Gause, Luckinbill, Veilleux, and What to Do. Christopher X Jon Jensen Stony Brook University
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1 Gause, Luckinbill, Veilleux, and What to Do Christopher X Jon Jensen Stony Brook University
2 Alternative Models of Predation: Functional Responses: f (N) Prey Dependent f (N/P) Ratio Dependent
3 Possible Outcomes of a Predator-Prey System: 1) Complete consumption of prey followed by starvation of the predator ( Dual Extinction ). 2) Oscillatory or non-oscillatory coexistence of predator and prey ( Coexistence ) 3) Starvation of the predator followed by escape of the prey ( Predator Extinction ).
4 The Paramecium-Didinium system: Paramecium caudatum Didinium nasutum Meets major assumptions of simple predator-prey models: Closed system Can be maintained without heterogeneities/refugia Single prey/single obligate predator Prey food can be delivered as semi-continuous input
5 Gause 1934: Expected: Lotka-Volterra theory predicted that all predator-prey systems should cycle indefinitely Actual: Gause found that he could not prevent his predators from completely consuming the prey population
6 Luckinbill 1973: Coexistence can be produced under two conditions: 1. Reduced interaction between predator and prey 2. Reduced prey food availability
7 Veilleux 1979: Parameter Changes High r Low r high a low a high e low e Food Concentration Number of Runs System Outcome Dual Extinction Predator Extinction 0.68 to Coexistence 0.18 to Predator Extinction Dual Extinction (6x) and Predator Extinction (3x) Qualitative outcome can be changed through modifications of prey food concentration
8 What about fitting? Prey-Dependence: better approximates Luckinbill 1973 data Ratio-Dependence: better approximates Veilleux 1979 data Despite their structural difference, the two models can produce very similar temporal dynamics both models fit very well to the data created by the other model. A good fit alone is therefore a poor indicator whether the used model correctly describes the processes that generated the data. (Jost 1998, Jost and Ellner 2000, Jost and Arditi 2001)
9 What about fitting? Given that in real life there would be substantial variation around the respective functions, because of stochastic environmental effects, population censusing errors, and variation in parameters among different ecosystems, we submit that the subtle difference in predictions of the prey- versus ratio-dependent models is minor and would not be detectable using simple regression tests (Lundberg and Fryxell 1995)
10 Possible Outcomes of a Predator-Prey System: 1) Dual Extinction 2) Coexistence 3) Predator Extinction
11 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) K (carrying capacity) r (prey growth rate) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx no change no change* CoEx DE
12 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) K (carrying capacity) r (prey growth rate) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx no change no change* CoEx DE
13 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) K (carrying capacity) r (prey growth rate) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx no change no change* CoEx DE
14 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) K (carrying capacity) r (prey growth rate) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx no change no change* CoEx DE
15 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) K (carrying capacity) r (prey growth rate) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx no change DE CoEx* CoEx DE
16 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) K (carrying capacity) r (prey growth rate) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx no change DE CoEx* CoEx DE
17 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) K (carrying capacity) r (prey growth rate) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx no change DE CoEx* CoEx DE
18 Proportional removal of prey will decrease r & K without changing other parameters: dn/dt = r0n (1 N/K) pn rp = (r0 p) Kp = K(1 p/r0)
19 Comparison of prey- vs. ratio dependent outcomes: Change in Parameter a (searching efficiency) Increasing levels of enforced proportional mortality (p) d (pred. death rate) e (conversion eff.) h (handling time) Prey-Dependent Outcome Ratio-Dependent Outcome DE PE; or DE CoEx CoEx DE
20 Experiment: PoE in reverse HP = increasing p (i.e. reducing K) in a stable system should maintain stability. [prey-dependent prediction] HR = increasing p (i.e. reducing r) in a stable system should destabilize the system. [ratio-dependent prediction]
21 Experiment: PoE forward HR = decreasing p (i.e. increasing r) in a stable system should maintain stability. [ratio-dependent prediction] HP = decreasing p (i.e. increasing K) in a stable system should destabilize the system. [prey-dependent prediction]
22 Acknowledgements: All of the thoughts presented are the result of extensive collaboration with Lev Ginzburg I am fortunate to be supported by a National Science Foundation Graduate Research Fellowship and the L.B. Slobodkin Endowment Fund for Graduate Research. science@czieje.com
23 Experiment: PoE in reverse Using manipulations of methyl cellulose and prey nutrient input, find conditions under which long-term coexistence occurs Impose proportional mortality of prey in as continuous a manner as possible Continue to increase proportional mortality until dual extinction can be achieved If dual extinction occurs, use a prey-only system with the same conditions to determine if the proportional mortality level at which dual extinction occurs corresponds to r < 0 or r >0
24 Experiment: PoE forward Begin with a system where high proportional mortality is being enforced Using manipulations of methyl cellulose and prey nutrient input, find conditions under which long-term coexistence occurs Increase r by incrementally releasing the system from enforced proportional mortality Continue to decrease proportional mortality until dual extinction can be achieved Note the problem here with having to prove the absence of a phenomenon
25 Luckinbill 1973: Dual Extinction
26 Luckinbill 1973: Predator Extinction
27 Luckinbill 1973: Coexistence
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