Predation (Chapter 18)

1. Predation (Chapter 18) • Predator-prey cycles • Models of predation • Functional vs. numeric responses • Stability in predator-prey models

2. Two big themes: • Predators can limit prey populations. This keeps populations below K.

3. Predator and prey populations increase and decrease in regular cycles.

6. A verbal model of predator-prey cycles: • Predators eat prey and reduce their numbers • Predators go hungry and decline in number • With fewer predators, prey survive better and increase • Increasing prey populations allow predators to increase And repeat…

7. Why don’t predators increase at the same time as the prey?

8. The Lotka-Volterra Model: Assumptions • Prey grow exponentially in the absence of predators. • Predation is directly proportional to the product of prey and predator abundances (random encounters). • Predator populations grow based on the number of prey. Death rates are independent of prey abundance.

9. R = prey population size (“resource”) P = predator population size r = exponential growth rate of the prey c = capture efficiency of the predators

10. removal of prey by predators rate of change in the prey population intrinsic growth rate of the prey

11. For the predators: a = efficiency with which prey are converted into predators d = death rate of predators death rate of predators rate of change in the predator population conversion of prey into new predators

12. Prey population reaches equilibrium when dR/dt = 0 • equilibrium – state of balance between opposing forces • populations at equilibrium do not change • Prey population stabilizes based on the size of the predator population

13. Predator population reaches equilibriumwhen dP/dt = 0 • Predator population stabilizes based on the size of the prey population

14. Isocline – a line along which populations will not change over time. • Predator numbers will stay constant if R = d/ac • Prey numbers will stay constant if P = r/c.

15. Predators are stable when: Prey are stable when: Number of Predators (P) Number of prey (R)

16. Prey are stable when: Prey Isocline Number of Predators (P) r/c d/ac Number of prey (R)

17. Predators are stable when: Predator isocline Number of Predators (P) d/ac Number of prey (R)

18. equilibrium Number of Predators (P) r/c d/ac Number of prey (R)

19. Predation (Chapter 18) • Finish Lotka-Volterra model • Functional vs. numeric responses • Stability in predator-prey cycles

20. Number of predators depends on the prey population. Predator isocline Number of Predators (P) Predators decrease Predators increase d/ac Number of prey (R)

21. Number of prey depends on the predator population. Prey decrease Prey Isocline Number of Predators (P) r/c Prey increase d/ac Number of prey (R)

24. Changing the number of prey can cause 2 types of responses: Functional response – relationship between an individual predator’s food consumption and the density of prey Numeric response – change in the population of predators in response to prey availability

25. Lotka-Volterra: prey are consumed in direct proportion to their availability (cRP term) • known as Type I functional response • predators never satiate! • no limit on the growth rate of predators!

26. Type II functional response – consumption rate increases at first, but eventually predators satiate (upper limit on consumption rate)

27. Type III functional response – consumption rate is low at low prey densities, increases, and then reaches an upper limit

28. Why type III functional response? • at low densities, prey may be able to hide, but at higher densities hiding spaces fill up • predators may be more efficient at capturing more common prey • predators may switch prey species as they become more/less abundant

30. Numeric response comes from • Population growth • (though most predator populations grow slowly) • Immigration • predator populations may be attracted to prey outbreaks

33. Predator-prey cycles can be unstable • efficient predators can drive prey to extinction • if the population moves away from the equilibrium, there is no force pulling the populations back to equilibrium • eventually random oscillations will drive one or both species to extinction

35. Factors promoting stability in predator-prey relationships • Inefficient predators (prey escaping) • less efficient predators (lower c) allow more prey to survive • more living prey support more predators • Outside factors limit populations • higher d for predators • lower r for prey

36. Alternative food sources for the predator • less pressure on prey populations • Refuges from predation at low prey densities • prevents prey populations from falling too low • Rapid numeric response of predators to changes in prey population

37. Huffaker’s experiment on predator-prey coexistence • 2 mite species, predator and prey

38. Initial experiments – predators drove prey extinct then went extinct themselves • Adding barriers to dispersal allowed predators and prey to coexist.

39. Refuges from predation allow predator and prey to coexist.

40. Prey population outbreaks Population growth curve for logistic population growth Per capita population growth rate ro K Density of prey population

41. Type III functional response curve for predators Per capita death rate K Density of prey population

42. Multiple stable states are possible.

43. Below A – birth rate > death rate; population increases A

44. Point A – stable equilibrium; population increases below A and decreases above A A

45. Between A & B – predators reduce population back to A A B

46. Unstable equilibrium – equilibrium point from which a population will move to a new, different equilibrium if disturbed

47. Point B – unstable equilibrium; below B, predation reduces population to A; above B, predators are less efficient, so population grows to C B

48. Between B & C – predators are less efficient, prey increase up to C B

49. Point C – stable equilibrium B

50. Predator-prey systems can have multiple stable states • Reducing the number of predators can lead to an outbreak of prey