The dynamics of Toronto’s population involve density-dependent growth, immigration, and emigration. The birth and death rates balance when the population is at carrying capacity; births exceed deaths when the population is below carrying capacity; and deaths exceed births when the population is above carrying capacity. Immigration and emigration provide additional population inflows and outflows based on 2025 data.

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.

Proceso de limpieza de botellones de agua

The dynamics of a human 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 goal of this simulation is to model the population growth of Markham, Ontario, Canada. The model begins with the initial population from 2025 statistics and examines how births, deaths, immigration and emigration affect the population. Births and deaths are represented by using the annual birth and death rate applied to the current population. Immigration and emigration are represented as annual values.

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.

This model shows how the population of Durham Region changes over time using a logistic growth model. The Durham Region Population is the main stock and represents the number of people living in the region. The Change in Durham Population is the flow that represents the yearly increase or decrease in the population. The model uses a Human Maximum Birth Rate and Human Minimum Death Rate to determine population growth. The Durham Carrying Capacity represents the population level that the region could reasonably approach as growth slows. The model also includes a delay in the effects of population density, which represents the idea that changes in population density may take time to affect population growth. The starting population is approximately 800,000 people, and the values used in the model are approximate and intended to demonstrate population dynamics rather than make an exact prediction.

This deterministic model explores how Durham Region's human population could change over 100 years from an illustrative baseline of 800,000 residents. Population is the stock, measured in people. Births and Inmigration add people; Deaths and Outmigration remove people. All four flows are measured in people per year. As the population grows, pressure on housing and services reduces inmigration. A two-year response delay represents the time between changing conditions and decisions to move. This creates a balancing feedback that gradually slows growth.

This model simulates the future trajectory of the global human population using a delayed logistic growth framework. Unlike simple exponential growth, this model assumes that Earth has a finite carrying capacity (K) limited by resources, space, and environmental constraints. It incorporates a generation-time delay to represent the lag between an increase in population density and its feedback effects on birth and death rates (e.g., resource depletion, urbanization, and social changes). The model demonstrates how a delay in density-dependent feedback can cause the human population to overshoot the carrying capacity before stabilizing or oscillating.

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.

This model shows how the population of Durham Region could change over 100 years. Population increases through births and immigration and decreases through deaths and emigration. The model also includes a population capacity, which causes the birth rate to decrease as the population becomes larger. This allows the model to show logistic population growth while also accounting for migration.

ASSUMPTIONS: The population capacity of 1.5 million is a modelling value used to demonstrate logistic growth and is not an official maximum population for Durham Region. The birth rate, death rate, immigration, and emigration values are simplified estimates that remain constant during the simulation. The delay and “population X years ago” parts of the example model were not included because I did not have a reasonable Durham-specific delay value, so the current population is used directly to adjust the birth rate.

This model simulates the population growth in the city of Toronto measured using birth and death rates as well as the rate of people moving in and out of the city. The city's population is the main stock measured in people, while the growth flow is measured in people/year. The carrying capacity represents the maximum potential population that the city's housing capacity could accommodate.