Predation model predictions

Predation model predictions

•

Simplest Lotka-Volterra. Linear functional

response no

K

=

neutral stability

•

L-V model with

K

=

stable equilibrium

•

No

K,

type II or III functional response = unstable

•

Hump shaped prey isocline, vertical predator

isocline = neutral stability, stable equilibrium or

prey extinction depending on intersection point

But predators may have carrying capacities determined by factors other than prey availability.

- In state space, a carrying capacity will bend the predator zero growth isocline to the right (more prey does not increase

predator numbers)

- This will generate a stable equilibrium along the horizontal part of the predator isocline

Changing the predator numerical response:

Have so far assumed the predator numerical response βV (per

capita growth rate of predators as function of prey abundance) is a linear function of prey abundance

Predator carrying capacity: predator can no longer drive prey to extinction. Stable coexistence

Outcome will depend on whether prey species cycle in concert If prey abundances are not strongly positively correlated then as one prey species becomes scarce, the predator can continue to feed and increase its population size.

Unstable equilibrium predators drive prey to extinction

Predator carrying capacity and multiple prey species:

Any factor that rotates the prey or predator isocline in a

clockwise direction (ie predator carrying capacity) will tend to stabilize interactions Predator isocline with a positive slope is stable (damped oscillations)

Conclusions: predation models

• Simple predation models generate a variety of behaviors - neutral stability, stable limit cycles, and equilibrium stability.

• Adding predator or prey carrying capacities tends to stabilize

models whereas incorporating non-linear functional responses and time lags generally leads to instability.

• Multiple prey species can destabilize populations if it allows

predators to over-exploit prey, but predation may also facilitate prey coexistence (discussion paper) - depending on predator preference and competitive interactions among prey species

Refugia and Dispersal

Predator-prey interactions are difficult to study in the lab because lab conditions do not capture the spatial extent and complexity of natural habitats:

Gause (1934) set out to look for evidence of population cycles between Paramecium (prey) and Didinium (predator) in a

microcosm experiment.

- Didinium quickly consumed all the Paramecium and then went extinct.

-When sediment was placed in the bottom of the microcosm Paramecium was able to hide, Didinium went extinct and Paramecium population recovered.

Cyclamen mite (Tarsonemus): pest of strawberries

Predatory mite (Typhlodromus): feeds on cyclamen mite and could potentially control population

Growing house experiment: Two treatments

1. Strawberry plants + predator and prey mites

2. Parathion insecticide (p) = kills predator but not prey

Huffaker (1956, 1958) Experiments with predator prey dynamics using mites

Predator! present!

Mite predator and prey dynamics mirror

observations in the field

New strawberry fields are over-run by cyclamen mites first year - then controlled second year. Prey better dispersers?? Application of parathion in fields led to cyclamen mite

outbreak.

Why do predators control prey in this system?

• High reproductive rate r of the predator

• Alternate food source for predator (can maintain high pop. density when cyclamen mite is rare.

Dispersal of prey away from predators can also help stabilize predator-prey dynamics by preventing prey extinction…

Huffaker’s (1958) oranges experiment

Six-spotted mite (prey); Typhlodromus mite (predator)

Dynamics on arrays of oranges and rubber balls

Dispersal - refuges in time?

Initial experiments:

Add 20 prey mites (parthenogenic females)

Prey population increased to 5-8,000 individuals and levelled off

Add 2 predator female mites

Predators increased - prey wiped out.

Speed of extinction depended on dispersion of habitat (oranges) because takes time for predators to arrival

Coexistence achieved by helping prey dispersal (launch pads), while hindering predators (vaseline)

Black spots= predators. Orange color=low density; red=high density of prey mites

Classical predator-prey dynamics

Gilg et al. (2003, 2006) finally explain what really happened to the lemmings…

Lemming populations cycle with a clear four-year periodicity

Cyclic dynamics of the arctic collared lemming Gilg et al. (2003, 2006)

Lemmings live in high arctic tundra (NW Greenland)

Measured lemming densities, over several years, along with food availability and predation rates (observations, skat samples, pellet samples, lemming fur in borrows)

Lemming density (indiv/ha)

F unc ti ona l re spons e L em m ings e at en/ da y Skua Fox

No evidence that food or space limits lemming pop. growth

N um eri c re spons e Y oung produc ed/ yr Skua Fox Stoat

Stoats always use lemming nests in winter. Only

predator to show a delayed response - highest

number year after lemming pop peak

N um eri c re spons e

Observed data

Model prediction Squares = lemmings Circles = nests

Conclusions:

1.

No space or food limitation

for lemmings

i.e.,

no

intrinsic prey density-dependence (resource

limitation)

2.

Only one specialist predator drives lemming

population cycles

3.

Dynamics generated by destabilizing predation by the

stoat (time lag effects)

4.

Delayed numerical response of stoat driving force for

numerical fluctuations

5.

Can use the model to examine effects of removing

one or more predators on prey population dynamics

Apparently Gilg (2006) not the last word on lemmings…

Island, where there are no stoats.

- Cycles are different at Wrangel (longer periodicity 5-8 yrs), and higher minimum lemming densities.

- Suggested that cycles may be changing across the arctic – may

represent climate change – warmer conditions lead to rainfall then icing?

• Most famous example is cycling of Canadian lynx and snoe-shoe hare (Elton and Nicholson 1942, Journal of Animal Ecol 11:215) • Hare populations cycle with peak abundance ~ every 10 yr

• Major source of hare mortality is predation

• Lynx populations appear to closely track hare populations often peaking 1-2 years after the hare peak

What about

examples where

food does become

limiting?

Classic example of Lotka-Volterra neutral stability?

Various hypotheses: Elton - variation in solar radiation resulting from periodic sunspot activity

Keith (1963) Wildlife’s ten year cycle. Univ Wisconsin Press: Starvation/disease at high population sizes or predation?

Snowshoe hare and lynx pelts collected by trappers for the Hudson Bay Co.

Lemming-style predator-prey model does not work:

- On Anticosti Island, Quebec there are no lynx but hare populations cycle anyway…

Keith (1983,1984) Oikos 40:385-395; Wildlife Monog. 90:1-43 looked at the cycle in detail and concluded:

Hare populations are co-limited by food availability and by predation from many sources (goshawks, owls, weasels, foxes, Coyotes).

Hare population growth rates are sufficient to produce

rapid depletion of food resources (principally buds and stems of

shrubs and saplings). Furthermore, induced defenses of hare forage results in additional declines in food availability.

Recognized that can only understand system with manipulation experiment

- 1 km2 _{plots. Monitoring of populations for 8 years}

- Fertilizer to increase plant growth (N,P,K)

- Electric fences to keep large predators out (permeable to hares) - Food addition treatments

- Predator exclusion = 2x hare density - Food addition = 3x hare density

- Predator exclusion x Food addition = 11 x hare density

Some unresolved questions: mechanism? Why hares low so long?

Definitive(?) study of snoeshow hare dynamics in

What’s grousing the red grouse??

Number of grouse shot (solid line) is a good measure of grouse abundance (dashed).

Number of grouse shot (solid line) is a good measure of grouse abundance (dashed).

Some years grouse are very scarce Grouse population growth rate

is strongly negative correlated with intensity of infection of nematode parasites

Nematodes affect adult survivorship

Control population

Grouse treated once with worm medicine

Grouse treated twice with worm medicine

Effect of nematocide on population cycling

Nematodes can explain population cycling, but not synchronicity of population cycling among populations

Cattadori et al. (2005) 91 grouse populations

Harvest records 1839-1994 High population synchronicity was correlated with climate: Warm dry May impede egg development followed by wet cold July inhibited nematode transmission.

Many examples:

Janzen suggested that seed predation is a major selective force favouring ‘masting’ (massive super-annual seed production)

e.g. Janzen (1976) Why bamboos wait so long to flower. Ann. Rev. Ecol. Syst. 7:347-391

Bamboos are the most dramatic mast fruiters, with many species Fruiting at 30-50 yr intervals and some much longer e.g.

Phyllostachys bambusoides fruits at 120 year intervals!

Predation escape through predator satiation

If predation rates decline at high prey densities (type II and III functional responses) then predator satiation may allow prey species to maintain their populations.

Magicicada spp emerge once every 13 or 17 yrs up to 4 million/ ha= 4 tonnes of cicadas/ha - the highest biomass of a natural population of terrestrial animals ever recorded!

Monitored Cicada populations in N. Arkansas

Estimated emergence rates by capturing insects in traps on the ground as they came out of the soil

Captured dead adults in traps and determined predation rate by birds by counting discarded wings

Williams et al (1993) Predator satiation by periodical cicadas Ecology 74:1143-1152

- Estimated 1,063,000 cicadas emerged in 16 ha - 50% of population emerged on just four nights

- Birds killed a large proportion of the first emergents when

e.g., Paine (1966) Gastropod Muricanthus eventually becomes too large for Heliaster starfish to feed on them.

Perhaps more often, small prey escape detection, or resources expended in capturing them exceed those obtained by their consumption

If there is size selection of prey by predators across a community then this may be an important determinant of community structure Example of a trade-off: the ‘size-efficiency hypothesis’

Proposed that size-dependent predation by fish determines the size structure of freshwater zooplankton

Observations:

- lakes seldom contained abundant large zooplankton (>0.5 mm) and small Zooplankton (<0.5 mm) together

- large zooplankton did not coexist with plankton feeding fish Hypotheses:

Large zooplankton assumed to be superior competitors for food (phytoplankton) because of greater filtering efficiency

Planktivorous fish thought to selectively consume large-bodied, competitively superior plankton

Brooks and Dodson (1965) Predation, body size, and

composition of plankton

Crystal lake Connecticut.

No planktivorous fish (Alosa) Large plankton