Population ( Read ) | Biology | CK Foundation
Population density refers to the number of people living in a particular unit of area . For example, in the United States, the average population See full answer. Ecologists estimate the size and density of populations using quadrats and the mark-recapture Demography: describing populations and how they change A variety of methods can be used to sample populations to determine their size and density. Using this information, we can formulate the following relationship: . The term “urban sprawl” describes the expansion of cities into rural areas. Describe the relationship between population density and petroleum use shown in.
How scientists define and measure population size, density, and distribution in space. Key points A population consists of all the organisms of a given species that live in a particular area. The statistical study of populations and how they change over time is called demography. Two important measures of a population are population size, the number of individuals, and population density, the number of individuals per unit area or volume. Ecologists estimate the size and density of populations using quadrats and the mark-recapture method.
The organisms in a population may be distributed in a uniform, random, or clumped pattern. Uniform means that the population is evenly spaced, random indicates random spacing, and clumped means that the population is distributed in clusters. What is a population? In everyday life, we often think about population as the number of people who live in a particular place—New York City has a population of 8.
Just think—you could double the population of Monowi if you felt like moving there!Population Ecology
In ecology, a population consists of all the organisms of a particular species living in a given area. For instance, we could say that a population of humans lives in New York City, and that another population of humans lives in Gross. We can describe these populations by their size—what we often mean by population when we're talking about towns and cities—as well as by their density—how many people per unit area—and distribution—how clumped or spread out the people are.
Instead, they're studying various kinds of plant, animal, fungal, and even bacterial populations. The statistical study of any population, human or otherwise, is known as demography. Why is demography important? Populations can change in their numbers and structure—for example age and sex distribution—for various reasons.
These changes can affect how the population interacts with its physical environment and with other species. By tracking populations over time, ecologists can see how these populations have changed and may be able to predict how they're likely to change in the future.
Monitoring the size and structure of populations can also help ecologists manage populations—for example, by showing whether conservation efforts are helping an endangered species increase in numbers. In this article, we'll begin our journey through demographics by looking at the concepts of population size, density, and distribution.
We'll also explore some methods ecologists use to determine these values for populations in nature. Population size and density To study the demographics of a population, we'll want to start off with a few baseline measures. In this analysis, we conduct a cross-country, cross-sectional analysis of population density and coverage levels of three maternal health services.
This is the first test of this relationship at a national level. To the degree population density matters, countries with dispersed populations face higher burdens to achieve uniform targets like the MDGs.
Methods We execute a cross-sectional analysis of country-level observations. This is represented by equation 1in which the subscript c denotes a country-specific variable. We further include the total fertility rate and the number of four-wheel vehicles per capita as determinants of demand.
For health spending, we use the natural log of total per-capita health expenditure foras reported by the World Health Organization in real US dollars [ 14 ]. For the number of four-wheel vehicles, hospital beds and total fertility rate, we use data series from IHME [ 13 ]. These independent variables are contemporaneous with the dependent variable because we expect little-to-no time lag in their effect on coverage levels.
A challenge with using population density is in identifying an appropriate metric to represent the concept. Prevailing density metrics may be inappropriate covariates in a cross-country analysis for a variety of reasons. Therefore, the failure to discount uninhabited areas downwardly biases the population-per-area ratio in some countries.
There are attempts to reconcile these differences, but many approaches excessively conflate the relationship between density with wealth [ 17 ]. To address this challenge, we separately use two measures of population density: First, we required a single dataset to provide population data at a global level. This is in contrast to the Gridded Population of the World GPW dataset, which reported data at both higher and idiosyncratic levels of aggregation.
For example, the GPW reports population and area by administrative unit, but the size of these units vary drastically across countries. Only cross-sectional population data from the year is available at our desired granularity, so we calculate our density metrics for the year only.
This is not contemporaneous with our dependent variable, and it could be a limitation of the analysis if population densities changed radically during the decade.
However, our assumption is that our metric of population density did not significantly change over this period of time, especially since it is calculated on a global scale. Moreover, this lag ensures density is predetermined in the data-generating process, which justifies our use of ordinary-least squares estimation.
The rate of decrease is governed by the cosine function.
Population size, density, & dispersal (article) | Khan Academy
So the rate of decrease is initially slow, but then speeds up as grids approach the poles [ 21 ]. We do not believe this change in area is relevant to our analysis, for two reasons. As previously noted, population metrics can be distorted when populations are assigned to uninhabited areas. This incorrect assignment is most likely to occur at the equator because that is where grids are the largest.
Yet even at the equator, grids are small enough one square kilometer that the magnitude of this problem is inherently limited.
Exploring the relationship between population density and maternal health coverage
Also, metrics could be distorted if the grids were trivially small [ 22 ]. This analysis is inherently global in perspective, and even for countries near the poles, the bulk of their population is measured in reasonably-sized grids of close to one square kilometer.
We use the unadjusted data, for two reasons.