Exam review Flashcards
What is demographic stochasticity?
Why is it that some simulation runs result in extinction while others result in exponential growth?
Demographic stochasticity describes individual variability of probability of birth and death, these events are random. (someone gets hit by a car, fallen tree etc.) This usually has a larger impact on smaller populations. Because these event are random, some simulation runs will inevitably go extinct (especially with a small initial population). It is random whether the next event is a birth or a death, and so it could take only a few deaths in a row to put a population on the road to extinction.
What is environmental stochasticity? Why is it that some simulation runs result in extinction while others result in approximately exponential growth?
Refers to unpredictable temporary spatiotemporal fluctuations in environmental conditions, that causes changes in population growth rates. Such as floods, droughts, availability of resources. Usually has as large of an effect on both small and large populations. Again, since they are random, it can lead to extinction in some simulation runs, and exponential growth in others.
Give the formula that can be used to calculate the probability of extinction due to demographic stochasticity.
Pext = N0*(1-b/d)
How is the proportion of simulation runs resulting in population extinction affected if (A) b is increased, (B) d is increased, (C) N is increased?
A. If b is increased, fewer simulation runs result in extinction
B. If d is increased, a larger prop of simulation runs will result in extinction.
C. If N is increased, each population has room for larger fluctuations and fewer simulation runs will go extinct.
How do you calculate a population x time steps into the future using lambda?
For example, 3 time steps into the future.
N1 = N0lambda
N2 = N1lambda
N3 = N2*lambda
What does geometric mean growth rate mean?
How can it be used to examine two different conservation strategies?
it is also known as “stochastic growth factor”. The number we are interested in, in population growth in a temporally fluctuating environment. A population has decreased in the end of a time series if geo.mean is < 1, and has increased i geo.mean > 1 (however, it might have techniqually been extinct sometime during the time series if there exists an extinction threshold.
The geometric mean can be used to estimate if the population will grow or shrink.
For example: lambda during four time steps are: 0.5, 2, 1.5, 0.7
Geometric mean:
0.5 * 2 * 1.5 * 0.7 = 1.05
λ^4 =1.05
λ = fourth root of 1.05
Geometric mean = fourth root of 1.05 = 1.01227
If we use these calculations on hypothetically increased lambdas (from a conservation strategy), we can estimate if a strategy will make a population grow.
What is the Allee effect? Name two biological mechanisms that can result in this effect.
The phenomenon that population growth is negative at low densities and turns positive only after a certain threshold has been passed.
Mate limitation, the difficulty of finding a compatible and receptive mate for sexual reproduction at lower population size or density.
Organisms with cooperative defence or that hunt in groups might do worse when their density is low because of limited group formation.
Imagine a plot of dynamics of logistic growth with Allee-effect. How do you determine the stability of each equilibrium?
Equilibriums are where N = 0, and where population growth rate dN/dt “crosses zero”, crosses the x-axis with N.
Alternating between the equilibriums, the growth rate is negative at low densities (from N1 to N2) and becomes positive after reaching the threshold N2. After reaching the threshold N3, it becomes negative once again. So it alternates between positive and negative.
If our starting population size is between N1 and N2, the growth rate is negative and pop will decrease until equilibrium N1.
If our starting population size is between N2 and N3, the growth rate is positive and pop will increase until equilibrium N3.
If it is larger than N3, it will decrease until reaching N3.
N1 and N3 are stabile equilibria and N2 is unstable. These always alternate.
What is the difference between local attractors and global attractors (Allee effect)
Equilibrias that are attractors of the dynamics for only a subset of initial conditions are called local attractors. For example N1 is the attractor of the interval 0 to N2.
Equilibrias that are attractors of the dynamics of all possible initial consitions (except unstable equilibria) are called global attractors. N3 is a global attractor with the interval N2 to infinity.
What is the implication of an Allee effect for the conservation of rare endangered species?
If there exists a threshold of population density such as under that threshold the population is destined for extinction without help, this of course has big implications for conservation. Knowing which direction a species is going in, which equilibria it is approaching and whether this is stable or unstable can be huge help in knowing where conservation efforts should be made.
Explain asymmetric competition
Asymmetric competition is an unequal division of resources among competing species.
Explain “ghost of competition past”
A term proposed to describe a possible reason for observed differentiations in niches. Two competing species might be less fit than a species that occupies a niche that does not overlap with other species, and therefore avoid competition. This way, natural selection might favor the non-competing species: it’s population increases while those of the competing species decrease.
The then observed differentiation in population sizes are a result of past competition, “a ghost of competition past”.
Which are the four conditions for classical metapopulation dynamics.
- The suitable habitat for a certain species occurs in discrete patches.
- Even the largest local populations have a substantial risk of extinction (if not, we have an island-mainland metapopulation)
- Habitat patches are not too isolated to prevent recolonizations (if they are too isolated, we have a non-equilibrium metapopulation)
- Local populations do not have completely synchronous dynamics (if they do, the metapopulation will not persist much longer than the local population with the smallest extinction risk)
How does the incidence function model improve our ability to understand metapopulation
dynamics as compared to Levins (or general) metapopulation model?
DONT KNOW!!
Rapoports rule.
explain the pattern in 1-2 sentences.
provide 2-3 sentences on one or more potential mechanisms thought to generate the pattern.
The tendency for species living at higher latitudes to have larger range sizes.
At higher altitudes there is greater climatic variability, which selects for broader environmental tolerances and makes species able to become more widespread in different environments. It is also hypothesised that species from higher altitudes with restricted ranges went extinct due to glaciation and climate change, leaving only those with larger ranges.
The latitudinal diversity gradient.
explain the pattern in 1-2 sentences.
provide 2-3 sentences on one or more potential mechanisms thought to generate the pattern.
The fact that there is higher diversity with lower latitude, closer to the equator and the tropics, while habitats in higher latitudes generally have lower diversity.
This is thought to be because of higher productivity in the tropics, more sun and higher temperatures gives more food, conditions for reproduction all year round, which can in turn give higher speciation rates. More stable climates can also mean that there is and has been lower extinction rates at lower latitudes.
The very basic version of Lotka - Volterra predator-prey model is:
dN/dt = rN – αNP and
dP/dt = fαNP-qP
How is predators functional response included in this model?
The predators functional response is the relationship between the per capita rate of consumption and the number of prey.
aN in this model is the capture efficiencynumber of prey.
How can predator functional response be modified to add more realism in the Lotka-Volterra model?
It becomes more realistic if we add handling time (type 2) or searching time (type 3). That way the predator cannot consume infinitely many prey as the prey population grows, because eventually the number of prey that can be consumed is limited by the handling time.
The diagram…
The very basic version of Lotka - Volterra predator-prey model is:
dN/dt = rN – αNP and
dP/dt = fαNP-qP
How is preys carrying capacity included in this model?
It’s not. To include prey carrying capacity the first part of the model would be:
dN/dt = rN (1-N/K) - aNP
By doing this the prey population would not be able to grow forever, which is important if we want to understand the cycles of predator and prey.
During the food web part, we talked about three different approaches to construct food webs, what are they?
- Connectedness web
- Energy flow web
- Functional web