This is the Logistics model for the country Nigeria over 25 years. Using a density-dependent rate,
At carrying capacity: Birth rate = Death rate. This is why at this point the population at reached a constant (a plateau) because the two rates equate themselves.
Below carrying capacity:Birth rate > death rate. There are enough resources for so the population so max birth rate is reached and more people are being birthed or are migrating into the country
Above carrying capacity: The birth rate < death rate. Nigeria's ecosystem have depleted and not enough to support its population so max death rate is reached.
Using this model, we see how population replenished per person
(Population per capita) decreases as the population nears carrying
capacity.
The dynamics of the human population in Oshawa are influenced by the carrying capacity.
When the population is below the carrying capacity, the birth rate exceeds the death rate, leading to population growth.
At the carrying capacity, the birth rate equals the death rate, and the population stabilizes.
If the population exceeds the carrying capacity, the death rate surpasses the birth rate, causing a population decline.
This model demonstrates how population growth is controlled by the availability of resources and space.
People (Human): Used for stocks like population and carrying capacity. It represents the number of individual humans.
People per person per year (Human / Human / Year): Used for rates like birth and death rates, indicating the number of people added or subtracted per person in the population each year.
People per year (Human / Year): Used for flows like population change, representing the total number of people added or subtracted from the population annually.
This model displays how the population of the Earth changes. With a larger birth rate than death rate the population increases and heads towards K (carrying capacity). If the birth rate is lower than the death rate, the population will slowly diminish and will move away from carrying capacity.
The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
Tanjiopolis is a unique municipality located in northern Canada, known for its extreme seasonal climate where there is six months of very hot summers followed by six months of very cold winters, with no transitional seasons. This distinct environment has driven Tanjiopolis to innovate and thrive, harnessing its natural resources to achieve energy independence.
The municipality has invested heavily in a robust infrastructure of solar and wind generators, complemented by a few nuclear power facilities. The nuclear plants operate at only 10% of their maximum capacity during the summer, as the abundant solar energy meets the municipality's power needs. In contrast, during the winter, the nuclear facilities ramp up to 100% capacity to compensate for the reduced solar output due to limited sunlight.
Tanjiopolis takes pride in its commitment to sustainability, reinforced by a government-mandated policy that requires 2 solar panels per residential building, 4 solar panels per small business building, and 6 solar panels per large business building. This ensures that the municipality can sustain a population of 3 million people entirely through renewable energy sources, maintaining a self-sufficient power grid that operates independently from external systems.
The following model shows us the fictional city in Ontario a municipality called Omnicity with fictional energy values and the relationship between all the energy types used within the city, how it affects the energy grid, with the inflows from the various types of energy the city produces. The power usage coming from Businesses and Residential, whilst the energy produced comes from Wind, Solar, Hydro, Nuclear, Natural Gas, and Micro-generated Solar Electricity from residential housing.
This model simulates the growth of human populations at a global level and a local level (e.g., Oshawa) using logistic growth principles. It includes components for population size, birth rates, death rates, migration for local population, and carrying capacity. Each stock and flow is described with units and explanations.
Star production was greater at the beginning of the universe, and has slowed down as lots of gases are now trapped in living stars. While this isn't true for every galaxy, this is a model simulating a galaxy where star formation rate is decreasing.
Everything is divided by 1 billion as to not have to simulate huge numbers.
This model stimulates the growth of the human population at a large scale, ranging from global to local growth. It is modeled using logistic growth, where the carrying capacity (maximum sustainable population) limits the exponential growth due to available resources.
The dynamics of household water management with constant water supply and usage rates.
This model simulates the management of water in a household. The “Water Reservoir” stock represents the total amount of water available. The “Water Supply” inflow adds water to the reservoir based on the “Supply Rate.” The “Water Usage” outflow removes water from the reservoir based on the “Usage Rate.” By adjusting the “Supply Rate” and “Usage Rate,” users can see how conservation efforts impact water availability over time.
This model displays how the population of the Earth changes. With a larger birth rate than death rate the population increases and heads towards K (carrying capacity). If the birth rate is lower than the death rate, the population will slowly diminish and will move away from carrying capacity.
The dynamics of the human population in Oshawa with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
The dynamics of a moose population with density-dependent birth rate...the birth rate equals the death rate when the population is at carrying capacity; the birth rate is greater than the death rate when the population is below carrying capacity; the birth rate is below the death rate when the population is above carrying capacity.
The dynamics of Oshawa’s population are modeled with a carrying capacity, where population growth is influenced by density-dependent factors. At lower population sizes, the birth rate exceeds the death rate, with a maximum birth rate and a maximum death rate. As the population increases and approaches the carrying capacity, resource limitations cause the birth rate and death rate to equalize. If the population exceeds the carrying capacity, the death rate will surpass the birth rate, gradually reducing population size, stabilizing near equilibrium.
