population density

Examples of population density in the following topics:

• Density-Dependent and Density-Independent Population Regulation

  • Population regulation is a density-dependent process, meaning that population growth rates are regulated by the density of a population.
  • In population ecology, density-dependent processes occur when population growth rates are regulated by the density of a population.
  • Its chances of survival are the same whether the population density is high or low.
  • A dense population that is reduced in a density-independent manner by some environmental factor(s) will be able to recover differently than would a sparse population.
  • In this population of roundworms, fecundity (number of eggs) decreases with population density.

• Population Size and Density

  • Population size and density are the two most important statistics scientists use to describe and understand populations.
  • Individuals in a low-density population are thinly dispersed; hence, they may have more difficulty finding a mate compared to individuals in a higher-density population.
  • Many factors influence density, but, as a rule-of-thumb, smaller organisms have higher population densities than do larger organisms .
  • Analyses of sample data enable scientists to infer population size and population density about the entire population.
  • Scientist uses a quadrat to measure plant population size and density

• U.S. Urban Patterns

  • Census Bureau classifies areas as urban or rural based on population size and density.
  • Usually, this type of population center is associated with a cluster of industrial and cultural enterprises.
  • Other definitions may consider total population size or population density.
  • The Census Bureau defines "urban areas" as areas with a population density of at least 1,000 people per square mile and at least 2,500 total people.
  • For example, the city of Greenville, South Carolina has a city population under 60,000 and an urbanized area population of over 300,000, while Greensboro, North Carolina has a city population over 200,000 and an urbanized area population of around 270,000.

• Density

  • If we are comparing two populations, and we note that there are many actors in one that are not connected to any other ("isolates"), and in the other population most actors are embedded in at least one dyad -- we would likely conclude that social life is very different in the two populations.
  • Measuring the density of a network gives us a ready index of the degree of dyadic connection in a population.
  • Network>Cohesion>Density is a useful tool for calculating the density of whole populations, or of partitions.
  • We can see that the three sub-populations appear to have some differences.
  • Governmental generalists (block 1) have quite dense in and out ties to one another, and to the other populations; non-government generalists (block 2) have out-ties among themselves and with block 1, and have high densities of in-ties with all three sub-populations.

• Overcoming Density-Dependent Regulation

  • Humans have exceeded density-dependent limits on population by enacting various environmental changes to accommodate our needs for hygiene, shelter, and food.
  • This capability is an underlying reason for human population growth as humans are able to overcome density-dependent limits on population growth, in contrast with all other organisms.
  • Migration has also contributed to human population growth.
  • In the fourteenth century, the bubonic plague killed as many as 100 million people: between 30 to 60 percent of Europe's population.
  • Describe ways in which humans overcome density-dependent regulation of population size

• The Density Scale

  • Density estimation is the construction of an estimate based on observed data of an unobservable, underlying probability density function.
  • Histograms are used to plot the density of data, and are often a useful tool for density estimation.
  • Density estimation is the construction of an estimate based on observed data of an unobservable, underlying probability density function.
  • The unobservable density function is thought of as the density according to which a large population is distributed.
  • The data are usually thought of as a random sample from that population.

• From histograms to continuous distributions

  • This suggests the population height as a continuous numerical variable might best be explained by a curve that represents the outline of extremely slim bins.
  • This smooth curve represents a probability density function (also called a density or distribution), and such a curve is shown in Figure 2.28 overlaid on a histogram of the sample.
  • A density has a special property: the total area under the density's curve is 1.
  • Density for heights in the US adult population with the area between 180 and 185 cm shaded.

• Hypotheses about one mean or density

  • We may want to test hypotheses about the density or mean tie strength of a network.
  • Network>Compare densities>Against theoretical parameter performs a statistical test to compare the value of a density or average tie strength observed in a network against a test value.
  • But, perhaps the difference between what we see (density = .544) and what the theory predicts (density = 1.000) is due to random variation (perhaps when we collected the information).
  • The "Expected density" is the value against which we want to test.
  • How often would a difference this large happen by random sampling variation, if the null hypothesis (density = 1.000) was really true in the population?

• Continuous Sampling Distributions

  • In the previous section, we created a sampling distribution out of a population consisting of three pool balls.
  • Now we will consider sampling distributions when the population distribution is continuous.
  • Therefore, these values are called probability densities rather than probabilities.
  • A probability density function, or density of a continuous random variable, is a function that describes the relative likelihood for this random variable to take on a given value.
  • Boxplot and probability density function of a normal distribution $N(0, 2)$.

• Density

  • The density of a binary network is simply the proportion of all possible ties that are actually present.
  • Network>Cohesion>Density is a quite powerful tool for calculating densities.
  • The Network>Cohesion>Density algorithm also can be used to calculate the densities within partitions or blocks by specifying the file name of an attribute data set that contains the node name and partition number.
  • That is, the density tool can be used to calculate within and between block densities for data that are grouped.
  • Or, it may indicate that the population we are studying is really composed of more than one sub-populations.