Predation and Its Effects
~9 min read · Tier toggle changes depth (T1 plain · T2 standard · T3 rigour)
You should be familiar with basic population growth models (exponential and logistic) and the concept of carrying capacity from previous units. Understanding how energy flows through trophic levels is also helpful.
Why this matters
Predation is a fundamental ecological interaction that shapes community structure and drives evolutionary change.
Unlike competition, where both parties lose, predation involves a clear winner and loser: the predator gains energy, while the prey loses life. This asymmetry creates powerful selective pressures that influence the behaviour, morphology, and physiology of both species. Predators regulate prey populations, preventing them from exceeding the environment's carrying capacity, while prey abundance determines the reproductive success of predators. This feedback loop creates dynamic cycles in nature, from the classic lynx-hare system to the complex interactions in marine food webs. Understanding these dynamics is crucial for conservation biology, pest control, and managing fisheries, as removing or adding a predator can have cascading effects throughout the entire ecosystem.
The Basic Interaction in Plain Language
Imagine a forest with a population of rabbits and a population of foxes. The rabbits eat grass, and their numbers grow rapidly if food is plentiful. However, as the rabbit population increases, there are more rabbits for the foxes to catch. This leads to an increase in the fox population because they have more food to support reproduction. As the fox population grows, they consume more rabbits, causing the rabbit population to decline. With fewer rabbits available, the foxes struggle to find enough food, leading to a decline in the fox population. As the fox numbers drop, the pressure on the rabbits decreases, allowing the rabbit population to recover. This cycle repeats, creating oscillations in the abundance of both species. The key insight is that the predator population lags behind the prey population. The predator peaks occur after the prey peaks, and the predator troughs occur after the prey troughs. This time lag is a hallmark of predator-prey dynamics and distinguishes them from other ecological interactions. The strength of this interaction depends on how efficiently the predator can catch the prey and how well the prey can avoid being caught. If the predator is too efficient, it may drive the prey to local extinction, which would eventually lead to the predator's own extinction. If the predator is too inefficient, it may not be able to sustain its population. In nature, this balance is maintained by a variety of factors, including the presence of alternative food sources, habitat complexity, and the physiological limits of both species. This simple model provides a foundation for understanding more complex ecological systems, where multiple predators and prey species interact simultaneously.
Functional and Numerical Responses
Predators respond to changes in prey density in two distinct ways: functionally and numerically. The functional response describes how the number of prey individuals consumed by a single predator changes as prey density varies. There are three main types of functional responses. Type I is linear, where consumption increases proportionally with prey density, typical of filter feeders. Type II is hyperbolic, where consumption increases at a decreasing rate until it reaches a maximum, common in many vertebrate predators due to handling time. Type III is sigmoidal, where consumption is low at low prey densities, increases rapidly at intermediate densities, and levels off at high densities, often due to predator learning or switching behaviour. The numerical response describes how the predator population size changes in response to prey density. This can occur through increased reproduction rates, increased survival, or immigration of predators into the area. The combined effect of functional and numerical responses determines the overall impact of predation on the prey population. For example, if a predator has a Type II functional response, it may be unable to control a rapidly growing prey population at high densities, leading to potential outbreaks. Conversely, a Type III response can provide stabilising effects, as predators switch to more abundant prey species, preventing any single prey species from dominating. Understanding these responses is crucial for predicting how ecosystems will respond to changes in species abundance, such as those caused by climate change or habitat loss.
Mathematical Description of Dynamics
The Lotka–Volterra model provides a simple mathematical framework for describing predator-prey dynamics. The model consists of two coupled differential equations. The first equation describes the change in prey population size \( N \) over time \( t \). It assumes that prey grow exponentially in the absence of predators, with intrinsic growth rate \( r \), and that predation reduces prey growth by a term proportional to the encounter rate between predators and prey, \( aNP \). The second equation describes the change in predator population size \( P \) over time. It assumes that predator growth is proportional to the number of prey consumed, \( baNP \), minus predator mortality, \( mP \). The parameter \( b \) represents the efficiency of converting consumed prey into new predators. These equations predict that prey and predator populations will oscillate around equilibrium points. The equilibrium prey density is \( m/(ba) \), set entirely by predator parameters, while the equilibrium predator density is \( r/a \). These oscillations are neutrally stable in the basic model, meaning that the amplitude of the cycles depends on initial conditions. In reality, additional factors such as density-dependent prey growth and predator self-limitation introduce damping or amplification of these cycles. The model highlights the importance of the interaction terms \( aNP \) and \( baNP \), which link the fate of the two species. Changes in any of the parameters \( r, a, b, \) or \( m \) can shift the equilibrium points or the stability of the system. For instance, an increase in the attack rate \( a \) lowers both equilibria: fewer prey are needed to sustain each predator, and fewer predators are needed to hold prey growth in check. This mathematical insight helps ecologists understand how perturbations to the system, such as the introduction of an invasive predator, might affect the entire community.
Evolutionary Consequences of Predation
Predation exerts strong selective pressures on both prey and predators, driving coevolutionary arms races. Prey species evolve defences to reduce their risk of being eaten, such as camouflage, warning colouration, toxins, spines, or behavioural strategies like flocking or fleeing. These defences are costly to produce or maintain, so they are only favoured when predation pressure is high. For example, the monarch butterfly has evolved sequestration of toxic cardenolides from its larval host plant, making it unpalatable to birds. In response, predators evolve counter-adaptations to overcome these defences. Birds that eat monarchs may evolve tolerance to cardenolides or learn to distinguish between palatable and unpalatable species. This coevolutionary dynamic can lead to increased diversity within both predator and prey communities, as different species specialise on different prey types or defences. Predation also influences the spatial distribution of species. Prey may avoid areas with high predator density, leading to patchy distributions that affect other ecological processes such as pollination and seed dispersal. Furthermore, predation can drive the evolution of life-history traits. Prey species that face high predation pressure may evolve to reproduce earlier or more frequently, a strategy known as r-selection, to maximise their chances of passing on their genes before being eaten. Conversely, predators may evolve to be more efficient hunters or to have longer lifespans, allowing them to exploit prey more effectively over time. These evolutionary responses are not static; they are ongoing processes that shape the biodiversity and complexity of ecosystems.
The concept
Predator-prey dynamics
Coupled cycles of predator and prey abundance.
A common misconception is that predators always keep prey populations below their carrying capacity. In reality, if the predator's functional response is Type II and the prey's intrinsic growth rate is high, the predator may be unable to suppress the prey population effectively, leading to prey outbreaks. Additionally, the Lotka–Volterra model assumes that predators have no alternative food sources and that prey have no refuges. In natural systems, these assumptions are often violated, leading to more complex dynamics that cannot be captured by the simple model. For example, if prey can hide in vegetation, the effective encounter rate \( a \) is reduced, stabilising the system. Conversely, if predators can switch to alternative prey, the numerical response is buffered, preventing extreme oscillations. Ignoring these complexities can lead to incorrect predictions about ecosystem stability and resilience. C
Worked example
Problem. Consider a predator-prey system with the following parameters: prey intrinsic growth rate \( r = 0.5\ \mathrm{day^{-1}} \), predator attack rate \( a = 0.02\ \mathrm{day^{-1}\,prey^{-1}} \), conversion efficiency \( b = 0.4\ \mathrm{predator^{-1}\,prey^{-1}} \), and predator mortality rate \( m = 0.2\ \mathrm{day^{-1}} \). Calculate the equilibrium densities of the prey and predator populations.
- Identify the equilibrium condition for the predator population. At equilibrium, \( \frac{dP}{dt} = 0 \), so \( baNP - mP = 0 \). Assuming \( P \neq 0 \), we can divide by \( P \) to get \( baN - m = 0 \).
- Solve for the equilibrium prey density \( N^* \). Rearranging the equation gives \( N^* = \frac{m}{ba} \). Substitute the given values: \( N^* = \frac{0.2}{0.4 \times 0.02} \).
- Calculate the numerical value of \( N^* \). \( N^* = \frac{0.2}{0.008} = 25\ \mathrm{prey\,individuals} \). This is the prey density at which predator births balance predator deaths.
- Identify the equilibrium condition for the prey population. At equilibrium, \( \frac{dN}{dt} = 0 \), so \( rN - aNP = 0 \). Assuming \( N \neq 0 \), we can divide by \( N \) to get \( r - aP = 0 \).
- Solve for the equilibrium predator density \( P^* \). Rearranging the equation gives \( P^* = \frac{r}{a} \). Substitute the given values: \( P^* = \frac{0.5}{0.02} \). Calculate the numerical value: \( P^* = 25\ \mathrm{predator\,individuals} \). This is the predator density at which prey growth balances predation loss.
Units check. The units of \( m/ba \) are \( \mathrm{day^{-1}} / (\mathrm{day^{-1}\,prey^{-1}} \times \mathrm{predator^{-1}\,prey^{-1}}) \), which simplifies to prey individuals, and the units of \( r/a \) are \( \mathrm{day^{-1}} / \mathrm{day^{-1}\,prey^{-1}} \), which simplifies to predator individuals, confirming dimensional consistency.
Three quick tests
- What is the difference between a Type I and a Type II functional response?
Answer
A Type I response is linear, with consumption increasing proportionally with prey density, while a Type II response is hyperbolic, with consumption increasing at a decreasing rate until it reaches a maximum due to handling time. - In the Lotka–Volterra model, what determines the equilibrium prey density?
Answer
The equilibrium prey density is determined by the ratio of predator mortality rate to the product of conversion efficiency and attack rate, \( N^* = m/ba \), and is independent of the prey's intrinsic growth rate. - Why might a Type III functional response be stabilising for a predator-prey system?
Answer
A Type III response is stabilising because predators switch to more abundant prey species at low densities, reducing predation pressure on rare prey and preventing their extinction, while increasing predation on abundant prey to prevent outbreaks.
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Lecture notes checked line by line by the editor; every number in the worked example was recomputed.