Insight diagram

This is a basic BIDE (birth, immigration, death, emigration) model.  Not all parts are implemented, however Birth and Death are.

Clone of Bio 190: BIDE Model With Carrying Capacity
Insight diagram

Rūta Grasberga

Cēsu iedzīvotāju skaita izmaiņas
Insight diagram
InClassExercise--ExponentialGrowthRabbits
Insight diagram
Influence of migration on the number of working-age population.
Clone of Radno sposobno stanovništvo
Insight diagram
UDB101 1d Assignment - Mitchell Collocott
Insight diagram
This simulation examines the caloric well of a given settlement. Just add in a few pieces of information and run the insight simulation.
Simple Caloric Well Simulator
Insight diagram
Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

​Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.
Clone of Clone of Z602 Population with four age groups
Insight diagram
Show relation of birth and death rate over time, creating the elements of the demographic transition. This one is for Australia. You can clone this insight for other nations, just plug in the new crude birth and death rates and find the starting population in 1960.
Demographic Transition-Australia
Insight diagram
Evolution of the world population
Insight diagram
Influence of migration on the number of working-age population.
Radno sposobno stanovništvo
Insight diagram
This is a simulation of growth rate
New York's Population Growth
Insight diagram

This is a basic BIDE (birth, immigration, death, emigration) model.  Not all parts are implemented, however Birth and Death are.

Clone of Bio 190: BIDE Model With Carrying Capacity
Insight diagram
Clone of Clone of BirthRateDeathRateAndR
Insight diagram

This is a basic model for use with our lab section.  The full BIDE options.

Clone of Bio 101: Basic Population Model
Insight diagram

This is a basic model for use with our lab section.  The full BIDE options.

Clone of Bio 101: Basic Population Model
Insight diagram
Simulation of how tiger population and anti poaching efforts effect the black market value of tiger organs.
Clone of Tiger Population and Black Market Value
Insight diagram

The Logistic Map is a polynomial mapping (equivalently, recurrence relation) of degree 2, often cited as an archetypal example of how complex, chaotic behaviour can arise from very simple non-linear dynamical equations. The map was popularized in a seminal 1976 paper by the biologist Robert May, in part as a discrete-time demographic model analogous to the logistic equation first created by Pierre François Verhulst

Mathematically, the logistic map is written

where:

 is a number between zero and one, and represents the ratio of existing population to the maximum possible population at year n, and hence x0 represents the initial ratio of population to max. population (at year 0)r is a positive number, and represents a combined rate for reproduction and starvation. To generate a bifurcation diagram, set 'r base' to 2 and 'r ramp' to 1
To demonstrate sensitivity to initial conditions, try two runs with 'r base' set to 3 and 'Initial X' of 0.5 and 0.501, then look at first ~20 time steps

Clone of The Logistic Map
Insight diagram

Dynamic simulation modelers are particularly interested in understanding and being able to distinguish between the behavior of stocks and flows that result from internal interactions and those that result from external forces acting on a system.  For some time modelers have been particularly interested in internal interactions that result in stable oscillations in the absence of any external forces acting on a system.  The model in this last scenario was independently developed by Alfred Lotka (1924) and Vito Volterra (1926).  Lotka was interested in understanding internal dynamics that might explain oscillations in moth and butterfly populations and the parasitoids that attack them.  Volterra was interested in explaining an increase in coastal populations of predatory fish and a decrease in their prey that was observed during World War I when human fishing pressures on the predator species declined.  Both discovered that a relatively simple model is capable of producing the cyclical behaviors they observed.  Since that time, several researchers have been able to reproduce the modeling dynamics in simple experimental systems consisting of only predators and prey.  It is now generally recognized that the model world that Lotka and Volterra produced is too simple to explain the complexity of most and predator-prey dynamics in nature.  And yet, the model significantly advanced our understanding of the critical role of feedback in predator-prey interactions and in feeding relationships that result in community dynamics.The Lotka–Volterra model makes a number of assumptions about the environment and evolution of the predator and prey populations:

1. The prey population finds ample food at all times.
2. The food supply of the predator population depends entirely on the size of the prey population.
3. The rate of change of population is proportional to its size.
4. During the process, the environment does not change in favour of one species and genetic adaptation is inconsequential.
5. Predators have limitless appetite.
As differential equations are used, the solution is deterministic and continuous. This, in turn, implies that the generations of both the predator and prey are continually overlapping.[23]

Prey
When multiplied out, the prey equation becomes
dx/dtαx - βxy
 The prey are assumed to have an unlimited food supply, and to reproduce exponentially unless subject to predation; this exponential growth is represented in the equation above by the term αx. The rate of predation upon the prey is assumed to be proportional to the rate at which the predators and the prey meet; this is represented above by βxy. If either x or y is zero then there can be no predation.

With these two terms the equation above can be interpreted as: the change in the prey's numbers is given by its own growth minus the rate at which it is preyed upon.

Predators

The predator equation becomes

dy/dt =  - 

In this equation, {\displaystyle \displaystyle \delta xy} represents the growth of the predator population. (Note the similarity to the predation rate; however, a different constant is used as the rate at which the predator population grows is not necessarily equal to the rate at which it consumes the prey). {\displaystyle \displaystyle \gamma y} represents the loss rate of the predators due to either natural death or emigration; it leads to an exponential decay in the absence of prey.

Hence the equation expresses the change in the predator population as growth fueled by the food supply, minus natural death.


Clone of Predator-Prey Model ("Lotka'Volterra")
Insight diagram

Exploring the conditions of permanent coexistence, rather than gradual disappearance of disadvantaged competitors. ​Z506 p32-35 System Zoo 3 by Hartmut Bossel.

Clone of Competition for Resources
Insight diagram

​Predator-prey models are the building masses of the bio-and environments as bio masses are become out of their asset masses. Species contend, advance and scatter essentially to look for assets to support their battle for their very presence. This model is designed to represent the moose and wolf population on Isle Royal. The variables include moose population, wolf population, moose birth rate, wolf birth rate, moose death proportionality constant, and wolf death proportionality constant. This model was adapted from https://insightmaker.com/insight/3A0dqQnXXh8zxWJtkwwAH9/Lotka-Volterra-Model-Prey-Predator-Simulation.

 Looking at Lotka-Volterra Model:

The well known Italian mathematician Vito Volterra proposed a differential condition model to clarify the watched increment in predator fish in the Adriatic Sea during World War I. Simultaneously in the United States, the conditions contemplated by Volterra were determined freely by Alfred Lotka (1925) to portray a theoretical synthetic response wherein the concoction fixations waver. The Lotka-Volterra model is the least complex model of predator-prey communications. It depends on direct per capita development rates, which are composed as f=b−py and g=rx−d. 

A detailed explanation of the parameters:

  • The parameter b is the development rate of species x (the prey) without communication with species y (the predators). Prey numbers are reduced by these collaborations: The per capita development rate diminishes (here directly) with expanding y, conceivably getting to be negative. 
  • The parameter p estimates the effect of predation on x˙/x. 
  • The parameter d is the death rate of species y without connection with species x. 
  • The term rx means the net rate of development of the predator population in light of the size of the prey population.

Reference:

http://www.scholarpedia.org/article/Predator-prey_model

https://insightmaker.com/insight/3A0dqQnXXh8zxWJtkwwAH9/Lotka-Volterra-Model-Prey-Predator-Simulation

Lotka-Volterra Model: Moose-Wolf Simulation
Insight diagram
  • Between 1999 and 2006 Koala population had dropped 26% in Queensland. 
  • By 2008 it was estimated there were around 2300 Koalas with more than a 50% population loss in less than 3 years. 
  • Main threats for Koala survival are a loss of habitat, vehicular trauma, dog attacks, urbanisation, disease and climate change.

    http://www.ehp.qld.gov.au/wildlife/koalas/research/pdf/koala-coast-report2010.pdf

Clone of Koala populations in SE Queensland
Insight diagram
Simulation of how tiger population and anti poaching efforts effect the black market value of tiger organs.
Clone of Clone of Tiger Population and Black Market Value
Insight diagram
Influence of migration on the number of working-age population.
Clone of Clone of Radno sposobno stanovništvo
Insight diagram
Adapted from Hartmut Bossel's "System Zoo 3 Simulation Models, Economy, Society, Development."

​Population model where the population is summarized in four age groups (children, parents, older people, old people). Used as a base population model for dealing with issues such as employment, care for the elderly, pensions dynamics, etc.
Clone of Clone of Z602 Population with four age groups