Module 0

Overview & Classification

A biome is a continental-scale ecological unit defined by climate rather than by geography or taxonomy. Humboldt’s 1799–1804 expeditions first noticed that similar vegetation forms recur at similar latitudes and altitudes on different continents — a case of convergent community structure. This module introduces the biome concept, the Whittaker and Holdridge classifications, and the ecological hierarchy in which biomes sit.

1. The Biome Concept

Biomes sit one level below the biosphere in the ecological hierarchy:

organism → population → community → ecosystem → biome → biosphere

Each level has emergent properties not present at lower levels. A biome is characterised by its structural vegetation (broadleaf evergreen vs. needle-leaf evergreen vs. grassland vs. bare soil), by its net primary productivity, and by its climatic envelope. A biome can span continents, with taxonomically unrelated species filling analogous functional roles — convergent evolution of community form.

2. Whittaker’s Temperature–Precipitation Diagram

Whittaker 1975 plotted mean annual temperature (MAT) against mean annual precipitation (MAP) and coloured regions by dominant biome, producing the iconic scatter-polygon diagram. The two climate variables recover the global biome map without reference to geography:

\[ B \;=\; f\bigl(\text{MAT},\ \text{MAP}\bigr) + \epsilon \]

The residual ε captures secondary drivers: seasonality of precipitation (wet/dry tropics vs. Mediterranean vs. evergreen tropics), soil type, fire regime, geomorphic history, and biogeographic accident. These account for roughly 10–20% of variance in biome identity beyond the raw MAT × MAP climate envelope.

Simulation: Whittaker Biome Diagram

Nine-biome Whittaker-style classification on the MAT–MAP plane.

Python

script.py41 lines

import numpy as np, matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon

fig, ax = plt.subplots(figsize=(12, 8), facecolor='#0a1a0d')
ax.set_facecolor('#111e14')
ax.tick_params(colors='#bae6fd')
for s in ax.spines.values(): s.set_color('#334155')

biomes = {
    'Tropical rainforest':   ([220, 400, 400, 220], [28, 28, 18, 18], '#1b5e20'),
    'Tropical seasonal':     ([130, 220, 220, 130], [28, 28, 18, 18], '#33691e'),
    'Temperate forest':      ([100, 220, 200, 80], [18, 18, 5, 5], '#2e7d32'),
    'Temperate grassland':   ([30, 100, 80, 30],   [18, 18, 0, 0], '#c49a3a'),
    'Woodland/shrubland':    ([40, 130, 130, 40],  [28, 28, 18, 18], '#a08b36'),
    'Subtropical desert':    ([0, 40, 40, 0],      [28, 28, 18, 18], '#a0522d'),
    'Temperate desert':      ([0, 30, 30, 0],      [18, 18, 0, 0], '#bc8f56'),
    'Boreal forest':         ([30, 200, 180, 30],  [5, 5, -5, -5], '#184d3a'),
    'Tundra':                ([0, 180, 180, 0],    [-5, -5, -15, -15], '#5481a3'),
}

for name, (xs, ys, col) in biomes.items():
    poly = Polygon(list(zip(xs, ys)), facecolor=col, edgecolor='#bae6fd',
                   alpha=0.55, linewidth=0.8)
    ax.add_patch(poly)
    cx, cy = np.mean(xs), np.mean(ys)
    ax.annotate(name, (cx, cy), ha='center', va='center',
                color='#fef9c3', fontsize=9, fontweight='bold')

ax.set_xlim(0, 450)
ax.set_ylim(-15, 32)
ax.set_xlabel('Mean annual precipitation (cm)', color='#bae6fd')
ax.set_ylabel('Mean annual temperature (C)', color='#bae6fd')
ax.set_title('Whittaker 1975 biome diagram',
             color='#fde68a', fontweight='bold')
ax.grid(True, color='#334155', alpha=0.3)
plt.tight_layout()
plt.savefig('output.png', dpi=120, bbox_inches='tight', facecolor='#0a1a0d')
print('Whittaker 1975: two climate axes predict biome identity worldwide')
print('Secondary drivers: seasonality, soils, fire regime')

Click Run to execute the Python code

Code will be executed with Python 3 on the server

3. Holdridge Life Zones

Holdridge’s 1947 triangular classification uses three variables: biotemperature (mean of monthly temperatures clipped above 0°C), annual precipitation, and the potential-evapotranspiration (PET) ratio. Thirty-eight life zones fall out, arranged in a triangular diagram with isolines of PET/P. Holdridge is more nuanced than Whittaker in arid and boreal regions because PET captures actual water stress; it is most widely used in tropical biogeography.

4. Key Functional Metrics

Metric	Units	Definition
NPP	g C m^-2 yr^-1	Net primary productivity: GPP minus autotrophic respiration.
MAT	°C	Mean annual temperature.
MAP	mm yr^-1	Mean annual precipitation.
AET	mm yr^-1	Actual evapotranspiration. Limited by the smaller of water supply and PET.
LAI	m² m^-2	Leaf area index: total one-sided leaf area per unit ground area.
Aridity	dimensionless	P / PET. <0.2 hyperarid; 0.2–0.5 arid; 0.5–0.65 semi-arid; >1 humid.

5. Convergent Community Structure

Mediterranean biomes in California, Chile, South Africa, southwest Australia, and the Mediterranean Basin share sclerophyllous evergreen shrub communities despite containing essentially no shared plant species at the genus level. The five independent “Mediterranean-type” biomes are a textbook test of the climate-drives-community-structure hypothesis: same climate, different evolutionary history, nearly identical vegetation.

Similar convergence appears in desert succulent floras (New World Cactaceae, African Euphorbiaceae, Asian Aizoaceae), in wet-meadow sedge communities across the Arctic, and in the graminoid structure of all major tropical savannas. Taxa differ; form follows climate.

Key References

• Whittaker, R. H. (1975). Communities and Ecosystems, 2nd ed. Macmillan.

• Holdridge, L. R. (1947). “Determination of world plant formations from simple climatic data.” Science, 105, 367–368.

• Olson, D. M. et al. (2001). “Terrestrial ecoregions of the world: a new map of life on Earth.” BioScience, 51, 933–938.

• Chapin, F. S., Matson, P. A. & Vitousek, P. M. (2011). Principles of Terrestrial Ecosystem Ecology, 2nd ed. Springer.

• Humboldt, A. von & Bonpland, A. (1807). Essai sur la géographie des plantes.

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