ◽  
Dump Trucks ◽  
Crushing Plant

The results of the analysis of the application of the methods of the theory of queuing for calculating the capacity of the receiving hopper of an open-pit crushing plant used in combination with a combined automobile-conveyor transport are presented. An analytical expression is given for calculating the capacity of the bunker, taking into account the minimum possible duration of the interval between unloading dump trucks into the bunker. The capacity of the receiving hopper of the crushing plant with a capacity of 4300 t / h was determined when working with dump trucks with a carrying capacity of up to 180 tons.

Lessons from a century of conservation translocations

2021 ◽  
Author(s):  
Shane D Morris ◽  
Katherine E. Moseby ◽  
Barry W. Brook ◽  
Christopher N. Johnson
Keyword(s):  
Growth Rate ◽  
Population Growth ◽  
Population Growth Rate ◽  
Historical Context ◽  
Extrinsic Factors ◽  
Global Database ◽  
Terrestrial Vertebrates ◽  
Failure Classification ◽  
High Population Growth ◽  
Number Of Individuals

Translocation—moving individuals for release in different locations—is among the most important conservation interventions for increasing or re-establishing populations of threatened species. However, translocations often fail. To improve their effectiveness, we need to understand the features that distinguish successful from failed translocations. Here, we assembled and analysed a global database of translocations of terrestrial vertebrates (n=514) to assess the effects of various design features and extrinsic factors on success. We analysed outcomes using standardized metrics i.e. a categorical success/failure classification, and population growth rate. Probability of categorical success and population growth rate increased with the total number of individuals released but with diminishing returns above about 20-50 individuals. There has been no increase in numbers released per translocation over time. Positive outcomes—reported success and high population growth—were less likely for translocation in Oceania, possibly because invasive species are a major threat in this region and are difficult to control at translocation sites. Increased rates of categorical reported success and population growth were found for Europe and North America, suggesting the key role of historical context in positive translocation outcomes. Categorical success has increased throughout the 20th century, but that increase may have plateaued at about 75% since about 1990. Our results suggest there is potential for further increase in the success of conservation translocations. This could be best achieved by greater investment in individual projects, as indicated by total number of animals released.

scholarly journals Psi-Caputo Logistic Population Growth Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Muath Awadalla ◽  
Yves Yannick Yameni Noupoue ◽  
Kinda Abu Asbeh
Keyword(s):  
Population Growth ◽  
Fractional Derivative ◽  
Carrying Capacity ◽  
Chinese Population ◽  
Logistic Model ◽  
Logistic Equation ◽  
Logistic Growth ◽  
Growth Equation ◽  
Uniqueness Of The Solution ◽  
Better Than

This article studies modeling of a population growth by logistic equation when the population carrying capacity K tends to infinity. Results are obtained using fractional calculus theories. A fractional derivative known as psi-Caputo plays a substantial role in the study. We proved existence and uniqueness of the solution to the problem using the psi-Caputo fractional derivative. The Chinese population, whose carrying capacity, K, tends to infinity, is used as evidence to prove that the proposed approach is appropriate and performs better than the usual logistic growth equation for a population with a large carrying capacity. A psi-Caputo logistic model with the kernel function x + 1 performed the best as it minimized the error rate to 3.20% with a fractional order of derivative α  = 1.6455.

VORTEX: a computer simulation model for population viability analysis

1993 ◽  
Vol 20 (1) ◽  
pp. 45 ◽  
Author(s):  
RC Lacy
Keyword(s):  
Computer Simulation ◽  
Population Growth ◽  
Simulation Model ◽  
Density Dependence ◽  
Carrying Capacity ◽  
Population Viability Analysis ◽  
Simulation Modelling ◽  
Population Viability ◽  
Reproductive Age ◽  
Viability Analysis

Population Viability Analysis (PVA) is the estimation of extinction probabilities by analyses that incorporate identifiable threats to population survival into models of the extinction process. Extrinsic forces, such as habitat loss, over-harvesting, and competition or predation by introduced species, often lead to population decline. Although the traditional methods of wildlife ecology can reveal such deterministic trends, random fluctuations that increase as populations become smaller can lead to extinction even of populations that have, on average, positive population growth when below carrying capacity. Computer simulation modelling provides a tool for exploring the viability of populations subjected to many complex, interacting deterministic and random processes. One such simulation model, VORTEX, has been used extensively by the Captive Breeding Specialist Group (Species Survival Commission, IUCN), by wildlife agencies, and by university classes. The algorithms, structure, assumptions and applications of VORTEX are described in this paper. VORTEX models population processes as discrete, sequential events, with probabilistic outcomes. VORTEX simulates birth and death processes and the transmission of genes through the generations by generating random numbers to determine whether each animal lives or dies, to determine the number of progeny produced by each female each year, and to determine which of the two alleles at a genetic locus are transmitted from each parent to each offspring. Fecundity is assumed to be independent of age after an animal reaches reproductive age. Mortality rates are specified for each pre-reproductive age-sex class and for reproductive-age animals. Inbreeding depression is modelled as a decrease in viability in inbred animals. The user has the option of modelling density dependence in reproductive rates. As a simple model of density dependence in survival, a carrying capacity is imposed by a probabilistic truncation of each age class if the population size exceeds the specified carrying capacity. VORTEX can model linear trends in the carrying capacity. VORTEX models environmental variation by sampling birth rates, death rates, and the carrying capacity from binomial or normal distributions. Catastrophes are modelled as sporadic random events that reduce survival and reproduction for one year. VORTEX also allows the user to supplement or harvest the population, and multiple subpopulations can be tracked, with user-specified migration among the units. VORTEX outputs summary statistics on population growth rates, the probability of population extinction, the time to extinction, and the mean size and genetic variation in extant populations. VORTEX necessarily makes many assumptions. The model it incorporates is most applicable to species with low fecundity and long lifespans, such as mammals, birds and reptiles. It integrates the interacting effects of many of the deterministic and stochastic processes that have an impact on the viability of small populations, providing opportunity for more complete analysis than is possible by other techniques. PVA by simulation modelling is an important tool for identifying populations at risk of extinction, determining the urgency of action, and evaluating options for management.

scholarly journals Spatial Dynamics Model for Land Carrying Capacity Prediction in Tangerang Selatan City

2019 ◽  
Vol 125 ◽  
pp. 01006
Author(s):  
Rizky Lamonda ◽  
Supriatna ◽  
Revi Hernina ◽  
Masita Dwi Mandini Manessa ◽  
Yoanna Ristya
Keyword(s):  
Population Growth ◽  
Carrying Capacity ◽  
Spatial Dynamics ◽  
Population Data ◽  
Landsat 8 ◽  
Dynamics Model ◽  
Model Method ◽  
The Status ◽  
Land Carrying Capacity ◽  
The Relationship

Tangerang Selatan is a city with the highest economic and population growth in Banten Province which makes the built-up land have high and rapid growth so that it can reduce the land carrying capacity of the city. This causes the predictions on the land carrying capacity need to be done so that the status of land carrying capacity can be detected before declining. The aim of this study is to produce a spatial dynamics model of land carrying capacity in Tangerang Selatan City. This study uses population data of 2008-2018, Landsat 5 TM (2008) images, and Landsat 8 OLI images (2013 and 2018). The land carrying capacity is predicted from 2008-2100 using the system dynamics model method based on the relationship between land requirements based on population growth and land availability based on built-up land, which then converted to spatial to see the spatial distribution with spatial dynamics model method. Research shows that in 2026 the land carrying capacity in Tangerang Selatan City has reached 30% and in 2056 the land carrying capacity has been exhausted.

Carrying Capacity of Resources and Problems of Sustainable Development in Dongting Lake’s Flood Detention Basin

2014 ◽  
Vol 962-965 ◽  
pp. 1961-1964
Author(s):  
Yuan Jun Yu ◽  
Lin Wu
Keyword(s):  
Economic Growth ◽  
Sustainable Development ◽  
Population Growth ◽  
Carrying Capacity ◽  
Control Population ◽  
Growth Mode ◽  
Hunan Province ◽  
Dynamic Changes ◽  
Detention Basin ◽  
Economic Growth Mode

The relative carrying capacity of resources was used to analyze the dynamic changes of Dongting Lake’s flood detention basin. The relative carrying capacity of resources of flood detention basin compared with Hunan province from2004 to 2011 was calculated. The results shown that the flood detention basin is in population relatively surplus state, but its severe overloading in economy resources. The consultation was drawn as the economic compensation should be offer by downstream areas. Flood detention basin should transform economic growth mode, strict control population in resources lack and environmental vulnerability areas should be taken to reduce population growth pressures on resources.

scholarly journals Modeling Bacterial Population Growth from Stochastic Single-Cell Dynamics

2014 ◽  
Vol 80 (17) ◽  
pp. 5241-5253 ◽  
Author(s):  
Antonio A. Alonso ◽  
Ignacio Molina ◽  
Constantinos Theodoropoulos
Keyword(s):  
Differential Equation ◽  
Population Growth ◽  
Single Cell ◽  
Bacterial Cells ◽  
Estimation Of Parameters ◽  
Deterministic Models ◽  
Cell Dynamics ◽  
Bacterial Populations ◽  
Content Type ◽  
Number Of Individuals

ABSTRACTA few bacterial cells may be sufficient to produce a food-borne illness outbreak, provided that they are capable of adapting and proliferating on a food matrix. This is why any quantitative health risk assessment policy must incorporate methods to accurately predict the growth of bacterial populations from a small number of pathogens. In this aim, mathematical models have become a powerful tool. Unfortunately, at low cell concentrations, standard deterministic models fail to predict the fate of the population, essentially because the heterogeneity between individuals becomes relevant. In this work, a stochastic differential equation (SDE) model is proposed to describe variability within single-cell growth and division and to simulate population growth from a given initial number of individuals. We provide evidence of the model ability to explain the observed distributions of times to division, including the lag time produced by the adaptation to the environment, by comparing model predictions with experiments from the literature forEscherichia coli,Listeria innocua, andSalmonella enterica. The model is shown to accurately predict experimental growth population dynamics for both small and large microbial populations. The use of stochastic models for the estimation of parameters to successfully fit experimental data is a particularly challenging problem. For instance, if Monte Carlo methods are employed to model the required distributions of times to division, the parameter estimation problem can become numerically intractable. We overcame this limitation by converting the stochastic description to a partial differential equation (backward Kolmogorov) instead, which relates to the distribution of division times. Contrary to previous stochastic formulations based on random parameters, the present model is capable of explaining the variability observed in populations that result from the growth of a small number of initial cells as well as the lack of it compared to populations initiated by a larger number of individuals, where the random effects become negligible.

scholarly journals DEMOGRAPHIC FACTORS INFLUENCE ON POPULATION ADDED IN SUMBERSARI JEMBER DISTRICT

2018 ◽  
Vol 2 (1) ◽  
pp. 60
Author(s):  
Elan Artono Nurdin ◽  
Fahrudi Ahwan Ikhsan ◽  
Bejo Apriyanto ◽  
Fahmi Arif Kurnianto
Keyword(s):  
Population Growth ◽  
Demographic Factors ◽  
Sampling Technique ◽  
Population Changes ◽  
Purposive Sampling ◽  
Research Areas ◽  
And Migration ◽  
Number Of Individuals ◽  
Selection Of

Population growth is the increasing population changes at any time which is calculated in the number of individuals. This study aimed to determine the effect of demographic factors on the growth of population in the district of Jember in East Java Sumbersari. Selection of research areas using purposive sampling technique which is in District SumbersariJember. The number of samples is equal to the number of population is the whole population in Jember in 2012 - 2016.The results of this study show the influence of demographic factors include fertility, mortality, and migration on population growth is the F> M and positive migration rises (N) in the District SumbersariJember, East Java. Keywords: population growth, demographics, migration

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