From individuals to populations. The basic entities of ecological research. Bees, ants, and other insect societies form superorganisms that behave as an ecological unit. Single information coding strand of DNA. Single celled Bacteria. Clonal organisms might have extreme longivity.
From individuals to populations
The basic entities of ecological research
Bees, ants, and other insect societies form superorganismsthat behave as an ecological unit.
Single information coding strand of DNA
Single celled Bacteria
Clonal organisms might have extreme longivity
Unitary organisms have genetically prescribed longivity
Clonal Populustremuloides forests
Modular organized Brown fungi
A clonal colony or genet is a group of genetically identical individuals, such as plants, fungi, or bacteria, that have grown in a given location, all originating vegetative, not sexually, from a single ancestor. In plants, an individual in such a population is referred to as a ramet.
A modular organism has an indeterminate structure wherein modules of various complexity (e.g., leaves, twigs) may be assembled without strict limits on their number or placement.
All organisms have life cycles from single celled zygotes through ontogenetic stages to adult forms. All organsims finally die.
Often stages of dormancy
Type I, high survivorship of young individuals: Large mammals, birds
Type II, survivorship independent of age, seed banks
Type III, low survivorship of young individuals, fish, many insects
Age dependent survival in annual plants
Each life stage t has a certain mortality rate dt.
The k-factor is the difference of the logarithms of the number of surviving indiiduals at the beginning and the end of each stage.
A simple life table
k-factors calculated for a number of years
Cleartemporaltrends in mortalityrates
No densitydependencein mortalityrates
Allometric constraints on life history parameters
Body size is an important determinant on life history.
Life history trade-offs
Optimal food intaketime
Quality of food
Degree of starvation
Number of offspring
Trade-offs: Organisms allocate limited energy or resources to one structure or function at the expense of another.
Allspecies face trade-off.
Trade-offs shape and constrain life history evolution.
Complex life histories appear to be one way to maximize reproductive success in such highly competitive environments.
The importance of individualistic behaviour
Th perceived food value migh remain more stable than food quality
The value of food is the product of food quality and the difference of total amount N and amount consumed C).
Amount of food consumed
For different individuals it pays to use resources of different quality.
Trade-offs between resource quality and resource availability at a given point of time mark the beginn of individualistic behaviour.
Individualistic behaviour is already observable in bacteria.
The precise estimation of resource value is one of the motors of brain evolution.
Trade-off decisions during life history
At each time step in life animals take decisions.
These decisions determine future reproductive success and ae objects of selective forces
How large to grow?
When to begin reproducing?
How fast to grow?
Each step is a decision on resource allocation.
How often to breed?
How long to live?
How many offspring?
When to change morphology?
Caring for offspring?
How long to live after reproduction?
What size of offspring?
How fast to develop?
Different selective forces might act on different stages of life.
Contrary forces might cause the development of subpopulations.
Contrasting selective forces on life history
r-selection and K-selection describe two ends of a continuum of reproductive patterns.
r refers to the high reproductive rate.
K refers to the carrying capacity of the habitat
High reproduction rate
High population growth
Low parental investment
No care of offspring
Often unstable habitats
Low reproduction rate
Low population growth
High parental investment
Intensive care of offspring
Often stable habitats
In many species different developmental stages,the sexes and particulalry subpopulations range differently on the r/K continuum!
mature more slowly and have a later age of first reproduction
have a longer lifespan
have few offspring at a time and are iteroparous
have a low mortality rate and a high offspring survival rate
have high parental investment
Have often relatively stable populations
r selected species
mature rapidly and have an early age of first reproduction
have a relatively short lifespan
have few reproductive events, or are semelparous
have a high mortality rate and a low offspring survival rate
have minimal parental care/investment
are often highly variable in population size
Literature: Reznick et al. 2002, Ecology 83.
The growth of populations
Number of births
Number of deaths
The net reproductive rate R is the number of reproducing female offspring produced per female per generation.
If R > 1: population size increases
If R = 1: population remains stable
If R < 1: population size decreases
The density of a population is the average number of individuals per unit of area.
Abundance is the total number of individuals in a given habitat.
The exponential growth of populations
The intrinsic rate of population growth r (per-capita growth rate) is fraction of population change per unit of time.
If r > 0: population size increases
If r = 0: population remains stable
If r < 0: population size decreases
North atlantic gannets in north-western England (Nelson 1978)
Population doubling time
Under exponential growth there is no equilibrium density.
Exponential growth is not a realistic model since populations cannot infinite sizes.
The growth rate is r = 0.057
The logistic growth of populations
Populations do not increase to infinity. There is an upper boundary, the carrying capacity K.
The logistic model of population growth
The logistic growth function is the standard model in population ecology
Pierre Francois Verhulst (1804-1849)
Raymond Pearl (1879-1940)
The logistic growth of populations
Maximum population growth
The equilibrium population size
Time t0 of maximum growth
The logistic growth of populations
How to estimate the population parameters?
Growth of yeast cells (data from Carlson 1913)
K = 665
Logistic growth occurs particularly in organisms with non-overlapping (discrete) populations, particularly in semelparous species: e.g. bacteria, protists, single celled fungi, insects.
Logistic population growth implies a density dependent regulation of population size
If N > K, dN/dt < 0: the population decreases
Natural variability in population size
Density dependence means that the increase or decrease in population size is regulated by population size.
The mechanism of regulation is intraspecific competition.
The number of offspring decrease with increasing population size due to resource shortage.
The Allee effect
Logistic growth is equivalent to a quadratic function of population growth
No Allee effect
Weak Allee effect
Strong Allee effect
At low population size propolation growth is in many cases lower than predicted by the logistic growth equation.
Allee extension of the logistic function
Most often Allee effects are caused by mate limitation at low population densities
A is an empitical factor that determines the strength of the Allee effect
Variability in population size
We use the variance mean ratio as a measure of the type of density fluctuation
The Lloyd index of aggregation needs m > > 1.
Taylor’s power law
The metapopulation of Melitaeacinxia
In fragmented landscapes populations are dived into small local populations separated by an inhostile matrix.
Between the habitat patches migration occurs.
Such a fragmented population structure connected by dispersal is called a metapopulation.
Different types of metapopulations
The Lotka – Volterra model of population growth
Dispersal in a fragmentedlandscape
Levins (1969) assumed that the change in the occupancy of single spatially separated habitats (islands) follows the same model.
Assume Pbeing the number of islands (total K) occupied. Q= K-Pis then the proportion of not occupied islands. m is the immigration and e the local extinction probability.
The Levinsmodel of meta-populations
Fragments differ in population size
Colonisation probability is exponentially dependent on the distance of the islands and extinction probability scales proportionally to island size.
The higher the population size is, the lower is the local extinction probability and the higher is the emigration rate
If we deal with the fraction of fragments colonized
The canonical model of metapopulation ecology
Metapopulation modelling allows for an estimation of species survival in fragmented landscapes and provides estimates on species occurrences.
If we know local extinction times TL we can estimate the regional time TR to extinction
When is a metapopulation stable?
Median time to extinction
The meta-population is only stable if m > e.
The condition for long-term survival
What does metapopulation ecology predict?
Occurrences of Hesperia comma in fragmented landscapes in southern England (from Hanski 1994)
In fragmentedlandscapesoccupancydeclinesnonlinear with decreasingpatcharea and with decreasingconncetivity (increasingisolation)
Extinction times of ground beetles on 15 Mazurian lake islands
Local extinction times (generations) are roughly proportional to local abundances
Population should be save if they occupy at least 12 islands.
Populationecologyneedslong-term data sets