Predator-Prey Population Dynamics
In any ecosystem, populations of species are linked through feeding relationships. Predators eat prey; prey eat plants. These links create time-delayed feedback loops that produce characteristic oscillating cycles � observed in real data from Canadian lynx and snowshoe hare populations (Hudson's Bay Company records, 1845�1935).
The classic Lotka-Volterra model captures these oscillations mathematically:
Where N = prey population, P = predator population, and a, �, d, ? are positive constants. Solutions are closed orbits in the (N, P) phase plane � populations oscillate indefinitely and out of phase: prey peak precedes predator peak.
Key Predictions of Lotka-Volterra
- Predator and prey populations oscillate with the same period, predators lagging prey by ~� cycle
- The average prey population equals ?/d regardless of initial conditions
- The average predator population equals a/� regardless of initial conditions
- Increasing prey growth rate (more food) paradoxically raises predator abundance, not prey
Carrying Capacity and Logistic Growth
Real populations cannot grow exponentially forever � environmental limits set a maximum sustainable population, the carrying capacity K. The logistic growth equation modifies the simple exponential model:
When N � K, growth is approximately exponential at rate r. As N approaches K, growth slows and stops. If N exceeds K (e.g. after an unusually productive breeding season), the population declines back toward K.
Biotic and Abiotic Factors
| Factor type | Examples | Effect on K |
|---|---|---|
| Density-dependent biotic | Food competition, predation, disease | Intensifies as N?, sets K |
| Density-independent abiotic | Temperature, drought, floods | Reduces K or causes sudden crashes |
| Climate shift | Global warming, El Ni�o | Shifts K up or down for different species |
| Human impact | Habitat loss, hunting, pollution | Reduces K, fragments populations |
Biodiversity Indices
Biodiversity is more than just a count of species. It includes the evenness � how equally distributed individuals are among species. Two well-known metrics:
Shannon Diversity Index H
Where p? is the proportion of individuals belonging to species i. H ranges from 0 (one species only) to ln(S) (all S species equally abundant). Most natural communities score H = 1.5�3.5.
Simpson Diversity Index D
D ranges from 0 (no diversity) to 1 - 1/S (maximum). It is less sensitive to rare species than Shannon H.
Species richness S and evenness E
A community where all species have equal proportions has E = 1. When one species dominates (e.g. after an invasive introduction), E drops sharply � even if total richness S is unchanged.
Trophic Levels and Food Webs
| Level | Name | Examples | Energy source |
|---|---|---|---|
| 1 | Producers | Trees, algae, grass, coral | Photosynthesis (sunlight) |
| 2 | Primary consumers | Deer, fish, rabbits, lemmings | Eat producers (herbivores) |
| 3 | Secondary consumers | Wolves, pike, foxes, sharks | Eat herbivores (carnivores) |
| 4 | Apex predators | Polar bear, eagle, orca | Eat secondary consumers |
| � | Decomposers | Bacteria, fungi, worms | Break down dead organic matter |
Only ~10% of energy transfers between trophic levels (the 10% rule), explaining why apex predator populations are always orders of magnitude smaller than primary producers. The simulator reflects this: plant populations vastly outnumber predators.
Trophic Cascades
Removing or adding a top predator can cause cascading effects through the food web. Famous examples:
- Yellowstone wolves � wolf reintroduction (1995) controlled deer, allowing riverbank vegetation to recover, changing hydrology
- Sea otters � their removal allows sea urchin populations to explode, destroying kelp forests
- Dingo removal � in Australia, fence exclusion of dingoes caused kangaroo and rodent eruptions
Try setting Human Impact high in the Grassland preset � the fox/hawk collapse releases rabbit populations, which then overgraze the grass.
Invasive Species
Invasive species cause ~40% of all animal extinctions since 1500. They succeed because:
- No evolved predators in the new environment
- High reproductive rates (r-strategy)
- Generalist diet and tolerance of disturbed habitats
- Pre-adaptation to conditions in the new range
In the simulator's Invasive preset, the introduced rat (🐀) has a reproduction threshold of just 45 energy units versus 90 for deer. This allows it to multiply twice as fast, outcompeting deer for plant resources. Shannon diversity H will typically drop from ~1.5 to <0.8 within 1000 ticks.
Management Strategies
| Strategy | Example | Effectiveness |
|---|---|---|
| Physical removal | Trapping, hunting invasive deer/rats | High if early; unsustainable alone |
| Biological control | Introducing natural predators of invasive | Risk of secondary invasions; very effective if well-matched |
| Chemical control | Herbicides for invasive plants; rodenticides | Effective short-term; collateral damage risk |
| Habitat management | Restore native vegetation to resist invasion | Long-term prevention; best combined with removal |
| Sterile insect technique | Release sterile invasive males to crash reproduction | Very effective for targeted species (e.g. screwworm) |
Curriculum Connections
| Topic | Qualification | Concepts Covered |
|---|---|---|
| Ecosystems and food webs | GCSE Biology | Trophic levels, energy transfer, 10% rule |
| Population ecology | A-Level Biology | Logistic growth, carrying capacity, interspecific competition |
| Biodiversity and conservation | A-Level Biology / Geography | Species richness, Shannon index, conservation strategies |
| Differential equations in biology | A-Level Further Maths / IB HL | Lotka-Volterra ODEs, phase plane analysis |
| Agent-based modelling | A-Level Computer Science | Individual-based rules, emergence, spatial simulation |
| Climate and human impact | GCSE/A-Level Geography | Habitat destruction, invasive species, conservation |