scholarly journals Stochastic PDE model for spatial population growth in random environments

2015 ◽  
Vol 21 (1) ◽  
pp. 55-65
Author(s):  
Pao-Liu Chow
Keyword(s):  
Population Growth ◽  
Random Environments ◽  
Stochastic Pde ◽  
Pde Model ◽  
Spatial Population

Resource Allocation Optimization Problem on the Population Growth Model

2013 ◽  
Vol 291-294 ◽  
pp. 1507-1513
Author(s):  
Yi Hong Luo ◽  
Shu Guang Zhang
Keyword(s):  
Resource Allocation ◽  
Population Growth ◽  
Growth Model ◽  
Economic Benefit ◽  
Optimization Problem ◽  
Industrial Structure ◽  
Population Models ◽  
Death Process ◽  
Pde Model

In this paper we mainly investigated the resource allocation optimization problem in three population models: death process, PDE model and birth and density-independent growth model. Considering the influence on population growth from different factors, find the best proportion of population to obtain the biggest economic benefit. Furthermore, we consider the effect on resource allocation from one more industrial structure on density-independent growth model. Finally, we compared the above models.

Population growth in random environments

1983 ◽  
Vol 45 (4) ◽  
pp. 635-641 ◽  
Author(s):  
Carlos A. Braumann
Keyword(s):  
Population Growth ◽  
Random Environments

PARAMETER IDENTIFICATION FOR NONLINEAR STOCHASTIC PDE MODEL OF A SPUTTERING PROCESS

2007 ◽  
Vol 40 (5) ◽  
pp. 215-220
Author(s):  
Yiming Lou ◽  
Gangshi Hu ◽  
Panagiotis D. Christofides ◽  
Gerassimos Orkoulas
Keyword(s):  
Parameter Identification ◽  
Stochastic Pde ◽  
Pde Model

scholarly journals Phenotypic diversity and population growth in a fluctuating environment

2011 ◽  
Vol 43 (02) ◽  
pp. 375-398 ◽  
Author(s):  
Clément Dombry ◽  
Christian Mazza ◽  
Vincent Bansaye
Keyword(s):  
Growth Rate ◽  
Population Growth ◽  
Environmental Changes ◽  
Phenotypic Diversity ◽  
Branching Processes ◽  
Optimal Strategies ◽  
Exact Expression ◽  
Random Environments ◽  
Lyapounov Exponent ◽  
Net Growth Rate

Organisms adapt to fluctuating environments by regulating their dynamics, and by adjusting their phenotypes to environmental changes. We model population growth using multitype branching processes in random environments, where the offspring distribution of some organism having trait t ∈ in environment e ∈ ε is given by some (fixed) distribution ϒ t,e on ℕ. Then, the phenotypes are attributed using a distribution (strategy) π t,e on the trait space . We look for the optimal strategy π t,e , t ∈ , e ∈ ε, maximizing the net growth rate or Lyapounov exponent, and characterize the set of optimal strategies. This is considered for various models of interest in biology: hereditary versus nonhereditary strategies and strategies involving or not involving a sensing mechanism. Our main results are obtained in the setting of nonhereditary strategies: thanks to a reduction to simple branching processes in a random environment, we derive an exact expression for the net growth rate and a characterization of optimal strategies. We also focus on typical genealogies, that is, we consider the problem of finding the typical lineage of a randomly chosen organism.

Firing properties of a stochastic PDE model of a rat sensory cortex layer 2/3 pyramidal cell

2004 ◽  
Vol 188 (1-2) ◽  
pp. 117-132 ◽  
Author(s):  
Nicolangelo Iannella ◽  
Henry C Tuckwell ◽  
Shigeru Tanaka
Keyword(s):  
Pyramidal Cell ◽  
Sensory Cortex ◽  
Stochastic Pde ◽  
Pde Model ◽  
Layer 2 ◽  
Firing Properties

THRESHOLD CROSSING PROBABILITIES FOR POPULATION GROWTH MODELS IN RANDOM ENVIRONMENTS

1995 ◽  
Vol 03 (02) ◽  
pp. 505-517 ◽  
Author(s):  
CARLOS A. BRAUMANN
Keyword(s):  
Population Growth ◽  
Growth Models ◽  
Animal Cell ◽  
Growth Parameter ◽  
Minimum Size ◽  
Random Environments ◽  
High Threshold ◽  
Demographic Stochasticity ◽  
Threshold Crossing ◽  
Crossing Probabilities

Consider the general population growth model in a random environment dN/dt= (r+σε(t))Nf(N), N(0)=N0>0, where N=N(t) is the (animal, cell, etc.) population size or biomass at time t≥0, r>0 is an intrinsic growth parameter subjected to environmental random fluctuations approximately described by σε(t) (σ>0 noise intensity parameter, ε(t) standard white noise), and f(N) is a well-behaved density-dependence function. Due to demographic stochasticity and Allee effects, a slightly modified model that corrects for inadequacies at small population sizes is also considered. In many applications (wildlife management, environmental impact assessment, pest control, growth of bacterial cultures, tumor or body growth, etc.), one needs the probability of N(t) ever crossing a given threshold during a given time horizon. We consider the cases of a low threshold N1<N0 (for instance, an extinction threshold or a minimum size required for economical, ecological or recreational reasons) and of a high threshold N h >N0 (for instance, a pest’s damaging level). We also obtain other related threshold crossing probabilities of interest. A reference is made to statistical estimation and hypothesis testing.

Spatial population trends and economic development in Puerto Rico, 1765–2010

2021 ◽  
Author(s):  
Brian Marein
Keyword(s):  
Economic Development ◽  
Puerto Rico ◽  
Population Growth ◽  
Crop Production ◽  
The United States ◽  
Modern Distribution ◽  
Agricultural Employment ◽  
Spatial Population ◽  
The Caribbean ◽  
Stages Of Economic Development

Abstract I use data for consistently defined municipalities to describe spatial patterns in population growth in Puerto Rico across all stages of economic development and rule by Spain and then the United States. The spatial distribution of population began to resemble the modern distribution after the turn of the twentieth century, around the time that municipal population densities diverged. Municipal population growth was positively correlated with crop production in the preindustrial era and was negatively correlated with agricultural employment from 1899 to 1970. Urbanization commenced around 1900, decades earlier than generally believed and before most of the Caribbean and Central America.

Sign in / Sign up

Export Citation Format

Share Document