Population Ecology
I. Attributes
II. Distribution
III. Population Growth – changes in size through time
IV. Species Interactions
V. Dynamics of Consumer-Resource Interactions
A. Consumers can limit prey populations
1. Importance
- the production of crop plants can be limited by herbivorous pests
- we use predatory insects to control populations of herbivorous insects
- consumer populations increase when prey are at high density; increasing transmission rates of parasites and
pathogens in high density human populations
- killing or excluding predators from an area can cause their prey populations to explode
- defining whether and when populations are limited by food (bottom up) or predators (top down) becomes an
important ecological question.
2. Examples
a. Cougar and Deer: Extirpate cougars, deer populations explode.
b.Cyclamen Mites: Cyclamen mites are a pest of strawberries, but they are preyed upon by Typhlodromus
mites that keep the m in check. Plots lacking predator populations had 25 times higher populations of Cyclamen
mites.
c. Cactus Moths: Cactoblastis moth from Argentina brought in to Australia to limit the spread of Opuntia
cactus. Knocked the population back by 99%.
d. Klamath Weed: Klamath weed was introduced from Europe to the western U.S., where it was
poisoning livestock. Introduction of a herbivorous insect Chrysolina reduced populations by 99%.
e. Grazing mammals: Even grazing mammals can limit plant populations, as exclosure experiments with
cattle and voles demonstrate. Where excluded, plant biomass increases and the relative abundance of plants
species changes.
f. Wolves/moose on Isle Royale: Wolves crossed an ice bridge and began to prey on a previously
insulated moose population. Predation is heaviest in winter when wolves run on top of the snow.
g. Urchins and kelp: The urchins remained high and grazed kelp to nothing because they had an
alternative food supply…
B. Oscillating Populations is a Common Pattern
1. Examples
a. Lynx-snowshoe hare data from Hudson’s Bay Trapping Co.
b. Vole – owl populations in Sweden
c. Measles in England
2. Reasons
a. Time Lags: time lags between birth and reproduction in both populations. So, A prey population
experiencing heavy mortality from predators will continue to decline even after the predators have declined,
because many of the prey individuals are pre-reproductives and must mature before they can breed. Likewise,
predator populations will lag because they cannot rebound until 1) after the prey population begins to increase
and 2) they convert this food into offspring. The longer the lag, the greater the amplitude in the oscillation (as
we saw in single species dynamics).
b. Seasonal refuges for prey can increase the lag: The vole-owl example is notable. In the northern part
of the range, the oscillations are large and fluctuate over a 4 year cycle. In the south, the oscillations are smaller
and on a one year cycle. In the north, the voles are protected from owls by winter snow – delaying owl
responses to vole populations and increasing the lag. In the south, owls can track vole populations more closely,
resulting in a smaller oscillation. With global warming, this trend is moving north.
c. Patterns in Resistance/Susceptibility: Disease outbreaks can also be cyclic, dependent upon the growth
of a susceptible population. In addition, the absolute density of the host population is critical, as that influences
transmission rates. Deadly diseases can only persist in a population if the host population is at high density
and/or the infection rate is very high. Selection will tend to favor a decrease in virulence in less dense
populations of hosts.
C. Models
1. Pathogens
Dependent on the rate of transmission (b) and rate of recovery (g), which is related (inversely) to the period
during which the host is contagious, and the number of susceptible, infectious, and recovered people in the
population. The reproductive ratio of the pathogen =(b/g)S, … so a pathogen population will grow (R > 1,
epidemic) if the rate of transmission is high, recovery is slow, or susceptible individuals are common. In this case,
each infected individual infects more than one new host and the disease spreads. As a disease proceeds and the
number of susceptible individuals declines, R  < 1 and the epidemic declines. Vaccinations decrease the
number of susceptible people and thwart the epidemic growth of a disease. In the simplest version of this model
(single generation), oscillations don’t occur because there is no production of new susceptible people and
immunity after recovery is permanent. However, if you add the production of susceptible newborns or
susceptible immigrants, or allow for vertical transmission from parent to offspring, or add a latency period, or
allow for reinfection (and not lifetime immunity), oscillations and cyclic epidemic occur because the number of
susceptible people can increase… driving R > 1.
2. Lotka-Volterra Models
- goal was to create a model of two populations, a predator and prey species, that caused oscillations in their
populations and was driven by simple but biologically realistic parameters.
a. Prey Population Growth: rate of change = intrinsic growth rate – death by predation.
dV/dt = rV – cVP
cVP = number dying by predation, which is a function of the encounter rate, which depends on
the population sizes of both species (V)(P), and the ‘functional response’ or ‘capture efficiency’ … what fraction
of those encounters end in the death of the prey.
b. Predator Population Growth: rate of change = birth rate (~ food supply) – death rate
dP= a(cVP) – dP
a = metabolic conversion rate of food to offspring, and cVP = number of prey caught.
c. Dynamics
- in these types of differential growth equations, we are particularly interested in the densities at which growth
rate changes from increasing to decreasing… in other words, where it is at equilibrium (b = d).
For prey, dv/dt = 0 when rV = cVP, or, when P = r/c. curiously, the prey population will reach an equilibrium
independent of its own density, when P = r/c. The prey population will increase (forever) if there are fewer than
this number of predators (P< r/c), and will decrease to zero if there are more than this number of predators
(P>r/c). At all combinations THIS predator density any ANY prey density (defining a line), the prey will neither
increase nor decline… this line of density combinations describing this equilibrial state is called a zero growth
isocline.
- The predators population will equilibrate when dP/dt = 0, when acVP = dP, or when V = d/ac. So, curiously,
there is a particular population size of prey at which the predator population, no matter how big it is, will
equilibrate. Add or substract prey and the predator population will expand or decline, respectively.
- A graphic analysis of the dynamics shows that these populations will indeed oscillate out of phase, just at L-V
wanted. There is a joint equilibrium that is possible where no oscillations will occur, but any perturbation from
this set pof densities will create a neutral oscillation that won’t change unless perturbed again.
d. Support for the Model
- The model predicts that an increase in the birth rate of prey should NOT affect the size of the prey population;
rather, this boost gets transmitted to the predator population. This was confirmed by studies of bacteria and
phage (virus that infects bacteria)
e. Criticisms of the Model
- instantaneous response with no time lags – biologically unrealistic.
- no density dependence in prey, even at low predator densities.
- constant ‘functional response’… the predator efficiency is constant across all prey densities. Predators harvest
the same fraction of a prey population, regardless of the prey density. This was challenged by Hollings, wh
suggested two alternatives. “Type II Functional Response”, in which the percentage of prey taken declines as
prey population increases. This could be explained by satiation (predators are full and so don’t kill prey at the
same rate), or by predators becoming limited by handling time – predators simply cannot capture and kill prey
any faster, regardless of how many more prey there are. Both of these will cause the predation rate to decline,
and the number of prey captured to equilibrate, as prey population size increases. Another density-dependent
phenomena would cause a “Type III functional response”, where there would be low efficiency at low prey
densities… maybe because the predator fails to develop a search image, doesn’t encounter the prey often
enough to know it is food, or does not encounter the prey often enough to develop an efficient handling strategy
(learning). Or, when one prey is at low density, a predator may switch to another, more abundant prey.
D. Lab Experiments
1. Gauss (1920’s):
In simple experiments, Didinium would graze Paramecium to extinction and then collapse. No
oscillations.
He could make the species persist by either creating a refuge for the prey (glass wool) that the predator
didn’t enter, or by adding prey (immigration) periodically.
2. Huffaker (1958):
Eotranychus and Typhlodromus mites on oranges, with the possibility of partitions in between, along
with orange balls that were inhospitable. Also, he put up barriers to the walking predator, and sticks from which
the silk-spinning, ‘parachuting’ prey mite could disperse. Essentially, by increasing the patchiness of the resource
(oranges) and by altering the habitat to create marked differences in dispersal rates, he could get the species to
exhibit persistent oscillations through time.
3. Holyoak and Lawler (1996):
Protist populations could persist in subdivided habitats, also, if migration rates were low. Established
metapopulation dynamics where prey populations would stay one step ahead of the predator populations.
E. Complexities and Applications:
1. Achieving Stable Dynamics
- if predator is inefficient, then both predators and prey eventually achieve high and stable population sizes.
- If predator can switch to alternate prey, or if the prey have a refuge, then the dynamic equilibrates with prey at
low densities (but they don’t go extinct because the predator switches) or at densities dependent on the size of
the refuge.
- Given these likely effects, or the additional stabilizing effects of density dependent effects on the prey, it seems
that the oscillations we see must usually be the result of time lags destabilizing otherwise stable relationships.
2. Alternate Stable States
- dynamics affected by different factors at different densities can be stable at multiple points (densities).
- Consider a predator population with a Type III functional response, inefficient at low and high densities. The
dynamic may equilibrate at low prey density, where prey are limited to refuges, or at high prey densities where
prey have escaped the limitation by satiating predators. Of course, an outbreak can shift the equilibrium from
low to high – and a new stable condition.
- Winter moth: In poor years, the low pupae numbers are consumed by small mammals that keep the population
in check. But in good years, the predators are satiated and the population balloons to the point where predators
no longer limit the prey population size.
3. Application: Maximum Sustainable Yield
- The maximum number of prey items that can be removed without causing a decline in the growth rate of the
population is called the “maximum sustainable yield”
- This will be greatest when the population is growing most rapidly – ie., when a population growing logistically
is at 1/2K.
- In this way, we remove the ‘interest’ (growth differential) generated by the ‘principal’ (population of size
1/2K). The population will not decline, so if the environment is stable, the N won’t change and we can take the
same amount next year. However, if ‘draw down’ the population, we are ‘spending the principal’. This smaller
population will generate fewer organisms next year (less interest) even if the growth rate is the same. This
produces a vicious cycle where a constant harvest eats progressively into the principal, generating less interest
and requiring more draw-down of principal each year.
Study Questions:
1) Write the Lotka-Volterra equation for a prey species. Given this equation, how is predation limiting
population growth (what factor is it contributing too)? Is this a density dependent effect?
2) Write the L-V equation for the predator population. What do each of the parameters in the ‘birth rate’
equation represent?
3) What are the oddities in the isoclines for both species – what determines each isoclines density?
4) Why do epidemic “outbreaks” occur periodically? Answer with respect to the factors that affect the
reproductive ratio of the pathogen.
5) Describe the three types of functional responses of predators, and explain why II and II are more biologically
realistic than I.
6) The experiments of Gause, Huffaker, and Holyoak and Lawler shed light on factors that can promote
coexistence between predator and prey populations. Describe the contributions of these experiments,
highlighting how coexistence was maintained in each.
7) How can multiple stable states occur in predator-prey systems, and what biological event might result from
such patterns?
8) What is Maximum Sustainable Yield? Explain the long-term problems associated with overharvesting.