Predation is.. The eating of live organisms, regardless of their identity

Transcription

1 Predation

2 Predation

3 Predation is.. The eating of live organisms, regardless of their identity

4 Predation 1)Moves energy and nutrients through the ecosystem 2)Regulates populations 3)Weeds the unfit from a population

5 Adaptation to predation Parrotfish teeth

6 Camouflage

7 Co-evolution

8 Geerat Vermeij

9 Predation effects on community structure keystone predator hypothesis Paine, Oecologia, 15:93-120

10 Castro & Huber Marine Biology.

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12 Estes et al. Science 282:

13 Wilson & Bossert A primer of Population Biology.

14 Lynx - oscillations

15 Hare and Lynx

16 Blasius et al Nature 399:

17 Lotka-Volterra predator-prey prey model Assumptions: 1. They live in an ideal environment 2. Predators depend on a single species of prey 3. Only negative feedback on predator population is mediated by prey population size. 4. Both populations reproduce continuously, are ageless and sexless (eliminating the essentials of age-structure, mating, evolution nevertheless lets proceed)

18 Thought experiment N N is the number of Prey P P is the number of Predators

19 N (Prey) P (Predator) dn/dt = dp/dt

20 Birth 1 Birth 2 N (Prey) P (Predator) Death 1 Death 2 dn/dt = (b-d).n( dp/dt = (b-d).p(

21 Intraspecific components of Prey (N) - absence of predators (P) dn/dtdn/dt = r 1 N r 1 is the intrinsic, per capita growth rate of the prey (r = b-d,, per capita birth and death)

22 Birth 1 Birth 2 N (Prey) P (Predator) Death 1 Death 2 dn/dt = (b-d).n( r1.n dp/dt = (b-d).p( r2.p

23 In combination We We assume, number of prey eaten is proportional to number of encounters (documentaries tell us they are, in reality it is not, which is a proportionality constant (C). Lets Lets assume they occur randomly, increasing with the product of prey and predator densities (N.Predators( N.Predators)

24 Birth 1 Birth 2 Fraction of encounters resulting in consumption N (Prey) C P (Predator) Death 1 Death 2 dn/dt = (b-d).n( r1.n dp/dt = (b-d).p( r2.p

25 C.N.Predator C.N.P is the predator s functional response, their per capita rate of prey consumption as a function of prey density (linear, but unrealistic, no satiation). Anyway, prey consumption has a positive effect on predator dynamics Therefore dn/dtdn/dt = r 1.N - CNP

26 Birth 1 Birth 2 N (Prey) C P (Predator) Death 1 Death 2 dn/dt = r1.n C.N.P dp/dt = (b-d).p( r2.p

27 Predator Assume Assume predator birth is proportional to prey consumed (d 2 ) Predators with no prey have no progeny, b 2 = 0 The The per capita growth rate, r 2, of these starving predators will be r 2 = -d 2 Therefore dp/dt = -d 2.P

28 Birth 1 Birth 2 N (Prey) C P (Predator) Death 1 Death 2 dn/dt = r.n C.N.P dp/dt = -d.p

29 C.N.Predator C.N.P C.N.P is the predator s functional response, their per capita rate of prey consumption as a function of prey density Therefore dp/dt = -d.p + CNP

30 Birth 1 Birth 2 N (Prey) C P (Predator) Death 1 Death 2 dn/dt = r.n C.N.P dp/dt = -d.p + CNP

31 Birth 1 Birth 2 N (Prey) C g is rate of conversion g P (Predator) 1-g Death 1 Death 2 dn/dt = r.n C.N.P dp/dt = -d.p + CNP

32 Therefore dp/dtdp/dt = -d 2.P + gcnp Where Where g is a term that is related to the efficiency at converting prey into new predators. gcnp is the predator s numerical response, rate at which new predators are born as a function of prey density.

33 dn/dt = r1.n - CNP dp/dt = -d2.p + gcnp

34 Predators Po = 20 d2 = 0.6 g = 0.5 dn/dt = r 1.N - CNP dp/dt = -d 2.P + gcnp Prey No = 20 r1 = 0.9 C = 0.1 d 2 = Predator birth is proportional to prey consumed = Predator birth is proportional to prey consumed g = conversion rate of captured prey into new predators C = proportion of predator-prey encounters resulting in prey mortality

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36 Lotka-Volterra predator-prey prey isoclines Pre dator (P) dn / dt = 0 dp / dt = 0 equilibrium Prey (N)

37 Acanthaster planci

38 Acanthaster planci -predator predator removal theory

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40 The Acanthaster phenomenon (Moran et al., 1992)

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43 Previous records of COTS outbreaks (Illustration by Moran, 1988)

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47 Wilson & Bossert A primer of Population Biology.

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50 Life History Yamaguchi, 1978

51 Birkeland s nutrient limitation hypothesis (1982) Nutrient pulse here, enhances survival. Source: possibly rivers

52 Hypothesis Survival rate Critical range Food concentration 0.4 μg L -1 Chl.a for COTS larvae (Lucas, 1982) Chl.a in GBR waters < 0.4 μg L -1 Larval starvation hypothesis Phytoplankton only?

53 Birkeland s nutrient limitation hypothesis Experimental test by R. Olson in the 1980s showed that this was incorrect larvae survived in low nutrient environment (in rearing tanks).

54 Birkeland s nutrient limitation hypothesis However, meticulous reassessment in the 1990s by K Okaji, using the same equipment, showed that the larval rearing tanks had high nutrient loads.

55 Media release 2004 Australian Institute of Marine Sciences Laboratory experiments reveal that twice as much phytoplankton results in a ten-fold increase in larval survival. Phytoplankton levels on reefs off the developed central Great Barrier Reef are double those north of Cooktown,, where there is little human influence.

56 A A computer model developed by Dr De ath predicts that such a doubling of phytoplankton will create more frequent outbreaks, from one every years to one every 15 years; frequencies consistent with those observed in the northern and central Great Barrier Reef.

57 Other realities 1. Foraging predators may interfere with one another, so the functional response becomes a function of both predators and prey 2. Predators that eat different kinds of prey will be less tightly coupled 3. Seasonal life cycles will have time lags and finite- difference equations 4. Populations linked to the landscape by dispersal of prey and predators will influence local abundances

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59 Optimal foraging Begon et al Ecology: Individuals, Populations, & Communities.

60 Landscape

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62 Louisiana confirms four West Nile deaths August 2, 2002 Posted: 12:26 PM EDT (1626 GMT) SLIDELL, Louisiana (AP) -- The West Nile virus has infected 58 Louisiana residents, killing four of them, while spreading to virtually every corner of the state, health officials said Friday.

63 SI, host-micro micro-parasite model Host individuals Susceptible Hosts (S) (not infected) Infected Hosts (I)

64 Assumptions 1. Individuals are unaffected at birth 2. Newly infected hosts can transmit the disease immediately 3. There is no age among hosts 4. The disease does not affect fecundity 5. Random events are ignored 6. No density dependent feedback 7. Infections occur in direct proportion to number of encounters between susceptible and infected individuals (set as a product SI)

65 Susceptible Hosts (S) Infected Hosts (I) ds/dt = S di/dt = I

66 Susceptible Hosts (S) Transmission B Infected Hosts (I)

67 Susceptible Hosts (S) Transmission B Infected Hosts (I) ds/dt = -BSI di/dt = BSI Product of densities SI and probability of transmission when encountered

68 Susceptible Hosts (S) Transmission B Infected Hosts (I) d (natural morality) Alpha (disease induced Mortality) + d Death Death

69 Susceptible Hosts (S) Transmission B Infected Hosts (I) d Alpha + d Death Death ds/dt = -BSI - ds di/dt = BSI (a +d)i

70 Birth b b Susceptible Hosts (S) Transmission B Infected Hosts (I) d Alpha + d Death Death

71 Birth b b Susceptible Hosts (S) Transmission B Infected Hosts (I) d Alpha + d Death Death ds/dt = b(s +I) - BSI - ds di/dt = BSI (a +d)i

72 Recovery v (immune) Birth b b Susceptible Hosts (S) Transmission B Infected Hosts (I) d Alpha + d Death Death

73 Recovery v Birth b b Susceptible Hosts (S) Transmission B Infected Hosts (I) d alpha + d Death Death ds/dt = b(s +I) + vi - BSI - ds di/dt = BSI (a +d+ v)i