AP Syllabus focus:
‘Evolution is supported by evidence from geographical, geological, physical, biochemical, and mathematical data.’
Evolution is not supported by a single “perfect” observation; it is strengthened by many independent disciplines that converge on the same explanation: populations change over time and share ancestry through descent with modification.
Core idea: consilience across disciplines
Multiple scientific fields test different predictions of evolutionary theory. When independent data sets (different methods, assumptions, and sources) agree, confidence increases because the same pattern is unlikely to arise by chance or bias.
Evolution (what is being supported)
Evolution: change in the genetic composition of a population across generations (often described as changes in allele frequencies over time).
Evidence does not need to directly “watch” evolution over millions of years; it can show consistent patterns, mechanisms, and historical signals that are best explained by evolution.
Geographical evidence (biogeography)
Geography shapes how populations are distributed and how related they are.
Island patterns: island species often resemble the nearest mainland species yet show distinct adaptations, consistent with colonisation followed by divergence.
Endemism: unique species in isolated regions (e.g., remote islands) aligns with limited dispersal and subsequent evolutionary change.
Continental patterns: regions with similar environments but long isolation often have different groups filling similar roles, supporting independent evolutionary histories rather than separate creation for each habitat.
Geological evidence (Earth processes and deep time)
Geology provides the context that makes large-scale evolutionary change plausible and testable.
Stratification: sedimentary layers record a sequence of environments through time; older layers represent earlier conditions.
Historical contingency: changing landforms, climates, and sea levels create barriers and connections that can separate or mix populations, matching predicted opportunities for divergence.
Consistency of timelines: multiple geological approaches (e.g., stratigraphy and radiometric methods) provide compatible ages, supporting a deep timescale for cumulative change.
Physical evidence (methods rooted in physics and chemistry)
Physical science contributes tools that validate evolutionary interpretations of history.
Radiometric principles: predictable isotope decay provides a mechanism for estimating the age of rocks and events, supporting long timescales needed for evolutionary diversification.
Radioactive decay follows a predictable exponential pattern: after each half-life, the number of parent nuclei remaining is halved. This kind of curve is the quantitative basis for radiometric dating, because measuring the fraction of parent isotope remaining lets scientists infer elapsed time under well-characterized physical laws. Source
Signal reliability: instrument-based measurements (mass spectrometry, spectroscopy) reduce reliance on subjective interpretation and allow replication across laboratories.
Rates and constraints: physics-based models of Earth and environmental change help evaluate whether proposed evolutionary histories are feasible under known physical limits.
Biochemical evidence (molecules reflect ancestry)
Biochemistry links organisms through shared molecular features that are unlikely to be independently invented in the same detailed way.
Universal or near-universal systems: the genetic code, ATP-based energetics, and core metabolic pathways indicate deep shared ancestry.
Sequence similarity: related organisms tend to share more similar DNA and protein sequences, consistent with inheritance plus accumulated change.
Conserved molecules: essential proteins (e.g., in cellular respiration) are often conserved across diverse taxa, supporting a common origin with later divergence.
Mathematical evidence (quantifying evolutionary patterns)
Mathematics provides testable models that connect mechanisms to observable data.
Population-genetic models: changes in allele frequencies can be predicted under different assumptions, allowing comparisons between observed data and expectations.
These plots show replicate populations’ allele frequencies changing across generations due to genetic drift, with much larger fluctuations in smaller populations. The figure highlights a core population-genetics result: stochastic sampling effects can drive alleles toward fixation or loss, and the expected magnitude of change depends strongly on effective population size and starting allele frequency. Source
Statistical phylogenetics: probability-based methods evaluate which evolutionary trees best explain observed sequence data, with uncertainty explicitly quantified.
Null hypotheses and inference: statistical testing helps distinguish patterns expected from evolutionary processes versus patterns likely under random sampling or measurement error.
How these lines of evidence work together
The strongest support comes from agreement across disciplines:
geographic distributions align with geological history (isolation, connections)
physical dating tools support geological sequences and deep time
biochemical similarities map onto mathematically inferred relatedness
independent methods converge on coherent evolutionary relationships and timelines
FAQ
They use independent methods, preregistered criteria where possible, and replication.
Key idea: different tools with different assumptions converging on the same pattern is harder to dismiss as bias.
Errors in one method can be shared (same assumptions, same instruments).
Agreement across disciplines reduces the chance that one shared flaw explains the whole pattern.
Many molecular features are highly constrained: small changes can disrupt function.
Shared, detailed similarities in constrained molecules are more consistent with inheritance than repeated independent origin.
They often report confidence measures (e.g., likelihood support, posterior probabilities).
These quantify how strongly the data favour one relationship/model over others.
Yes. Distribution patterns (e.g., endemism, island–mainland similarity) can be explained by colonisation and divergence.
Genetics strengthens the inference, but biogeography can be evidence on its own.
Practice Questions
State two different scientific disciplines that provide evidence supporting evolution, and for each discipline briefly describe one type of evidence it contributes. (2 marks)
1 mark: Names any valid discipline (e.g., geographical/biogeography, geological, physical, biochemical, mathematical).
1 mark: Correctly links each named discipline to a relevant evidence type (e.g., island endemism; rock layer sequence/deep time; radiometric decay measurements; DNA/protein similarity; statistical phylogenetic inference). (Award up to 2 total marks.)
Explain how evidence from biochemical data and mathematical analysis can jointly support evolution. Your answer should refer to what is measured and how conclusions are tested. (5 marks)
1 mark: Biochemical evidence involves measurable molecular features (e.g., DNA/protein sequences, conserved pathways).
1 mark: Similarities are explained by inheritance from common ancestry plus accumulated changes.
1 mark: Mathematical methods model/quantify patterns (e.g., population-genetic models or statistical phylogenetics).
1 mark: Models/test frameworks assess which evolutionary relationships best fit observed data (probability/statistics, uncertainty).
1 mark: Joint support comes from convergence: molecular measurements match mathematically inferred relatedness more than alternative explanations.
