Why the Standard Definition Masks the Lack of Functional Innovation
This post argues that defining evolution as changes in allele frequencies oversimplifies the concept and ignores the need for new functional genetic information. It critiques common examples of genetic change—gain, selection, and loss—as insufficient to explain creative biological innovation, favoring a design-based interpretation instead.
1. Introduction
One of the most commonly cited definitions of evolution in modern biology is simple:
“Evolution is a change in the allele frequencies of a population over time.”
— Douglas J. Futuyma, Evolution (3rd ed., 2013), p. 5
Textbooks echo this idea widely:
“Evolution is defined as a change in the genetic composition of a population over successive generations.”
— Campbell Biology, 11th ed., Urry et al., Pearson, 2017, p. 488
At first glance, this seems harmless—perhaps even intuitive. Populations change over time, and those changes involve genetics. What’s the problem?
The problem is this: such definitions are tautological. They define evolution as “change,” and then treat all change as proof of evolution. They collapse a deep explanatory question into a mere operational description. And most importantly, they mask the actual nature of those changes—whether the change adds new biological functions (actual evolution), merely shifts existing traits (design), or degrades what already exists (design).
2. Three Categories of Genetic Change
The standard definition obscures a crucial distinction: not all allele frequency changes are created equal. We must ask: what kind of change is taking place? Genetic variation can be divided into three broad categories:
2.1 Supposed Gain-of-Function Mutations (i.e. evolution)
These would be truly evolutionary in the Darwinian sense—mutations that add new, specified functional information to the genome. If evolution is to build from bacteria to Beethoven, it must do this countless times.
However, no clear examples exist of this in observed microbial evolution. Several case studies often cited as gain-of-function collapse under scrutiny:
- Nylonase (Flavobacterium): Arises from a frameshift mutation of a pre-existing gene. Function is crude and inefficient. (Source: Negoro, S. (2000). Applied Microbiology and Biotechnology)
- Rifampin Resistance (Mycobacterium tuberculosis): Point mutation in
rpoBalters binding site for antibiotic. Results in degraded specificity and reduced fitness. (Source: Telenti et al., Lancet, 1993) - Penicillin Resistance (Staphylococcus aureus): Production of β-lactamase, usually through gene acquisition (not mutation). Mutations that do occur only increase expression. (Source: Livermore, D. M., Clinical Microbiology Reviews, 1995)
- Tetracycline Resistance (E. coli): Caused by overexpression of efflux pumps or loss of repressor function—none of which introduce new functions. (Source: Levy, S. B., Antimicrobial Agents and Chemotherapy, 1992)
- Vancomycin Resistance (Enterococcus): Resistance arises from horizontal gene transfer of the
vanAcluster. No novel mutation involved. (Source: Arthur & Courvalin, Antimicrobial Agents and Chemotherapy, 1993)
In summary, no known mutation in these examples introduces a truly novel function. All involve degradation, overexpression, or acquisition of pre-existing information—not innovation.
So let me translate this – there is currently NO CLEAR DNA EVIDENCE SUPPORTING EVOLUTION.
2.2 Selection Among Existing Variation
In some cases, allele frequencies shift due to selection pressures favoring certain pre-existing traits. For example, an organism may already carry alleles better suited to high temperatures or antibiotic exposure. The environment then selects these alleles.
This is genuine adaptation, but not evolution in the creative sense. It merely shuffles or amplifies existing options. No new functionality is introduced into the genome. It’s like picking your warmest coat on a cold day—you haven’t invented anything new, just chosen from what you already had.
2.3 Loss-of-Function Mutations
By far the most common mutations are loss-of-function mutations. These may confer short-term advantages by disabling or breaking existing systems—especially under artificial laboratory pressures.
Examples include blocking membrane channels to prevent antibiotic uptake, disrupting metabolic genes, or deactivating regulatory systems.
“The great majority of beneficial mutations are loss-of-function mutations… They adapt an organism by breaking or blunting a pre-existing system.” (Michael Behe, Darwin Devolves, 2019, p. 182)
“Most real mutations are deleterious, and only a very few beneficial mutations represent a gain of new functional information.” (John Sanford, Genetic Entropy, 2005, p. 23)
3. The Design Hypothesis Explains the Data
Design theory predicts exactly what we see:
- Genomes were originally rich in functional, specified information.
- Over time, mutations tend to degrade that information or repurpose existing parts.
- Natural selection fine-tunes what is already there—but it does not create new, integrated systems.
Far from supporting evolution, the data points toward degeneration over time—a slow erosion of original function. This explains why beneficial mutations are overwhelmingly destructive and why observed changes never add genuine complexity.
“Evolution has become in many respects a theory driven by its definitions. It is one thing to say that species change; it is another to define that change as evolution.” (— David Berlinski, The Deniable Darwin, 2009, p. 16)
4. Conclusion
Yes, allele frequencies change. But unless we ask what kind of change, we are only playing with semantics. The standard definition of evolution—“change in allele frequencies”—is an operational metric, not an explanatory mechanism.
When all change is labeled “evolution,” the term loses explanatory power. Most importantly, the types of changes we observe—selection among existing traits and loss-of-function mutations—do not support Darwin’s vision of ever-increasing complexity. They support a design-centered model of descent with degradation, not ascent with innovation.