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Evolution & Population Genetics

Mean Population Fitness Contribution Workbench

Weight comparable type-specific fitness components by entered population frequencies or observed type counts and expose every contribution to the mean.

Biology · experimental measurements

Make the normalization denominator used by deterministic selection arithmetic explicit without assuming equilibrium or independent observations.

Private calculations in your browser · explicit inputs and model boundaries
Example preview · Three genotype frequenciesContribution of each entered type to the mean fitness component
AA0.36 relative viability
Aa0.432 relative viability
aa0.096 relative viability

Each bar is fiWi in relative viability; the bar sum is W̄ = 0.888. Bar size reflects both starting share and the entered component.

  1. 1EnterProvide the known values
  2. 2CalculateResults update automatically
  3. 3VerifyReview the details and units
Try an example

Type frequency and fitness records
1 row
Row 1

Empty rows are ignored until edited. Keep commas and tabs out of individual entries; use the paste view for comma- or tab-separated records.

Calculation result

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Understand the relationship

The reasoning behind the result

The mean is frequency weighted

W̄ = Σ fiWi

Each type contributes its pre-selection share multiplied by its comparable fitness component. Counts are converted to shares using the total observed count.

An unweighted mean of type fitnesses answers a different question unless all type shares are equal.

The same mean normalizes selected shares

f′i = fiWi/W̄

When the weighted mean is positive, each contribution divided by the mean gives its normalized share after the declared weighting step. These shares sum to one.

If every component is zero, the mean is zero and post-selection shares are undefined rather than fabricated.

Equilibrium is not inserted

Entered genotype frequencies are used exactly as supplied. The calculator does not replace them with Hardy–Weinberg proportions or infer alleles from type labels.

Observed counts may contain sampling error, dependence or structure that this descriptive weighting does not model.

Mean fitness is model specific

The output can be an absolute component with units or a relative component, depending on the entries. It is not automatically lifetime fitness or a scalar measure of population health.

Changing environment, stage or included type set can change the result.

Follow the numbers

Weight three genotype viability components

  1. The entered frequencies 0.36, 0.48 and 0.16 sum to one.
  2. AA contributes 0.36 × 1 = 0.36.
  3. Aa contributes 0.48 × 0.9 = 0.432.
  4. aa contributes 0.16 × 0.6 = 0.096.
  5. The mean component is 0.36 + 0.432 + 0.096 = 0.888.

The mean is the explicit contribution sum for the entered genotype frequencies and viability component.

Quick guide

How to use this calculator

  1. Enter mutually exclusive type frequencies summing to one or observed pre-selection counts.
  2. Use one common fitness component and interval across all types.
  3. Inspect the contribution ledger; a large fitness value with a small starting share may contribute less than a common type.

Calculation method

Calculation and interpretation

Make the normalization denominator used by deterministic selection arithmetic explicit without assuming equilibrium or independent observations.

Mean population fitness component W̄ = Σ fiWi; normalized post-selection share f′i = fiWi/W̄ when W̄ > 0

Worked example

Weight three genotype viability components

The mean is the explicit contribution sum for the entered genotype frequencies and viability component.

Mean population fitness component W̄ = Σ fiWi; normalized post-selection share f′i = fiWi/W̄ when W̄ > 0

Supported inputs

Precision and limits

Mutually exclusive weights

Rows must partition the entered population for the declared calculation; frequency rows must sum to one.

Comparable component only

Fitness values must share the same interval, stage, denominator and unit.

No equilibrium assumption

Genotype labels do not trigger Hardy–Weinberg proportions, dominance rules or allele inference.

No uncertainty or causation

Sampling variance, repeated records, environmental effects and causal selection inference remain external.

Numerical support

Use 1–30 rows, nonnegative frequencies or safe whole counts, and fitness components from zero through 10¹². Positive weighted contributions and normalized shares must remain representable; otherwise an explicit range message asks for equivalent rescaling.

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