Decoding Fisher’s Fundamental Theorem of Natural Selection
Formula: (varianceFitness, meanFitness) => meanFitness == 0 ? 'Mean fitness cannot be zero' : varianceFitness / meanFitness
Understanding Fisher’s Fundamental Theorem of Natural Selection
Fisher’s Fundamental Theorem of Natural Selection is a cornerstone concept in evolutionary biology, often compared to the law of gravity in physics because of its fundamental importance. Introduced by Ronald A. Fisher in 1930, the theorem connects the change in population mean fitness to the genetic variance in fitness within the population. Let's dive into the formula, its components, and its real world significance.
The Formula and Its Components
The formula essentially states that the rate of increase in mean fitness of a population is equal to the additive genetic variance in fitness:
(varianceFitness, meanFitness) => meanFitness == 0 ? 'Mean fitness cannot be zero' : varianceFitness / meanFitness
This can be broken down into two main components:
- varianceFitness: This is the additive genetic variance in fitness in the population. It measures how much the fitness values vary due to genetic differences. Typically, this is measured in arbitrary fitness units that quantify genetic variations.
- meanFitness: This is the average fitness of the population, calculated by taking the sum of all fitness values and dividing by the number of individuals. This is also measured in fitness units.
By dividing the additive genetic variance in fitness by the mean fitness, the theorem provides a rate of increase in fitness, which helps us understand how natural selection brings evolutionary change.
Clarifying Inputs and Outputs
The inputs and outputs of this formula can be a bit abstract, so let’s make them more tangible:
- varianceFitness
Type: Numeric (measured in arbitrary fitness units)
Example Value: 25.0 (higher values indicate greater genetic diversity in fitness)
- meanFitness
Type: Numeric (measured in arbitrary fitness units)
Example Value: 100.0 (the average fitness of the population)
Rate of increase in mean fitness (numeric, same units as inputs) results from the division. For example, if varianceFitness is 25.0 and meanFitness is 100.0, the rate of increase in mean fitness will be 0.25.
Real World Example
Consider a population of beetles where the fitness of individuals (measured by their reproductive success) varies due to genetic differences. Suppose we have the following data:
varianceFitness: 30.0 fitness units
meanFitness: 120.0 fitness units
Using Fisher’s Fundamental Theorem, we calculate:
(varianceFitness, meanFitness) => meanFitness == 0 ? 'Mean fitness cannot be zero' : 30.0 / 120.0 = 0.25
Thus, the rate of increase in mean fitness of this beetle population due to natural selection is 0.25 fitness units. This means that the mean fitness of the population is expected to increase, reflecting evolutionary adaptation.
Data Table Example
Parameter | Description | Value |
---|---|---|
varianceFitness | Additive genetic variance in fitness | 30.0 |
meanFitness | Mean fitness of the population | 120.0 |
Rate of increase in mean fitness | Result of the theorem | 0.25 |
Common Questions About Fisher’s Fundamental Theorem of Natural Selection
What is the significance of the theorem?
The theorem highlights the power of natural selection in driving evolutionary changes in a population by showing how genetic variance contributes to the increase in mean fitness.
How is fitness measured?
Fitness is typically measured in terms of reproductive success or the number of offspring an individual can produce. It's an abstract value but can be quantified in arbitrary units appropriate for the study.
Is the theorem always accurate?
While it provides a robust framework, real world populations often experience factors like genetic drift, mutations, and environmental changes that can affect fitness and deviate from the ideal scenario described by the theorem.
Summary
Fisher’s Fundamental Theorem of Natural Selection is a seminal principle in evolutionary biology, quantifying how genetic variation drives the adaptive change in populations. By understanding and applying this theorem, biologists can predict and study the evolutionary dynamics of various species.
Tags: Evolution, Biology, Natural Selection