Darwinian fitness

Examples of Darwinian fitness in the following topics:

• Natural Selection and Adaptive Evolution

  • Natural selection acts at the level of the individual; it selects for individuals with greater contributions to the gene pool of the next generation, known as an organism's evolutionary fitness (or Darwinian fitness).
  • Fitness is often quantifiable and is measured by scientists in the field.
  • However, it is not the absolute fitness of an individual that counts, but rather how it compares to the other organisms in the population.
  • This concept, called relative fitness, allows researchers to determine which individuals are contributing additional offspring to the next generation and, thus, how the population might evolve.

• Sociobiology

  • Within the study of human societies, sociobiology is very closely allied to the fields of Darwinian anthropology, human behavioral ecology, and evolutionary psychology.
  • Critics also see parallels between sociobiology and biological determinism as a philosophy underlying the social Darwinian and eugenics movements of the early 20th century as well as controversies in the history of intelligence testing.

• The Interactionist Perspective

  • Park and fellow sociologist Ernest Burgess suggested that cities were governed by many of the same forces of Darwinian evolution evident in ecosystems.

• The Changing Definitions of Race

  • The advent of Darwinian models of evolution and Mendelian genetics, however, called into question the scientific validity of both characteristics and required a radical reconsideration of race.

• Social Darwinism in America

  • Names such as "Darwinian collectivism" or "Reform Darwinism" have been suggested to describe these views, in order to differentiate them from the individualist type of social Darwinism.
  • Describe the role that Darwinian ideas about natural selection had in the social science and public policy of the late 19th-century

• Selective Breeding

  • Dallinger concluded that he had found evidence for Darwinian adaptation in his incubator, and that the organisms had adapted to live in a high-temperature environment .

• Tactics for Improving Fit

  • The first step in improving fit for a given job design is training.
  • Job analysis employs a series of steps which enable a supervisor to assess a given employee/job fit and to improve the fit, if necessary.
  • Observation: The simplest method of assessing how a job and employee fit is observing the employee at work.
  • Checklist: Another method of improving job fit is to create a checklist.
  • Employee questionnaires can be a useful method of assessing job fit.

• Hybrid Zones

  • Over time, two species may further diverge or reconnect, depending on the fitness strength and the reproductive barriers of the hybrids.
  • Over time, the hybrid zone may change depending on the fitness strength and the reproductive barriers of the hybrids .
  • Hybrids can have less fitness, more fitness, or about the same fitness level as the purebred parents.
  • If the hybrids are as fit or more fit than the parents, or the reproductive barriers weaken, the two species may fuse back into one species (reconnection).
  • Discuss how the fitness of a hybrid will lead to changes in the hybrid zone over time

• The Chi-Square Distribution: Comparison Summary of the Chi-Square Tests Goodness-of-Fit, Independence and Homogeneity

  • Goodness-of-Fit: Use the Goodness-of-Fit Test to decide whether a population with unknown distribution "fits" a known distribution.
  • Goodness-of-Fit is typically used to see if the population is uniform (all outcomes occur with equal frequency), the population is normal, or the population is the same as another population with known distribution.
  • The null and alternative hypotheses are: Ho: The population fits the given distribution.
  • Ha: The population does not fit the given distribution.

• The Equation of a Line

  • In statistics, linear regression can be used to fit a predictive model to an observed data set of $y$ and $x$ values.
  • Simple linear regression fits a straight line through the set of $n$ points in such a way that makes the sum of squared residuals of the model (that is, vertical distances between the points of the data set and the fitted line) as small as possible.
  • The slope of the fitted line is equal to the correlation between $y$ and $x$ corrected by the ratio of standard deviations of these variables.
  • The intercept of the fitted line is such that it passes through the center of mass $(x, y)$ of the data points.
  • If the goal is prediction, or forecasting, linear regression can be used to fit a predictive model to an observed data set of $y$ and $X$ values.