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Multi-Trophic Level, Pairwise Species Interactions: Predator-Prey, Parasitoid-Host & Parasite-Host Relationships “Nature red in tooth & claw” Alfred Tennyson.

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Presentation on theme: "Multi-Trophic Level, Pairwise Species Interactions: Predator-Prey, Parasitoid-Host & Parasite-Host Relationships “Nature red in tooth & claw” Alfred Tennyson."— Presentation transcript:

1 Multi-Trophic Level, Pairwise Species Interactions: Predator-Prey, Parasitoid-Host & Parasite-Host Relationships “Nature red in tooth & claw” Alfred Tennyson ( )

2 Why study predation & parasitism? A basic-science answer: All organisms are subject to various sources of mortality, including starvation, disease & predation; to understand population & community structure & dynamics requires knowing something about these processes Predation & Parasitism Photo from Greg Dimijian

3 Why study predation & parasitism? A basic-science answer: All organisms are subject to various sources of mortality, including starvation, disease & predation; to understand population & community structure & dynamics requires knowing something about these processes A utilitarian answer: Understanding how much natural mortality occurs, and why, in populations is critical to managing those that we exploit (e.g., fisheries, game animals, etc.), or wish to control (e.g., weeds, disease organisms or vectors, invasive species, etc.) Photo from Greg Dimijian Predation & Parasitism

4 Modeling predation: Lotka-Volterra model Prey (victims) in the absence of predators: dV/dt = rV Losses to predators are proportional to VP (probability of random encounters) and  (capture efficiency – effect of a single predator on the per capita growth rate of the prey population) Large  is exemplified by a baleen whale eating krill, small  by a spider catching flies in its web Prey in the presence of predators: dV/dt = rV -  VP where  VP is loss to predators  V is the functional response of the predator (rate of prey capture as a function of prey abundance); in this case linear, i.e., prey capture increases at a constant rate as prey density increases Predation

5 In the model’s simplest form, the predator is specialized on 1 prey species; in the absence of prey the predator pop. declines exponentially: dP/dt = -qP P is the predator pop. size, and q is the per capita death rate Positive population growth occurs when prey are present: dP/dt = ßVP - qP ß is the conversion efficiency – the ability of predators to turn a prey item into per capita growth Large ß is exemplified by a spider catching flies in its web (or wolves preying on moose), small ß by a baleen whale eating krill ßV is the numerical response of the predator population – the per capita growth rate of the predator pop. as a function of the prey pop.

6 dV/dt > 0 dV/dt < 0 Equilibrium solution: For the prey (V) population: dV/dt = rV -  VP 0 = rV -  VP  VP = rV  P = r P = r/  The prey isocline P depends on the ratio of the growth rate of prey to the capture efficiency of the predator ^ Figure from Gotelli (2001)

7 The predator isocline V depends on the ratio of the death rate of predators to the conversion efficiency of predators Equilibrium solution: For the predator (P) population: dP/dt = ßVP - qP 0 = ßVP - qP ßVP = qP ßV = q V = q/ß dP/dt > 0dP/dt < 0 ^ Figure from Gotelli (2001)

8 Combined graphical solution in state space: The predator and prey populations cycle because they reciprocally control one another’s growth Figure from Gotelli (2001)

9 Combined graphical solution in state space: The predator and prey populations cycle because they reciprocally control one another’s growth Figure from Gotelli (2001)

10 Prey limited by both intraspecific competition and predation: dV/dt = rV -  VP - cV 2  VP  due to predator cV 2  due to conspecifics dP/dt = ßVP – qP Now the prey isocline slopes downward, as in the Lotka- Volterra competition models At this point, the prey population is self-limiting, i.e., no predators are required to keep the population from changing in size. The predator and prey populations reach a stable equilibrium What did this point represent in the competition models? Figure from Gotelli (2001)

11 Satiation Host-switching, developing a search image, etc. Why might functional responses have these shapes? Rate of prey capture Victim abundance (V) Figure from Gotelli (2001), after Holling (1959) Functional Response Curves

12 Predators with either a Type II or Type III functional response: Type II for prey: dV/dt = rV - [(kV) / (V+D)]P Type III for prey: dV/dt = rV - [(kV 2 ) / (V 2 +D 2 )]P Where k = maximum feeding rate; D = half-saturation constant, i.e., abundance of prey at which feeding rate is half-maximal The equilibrium in both cases (Type II & Type III functional responses) is unstable Predator: dP/dt = ßVP – qP Figure from Gotelli (2001)

13 An even more realistic prey isocline may be a humped curve: In this case, the position of the predator isocline with respect to the maximum in the prey isocline determines dynamics For example, imagine a combination of Allee effects, decreasing impact of predators with increases in prey numbers (e.g., Type II or III functional response), plus increasing impact of intraspecific competition Figure from Gotelli (2001)

14 An even more realistic prey isocline may be a humped curve: In this case, the position of the predator isocline with respect to the maximum in the prey isocline determines dynamics For example, imagine a combination of Allee effects, decreasing impact of predators with increases in prey numbers (e.g., Type II or III functional response), plus increasing impact of intraspecific competition Figure from Gotelli (2001)

15 An even more realistic prey isocline may be a humped curve: In this case, the position of the predator isocline with respect to the maximum in the prey isocline determines dynamics For example, imagine a combination of Allee effects, decreasing impact of predators with increases in prey numbers (e.g., Type II or III functional response), plus increasing impact of intraspecific competition Figure from Gotelli (2001)

16 Coexistence with stable limit cycles Coexistence at stable equilibrium Unstable equilibrium Figure from Gotelli (2001)

17 This idea developed out of a desire to warn against indiscriminate use of resource enrichment to bolster a population under management Figure from Gotelli (2001) Paradox of enrichment in predator-prey interactions (Rosenzweig 1971) “control” conditions enriched conditions

18 This idea developed out of a desire to warn against indiscriminate use of resource enrichment to bolster a population under management Figure from Gotelli (2001) Paradox of enrichment in predator-prey interactions (Rosenzweig 1971) “control” conditions enriched conditions

19 But is it only of theoretical interest? See: Abrams & Walters 1996; Murdoch et al. 1998, Persson et al In the real world enrichment generally fails to destabilize dynamics in this way, perhaps due to nearly ubiquitous occurrence of some invulnerable prey Figure from Gotelli (2001) Paradox of enrichment in predator-prey interactions (Rosenzweig 1971) “control” conditions enriched conditions

20 R 1 [N] R 2 [P] A B Resource supply point Consumption vectors Slope of consumption vectors for A Slope of consumption vectors for B This is one way in which competitive interactions can also result in a paradox of enrichment This idea also developed out of a desire to warn against indiscriminate use of resource enrichment to bolster a population under management Imagine what happens when we fertilize with N Paradox of enrichment in competitive interactions (Riebesell 1974; Tilman 1982, 1988)

21 Predator is a complete specialist on the focal prey Predator’s K depends on the abundance of the focal prey Predator uses multiple prey, so predator’s K is independent of the focal prey; in this case predator has low K Where would the predator isocline be if the predator uses multiple prey and deterministically drives the focal prey extinct? Figure from Gotelli (2001) Effect of changing the predator isocline (by changing the numerical response of the predator)

22 Tends to stabilize dynamics Figure from Gotelli (2001) Effect of prey refuges or immigration (rescue effect)

23 Experiment demonstrating the stabilizing influence of refuges Six-spotted mite feeds on oranges and disperses among oranges by foot or by ballooning on silk strands Predatory mite disperses by foot See Huffaker (1958)

24 In experimental arrays, predators drove prey extinct in the absence of prey refuges; predator pop. then crashed In large arrays with refuges (see fig.) predators & prey coexisted with coupled oscillations Experiment demonstrating the stabilizing influence of refuges Six-spotted mite feeds on oranges and disperses among oranges by foot or by ballooning on silk strands Predatory mite disperses by foot See Huffaker (1958)

25 So far we have assumed that responses of predators to prey (and vice versa) are instantaneous Time lags (the time required for consumed prey to be transformed into new predators, or for predators to die from starvation) add realism Incorporating time lags into models generally has a destabilizing effect, leading to larger-amplitude oscillations Harrison (1995) incorporated time lags into the numerical response of Didinium consuming Paramecium prey This greatly improved fits of models to actual population fluctuations of predator & prey described by Luckinbill (1973) Effect of time lags

26 Additional biological complexity Coexistence at stable equilibria, after damped oscillation cycles, or within stable limit cycles, or instability & lack of coexistence, depending especially on the biology of the interacting species: Functional response of predators to prey (generally destabilizing if non-linear) Carrying capacity of predators and prey in the absence of the other (often stabilizing) Refuges for the prey (often stabilizing) Specificity of the predator to the prey (destabilizing if the switch occurs at a very low prey density, but stabilizing if the switch occurs at a higher prey density) Etc…

27 Hare pops. cycle with peak abundance ~ every 10 yr; Lynx pops. track hare pops., with ~ yr time lag Canada Lynx & Snowshoe Hare exhibit synchronized oscillatory dynamics in nature (Elton & Nicholson 1942) Figure from Gotelli (2001) Lynx & hare

28 Hare populations are co-limited by food availability & predation (e.g., Keith 1983); hares rapidly deplete food quantity (principally buds & young stems of shrubs & saplings) & quality (hares stimulate induced defenses of food plants) Low food availability increases susceptibility to predation (lynx, weasels, foxes, coyotes, goshawks, owls & etc.) Simple Lotka-Volterra model is not a complete explanation; e.g., cycles are broadly synchronized, even on some Canadian islands w/o lynx Sun spot cycles and their influence on climate & food plants are also implicated (e.g., Krebs et al. 2001) At any rate, the lynx-hare cycle is more complex than suggested by the superficial resemblance to Lotka-Volterra models Lynx & hare

29 How might the evolutionary advent of phenotypic plasticity alter predator-prey dynamics? Agrawal (2001), Fig. 1 Phenotypic Plasticity & Predation

30 Plant examples: Janzen (1976) suggested that seed predation is a major selective force favoring “masting” (massive supra-annual seed production). Bamboos are the most dramatic mast fruiters, with many species fruiting at yr intervals and some much longer, e.g., Phyllostachys bambusoides fruits at 120 year intervals! Other masters: Dipterocarpaceae, oaks, beech, many conifers, and possibly the majority of tropical trees. Animal examples: Williams et al. (1983) provided evidence that Magicicada spp. emerge once every 13 or 17 yrs to avoid similarly cycling predators. These emerge at densities of up to 4 million/ha = 4 tons of cicadas/ha  the highest biomass of a natural population of terrestrial animals ever recorded. Escape through predator satiation (as may occur in Type II & III functional responses)

31 Mean-field assumption: all prey are the same (size, etc.) Small prey may escape detection, or resources expended in capturing and handling them may exceed resources obtained by their consumption (the “celery bind”) Size-dependent predation Large prey may escape consumption owing to mechanical constraints on feeding, e.g., Paine (1966) found that the gastropod Muricanthus becomes too large for Heliaster starfish to handle

32 Brooks and Dodson (1965) proposed that size-dependent predation by fish determines the size structure of freshwater zooplankton Observations: Lakes seldom contained abundant large zooplankton (>0.5 mm) & small zooplankton (<0.5 mm) together Large zooplankton were not found with plankton-feeding fish Size-dependent predation

33 Crystal Lake, Connecticut No planktivorous fish Large plankton Crystal Lake 22 yr after introduction of Alosa aestivalis (Blueback Herring) Size-dependent predation

34 Brooks and Dodson (1965) proposed that size-dependent predation by fish determines the size structure of freshwater zooplankton Observations: Lakes seldom contained abundant large zooplankton (>0.5 mm) & small zooplankton (<0.5 mm) together Large zooplankton were not found with plankton-feeding fish Hypotheses: Large zooplankton are superior competitors for food (phytoplankton) because of greater filtering efficiency Planktivorous fish selectively consume large-bodied, competitively superior plankton Size-dependent predation

35 Detailed analyses of the mechanisms of change showed that: Fish do indeed selectively remove large-bodied zooplankton But, large-bodied zooplankton do not competitively exclude small- bodied zooplankton… they eat them (intra-guild predation)! Size-dependent predation

36 In some cases brood parasitism represents “predation” and parasitism combined Davies 1992, pg. 217 Brood Parasitism

37 Microparasites – parasites that reproduce within the host, often within the host’s cells, and are generally small in size and have short lifespans relative to their hosts; hosts that recover often have an immune period after infection (sometimes for life); infections are often transient; examples include: bacterial, viral, fungal infectious agents, as well as many protozoans Macroparasites – parasites that grow, but have no direct reproduction within the host (they produce infective stages that must colonize new hosts); typically much larger and have longer generation times than microparasites; immune response in hosts is typically absent or very short- lived; infections are often chronic as hosts are continually reinfected; examples include: helminths and arthropods Parasitoids – insects whose larvae develop by feeding on a single arthropod host and invariably kill that host; e.g., Nicholson-Bailey models Conceptual models of parasitism (usually categorized by function rather than taxonomy)

38 Susceptible hosts (X) Infected hosts (Y) Immune hosts (Z) Birth Death aaa b α + bb βv  dX/dt = a(X+Y+Z) - bX - βXY +  Z dY/dt = βXY - (α+b+v)Y dZ/dt = vY - (b+  )Z Coupled differential equations, one for each type of host What is βXY?Combined encounter & infection rate See: Anderson & May (1979); May (1983) Modeling microparasite-host dynamics

39 Red grouse (U.K.) and their nematode parasites (Dobson & Hudson 1992) Grouse: dH/dt = (b-d-cH)H - (α+  )P Incorporates reduction in survival (α) & reprod. (  ) Free-living stages (eggs and larvae) of the worms: dW/dt = P -  W - βWH Adult worm population (within caecae of grouse): dP/dt= βWH - (  +d+α)P - α(P 2 /H)(k+1/k) Final term represents aggregation among hosts (smaller k  more aggregated) Parameter values estimated in the field Scotland (wetter) – model predicted observed 5-yr cycles England (drier) – model predicted observed lack of cycles (possibly owing to higher mortality of free-living stages) Modeling macroparasite-host dynamics

40 The same rich variety of dynamics observed for predators and their prey arise in various kinds of parasite-host and parasitoid-host models, including all possibilities from stable coexistence, to unstable exclusion, to cycles and chaos Parasitism

41 Standardized S of parasites in native range Standardized S of parasites in introduced range molluscs crustaceans amphibians & reptiles fish mammals birds Parasite species richness (shown below) and parasite prevalence (% infected hosts) showed similar patterns 1-to-1 line Redrawn from Torchin et al. (2003) Parasite-host interactions & invasive species

42 Evolutionary trajectories of virulence… Some key results: Horizontal vs. vertical transmission (see Ewald 1994) Horizontal transmission generally leads to greater virulence than vertical transmission Greater virulence usually results from higher transmission rates in general Degree of alignment of reprod. interests (see Herre et al. 1999) The tighter the dependence of parasite reproduction on host reproduction, the less virulent parasites tend to become Parasite-host interactions through evolutionary time Darwinian Agriculture & Medicine make good use of these observations (see R. F. Denison; G. C. Williams & R. M. Nesse)

43 Co-cladogenesis and other macro-evolutionary processes… Parasite-host interactions through evolutionary time

44 Cospeciation Host switch Duplication Missing the boat Extinction Host Failure to speciate Parasite Coexistence Which are most likely under strictly vertical transmission? From J. Weckstein (2003)

45 ?? All else being equal, will host-switches preferentially occur onto more common potential hosts?

46 All else being equal, will host-switches preferentially occur onto potential hosts that are more closely related to the current host? ? ?

47 What patterns do we expect in communities in which parasites (predators, parasitoids) have multiple potential “choices”? ? ??

48 Ghosts of Predation Past Photos from: North American Cheetah (Miracinonyx) went extinct ~11,000 yr ago; even so the Pronghorn Antelope remains the fastest land animal in N. Am. Miracinonyx was similar to extant Acinonyx jubatus


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