Part 1: Darwin, Wallace & Natural Selection

Foundations of Evolutionary Theory

The idea that species change over time did not spring fully formed from a single mind. Before Charles Darwin published On the Origin of Species in 1859, several thinkers laid crucial groundwork — from ancient Greek philosophers who speculated about change in nature, to 18th-century naturalists who catalogued the living world. Understanding this intellectual arc is essential because it reveals how evidence gradually replaced speculation, transforming biology from a descriptive catalogue into a predictive science.

Pre-Darwin Ideas

Long before Darwin, several thinkers wrestled with the question of how life changes. Their ideas — though frequently wrong in mechanism — were essential stepping stones.

Darwin & Wallace

Charles Darwin (1809–1882) and Alfred Russel Wallace (1823–1913) independently arrived at the theory of evolution by natural selection — one of the most remarkable examples of simultaneous discovery in the history of science.

Darwin spent over 20 years collecting evidence and refining his theory after returning from the Beagle voyage in 1836. He was famously cautious, worried about the social and religious implications of his ideas. Meanwhile, Wallace — a self-taught naturalist working in the Malay Archipelago — independently conceived the same mechanism while suffering from malaria-induced fever in 1858. He sent his manuscript to Darwin, precipitating one of science's most famous priority crises.

Despite the potential for rivalry, Darwin and Wallace maintained a respectful relationship. Wallace later referred to natural selection as "Darwinism" and spent decades defending the theory. Their shared discovery underscores a key principle: when evidence is ripe, ideas tend to converge independently.

The HMS Beagle Voyage

Darwin's five-year voyage aboard HMS Beagle (1831–1836) was the formative experience of his scientific life. As the ship's gentleman naturalist, the 22-year-old Darwin observed geology, fossils, and living organisms across South America, the Galápagos Islands, Australia, and beyond.

Key observations from the voyage included:

• Fossil glyptodonts in South America resembled living armadillos — suggesting descent with modification rather than separate creation
• Galápagos mockingbirds and tortoises varied between islands, each adapted to local conditions
• Geological formations confirmed Lyell's uniformitarianism — the Andes were still rising, coral atolls were sinking platforms
• Biogeographic patterns — similar environments on different continents housed different species, falsifying the idea that God placed each species in its ideal habitat

Core Principles of Natural Selection

Natural selection is remarkably simple in concept yet staggeringly powerful in consequence. It requires only four conditions, and wherever all four are met — whether in bacteria, beetles, or birds — evolution inevitably follows. Darwin called it "descent with modification," and it remains biology's most unifying principle.

Variation Within Populations

No two individuals in a population are genetically identical (except identical twins, and even they accumulate somatic mutations). This variation arises from mutation (random changes in DNA), recombination (shuffling during sexual reproduction), and gene flow (immigration of alleles from other populations).

Variation can be continuous (height, weight — controlled by many genes) or discrete (blood type, flower colour — single-gene effects). Both types provide raw material for selection.

import numpy as np
import matplotlib.pyplot as plt

# Simulate continuous variation in beak depth (mm) across 500 finches
np.random.seed(42)
beak_depths = np.random.normal(loc=10.5, scale=1.8, size=500)

plt.figure(figsize=(8, 4))
plt.hist(beak_depths, bins=30, color='#3B9797', edgecolor='white', alpha=0.85)
plt.axvline(np.mean(beak_depths), color='#BF092F', linestyle='--', linewidth=2, label=f'Mean = {np.mean(beak_depths):.1f} mm')
plt.xlabel('Beak Depth (mm)')
plt.ylabel('Number of Finches')
plt.title('Continuous Variation in Galápagos Finch Beak Depth')
plt.legend()
plt.tight_layout()
plt.show()
print(f"Range: {beak_depths.min():.1f} – {beak_depths.max():.1f} mm")
print(f"Standard deviation: {np.std(beak_depths):.2f} mm")

Heritability

Variation matters only if it can be inherited. Heritability (denoted h²) measures the proportion of phenotypic variation in a population that is attributable to genetic differences. If h² = 0, traits are entirely environmentally determined; if h² = 1, they are entirely genetic.

Most traits of evolutionary interest have intermediate heritability. For instance, beak depth in Darwin's finches has an estimated h² ≈ 0.65 — meaning about 65% of the variation in beak size across a population is due to genetic differences, with the remaining 35% attributable to environmental factors like nutrition.

Differential Survival & Reproduction

The engine of natural selection is differential fitness — some individuals survive and reproduce more successfully than others because of their heritable traits. This is not random death; it is non-random elimination of less-fit variants.

Adaptation

An adaptation is any heritable trait that increases an organism's fitness in its environment. Adaptations accumulate over many generations as natural selection favours beneficial variants and eliminates harmful ones.

Not every trait is an adaptation. Some features arise as by-products of selection on other traits (called spandrels, a term borrowed from architecture by Stephen Jay Gould). Others may be relics of ancestral environments that no longer apply (vestigial structures, such as the human appendix or whale pelvic bones).

Types of Selection

Natural selection is not one-size-fits-all. Depending on which phenotypes are favoured, selection can push a population's trait distribution in different directions. Understanding these modes is critical for predicting how populations respond to environmental change.

Directional Selection

Directional selection favours individuals at one extreme of the trait distribution. Over time, the population mean shifts toward that extreme. This is the most intuitive form of selection — bigger beaks, faster legs, thicker shells.

import numpy as np
import matplotlib.pyplot as plt

# Directional selection: shifting mean beak depth over generations
np.random.seed(42)
generations = ['Before Drought', 'After Drought']
means = [10.5, 11.0]
stds = [1.8, 1.6]

fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True)
for i, (gen, mu, sd) in enumerate(zip(generations, means, stds)):
    data = np.random.normal(mu, sd, 500)
    axes[i].hist(data, bins=25, color='#3B9797' if i == 0 else '#BF092F',
                 edgecolor='white', alpha=0.8)
    axes[i].axvline(mu, color='#132440', linestyle='--', linewidth=2)
    axes[i].set_title(gen, fontsize=13, fontweight='bold')
    axes[i].set_xlabel('Beak Depth (mm)')
axes[0].set_ylabel('Count')
plt.suptitle('Directional Selection Shifts the Mean', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.show()

Stabilizing Selection

Stabilizing selection favours intermediate phenotypes and acts against extremes. This narrows the distribution of traits and is the most common type of selection in stable environments.

Disruptive Selection

Disruptive selection (also called diversifying selection) favours individuals at both extremes of the distribution and selects against intermediate phenotypes. This can split a population into two distinct groups and is thought to be a precursor to sympatric speciation.

A well-studied example occurs in African seed-cracking finches (Pyrenestes ostrinus), where beaks come in two distinct sizes — large and small — with few intermediates. Large-beaked birds specialise on hard seeds; small-beaked birds on soft seeds. Intermediate beaks are poor at cracking either type, so they have lower fitness.

Sexual Selection

Sexual selection is a special form of natural selection where traits are favoured because they increase mating success rather than survival. Darwin proposed this in 1871 to explain extravagant features like the peacock's tail, which clearly hinders survival but dramatically increases reproductive success.

Sexual selection operates through two mechanisms:

• Intersexual selection (mate choice): One sex (usually females) chooses mates based on display traits — bright plumage, elaborate songs, courtship dances. The "choosy" sex drives the evolution of ornamental traits in the other.
• Intrasexual selection (same-sex competition): Members of one sex (usually males) compete directly for access to mates — through fighting, territory defence, or sperm competition. This drives the evolution of weapons (antlers, tusks) and large body size.

Kin Selection

Kin selection explains the evolution of altruistic behaviour — actions that reduce the actor's fitness but increase the fitness of genetic relatives. At first glance, altruism seems to contradict natural selection (why sacrifice for others?), but kin selection resolves the paradox by showing that helping relatives propagates shared genes.

The most dramatic example is the sterile worker castes of eusocial insects. In honey bee colonies, workers (all female) sacrifice their own reproduction entirely, devoting their lives to helping the queen reproduce. Because of the unusual haplodiploid genetics of hymenopterans, workers share 75% of their genes with sisters (vs. 50% with their own offspring), making it genetically "cheaper" to raise sisters than daughters.

Modern Extensions

While Darwin's core insight remains intact, 20th- and 21st-century biologists have extended the theory in important ways — broadening "fitness" beyond the individual, recognising that organisms face trade-offs between competing life demands, and developing frameworks that predict optimal life strategies.

Inclusive Fitness

Inclusive fitness extends the concept of individual fitness to include the reproductive success of genetic relatives, weighted by relatedness. An organism's inclusive fitness = its direct fitness (own offspring) + its indirect fitness (extra offspring produced by relatives because of the organism's help).

This framework, developed by W.D. Hamilton, resolved the "problem of altruism" that puzzled Darwin himself. It explains why ground squirrels give alarm calls (alerting relatives to predators despite drawing attention to themselves), why worker bees sacrifice reproduction, and why human parents invest so heavily in their children.

import numpy as np
import matplotlib.pyplot as plt

# Hamilton's Rule: altruism is favoured when rB > C
# Demonstrate with varying relatedness
relatedness = np.array([1.0, 0.5, 0.25, 0.125, 0.0625])
labels = ['Identical twin\n(r=1.0)', 'Sibling\n(r=0.5)',
          'Half-sibling\n(r=0.25)', 'First cousin\n(r=0.125)',
          'Second cousin\n(r=0.0625)']
cost = 1  # fixed cost to the altruist
min_benefit_needed = cost / relatedness  # B > C/r

plt.figure(figsize=(9, 5))
bars = plt.bar(labels, min_benefit_needed, color=['#132440', '#16476A', '#3B9797', '#BF092F', '#999999'],
               edgecolor='white', linewidth=1.5)
plt.ylabel('Minimum Benefit (B) Needed for Altruism (C = 1)')
plt.title("Hamilton's Rule: How Related Must Recipients Be?")
for bar, val in zip(bars, min_benefit_needed):
    plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.3,
             f'B > {val:.1f}', ha='center', fontweight='bold', fontsize=10)
plt.tight_layout()
plt.show()

Life-History Theory

Life-history theory studies how organisms allocate limited energy and time among competing demands — growth, reproduction, maintenance, and survival. Every organism faces a fundamental set of trade-offs: energy spent on reproduction cannot simultaneously be invested in growth or immune function.

Evolutionary Trade-Offs

No organism can maximise every trait simultaneously. Trade-offs are constraints imposed by limited resources, physics, or genetics. Understanding trade-offs is critical because they explain why organisms are not "perfectly" adapted — perfection in one trait comes at the cost of another.

Exercises & Review

import numpy as np
import matplotlib.pyplot as plt

# Original population
np.random.seed(42)
beaks = np.random.normal(loc=10.5, scale=1.8, size=1000)

# Apply stabilizing selection: keep only individuals within 1.5 SD of mean
mean_beak = np.mean(beaks)
sd_beak = np.std(beaks)
survivors = beaks[np.abs(beaks - mean_beak) <= 1.5 * sd_beak]

fig, axes = plt.subplots(1, 2, figsize=(10, 4), sharey=True)
axes[0].hist(beaks, bins=30, color='#3B9797', edgecolor='white')
axes[0].set_title(f'Before Selection (n={len(beaks)})')
axes[0].set_xlabel('Beak Depth (mm)')
axes[1].hist(survivors, bins=30, color='#BF092F', edgecolor='white')
axes[1].set_title(f'After Stabilizing Selection (n={len(survivors)})')
axes[1].set_xlabel('Beak Depth (mm)')
axes[0].set_ylabel('Count')
plt.suptitle('Stabilizing Selection Narrows the Distribution')
plt.tight_layout()
plt.show()
print(f"Original SD: {np.std(beaks):.2f}, Survivor SD: {np.std(survivors):.2f}")

Downloadable Worksheet

Conclusion & Next Steps

Charles Darwin and Alfred Russel Wallace gave science its most powerful organising principle: evolution by natural selection. From pre-Darwinian speculation through the Beagle voyage and beyond, we have traced how the simple logic of variation, heritability, and differential reproduction generates the stunning diversity of life on Earth.

We explored how directional, stabilizing, and disruptive selection reshape trait distributions, how sexual selection drives extravagant ornaments, and how kin selection explains apparent altruism. Modern extensions — inclusive fitness, life-history theory, and evolutionary trade-offs — complete the picture, revealing that organisms are not "perfectly" adapted but rather optimally compromised by the constraints they face.