Understanding Populations and their Place in the Biosphere

1. Populations and our place in the Biosphere To begin understanding populations, remember the definition of ecology: Ecology is the study of the distribution and abundance of species (or populations). It is also the study of causes of those distributions and abundances.

2. What kinds of distributions are typically seen in natural populations? Three kinds represent pigeonholes along what is really a continuous distribution of kinds: 1) random 2) regular (or overdispersed) 3) clumped (or contagious)

3. 1) Randomly - one example is the distribution of trees of a single species in the diverse and complex tropical forest.

4. 2) Regular - (or uniform or overdispersed) - One example of this distribution include the penguins shown below on nesting territories. Each one is just beyond the reach of its neighbors. Another is the spacing of creosote bushes, each poisoning the ground around it.

5. 3) clumped - organisms ‘gather’ where conditions are better for them. When you measure population density, it is higher where conditions are better. This is, especially when viewed from a larger perspective, the most common pattern.

6. With that background about distribution, what about abundance? Populations are dynamic. They grow in size (number). Growth seems to follow two different patterns: 1) Exponential (or density-independent) growth 2) Logistic (or density-dependent) growth If a real population grew indefinitely, it would eventually follow the logistic pattern (because the resources supporting it are not infinite, but some species (like insects) grow seasonally, and during their growing season, grow essentially exponentially.

7. Exponential growth -Limits to exponential growth are set by abiotic factors, e.g. weather -The same proportion of population is affected by abiotic factors, whatever the size or density of the population (that’s why it’s described as density independent. -The slope of the growth curve (rate of growth) increases as population grows, and in direct proportion to the size. Mathematically:

8. Graphically: The 1.0 and 0.5 are values of r, the growth rate per unit of time. N is the population size.

9. Populations that seem to grow exponentially still have a carrying capacity - a population size that can be supported by prevailing conditions and resources. Some populations growing exponentially can, due to their rapid rates of increase, temporarily exceed the carrying capacity. Those populations then crash to sizes well below carrying capacity. Think of this as boom and bust. carrying capacity, K # time

10. Logistic growth -Limits to growth are set by biotic factors, e.g. food, interactions with other species like competition or predation -The growth curve at low density looks like the exponential, but slope and growth rate decline as numbers increase, until there is no further growth when population size reaches K, the carrying capacity. -The proportion affected increases with density (so this growth pattern is called density-dependent.

11. Mathematically: Graphically: The data points are a real growth curve for Paramecium in a culture flask.

12. Biological interactions influence growth rate: a) predation, parasitism (and disease, which is a form of parasitism) reduce growth rate b) predators and parasites are more likely to encounter and attack members of a population as its density increases c) competition, both between species and among members of this population, has greater impact as density increases d) stresses in the denser population also increase the likelihood that members will emigrate

13. e. Thus, the growth rate slows as the carrying capacity is approached. In theory, growth ceases, and the population reaches an equilibrium at the carrying capacity. f. Due to the shape of the growth curve (basically S-shaped), logistic growth is sometimes described as ‘sigmoid’.

14. What processes determine the changes in population size? Birth - adds new individuals to a population Death - removes the dying from the population Immigration - brings new individuals into it Emigration - individuals leave, decreasing the population We usually disregard immigration and emigration in simple models of population growth. Instead we study the demography of the population.

15. Demography is the study of the age-specific survivorship and reproduction of individuals in a population. From these we can predict whether a population is going to grow, remain stable, or decline. There are two approaches: numerical and quantitative or descriptive and graphical. First the graphical approach: a “demographers curve” plots the fractions of a population in each age class, separating males and females

16. Note the differences in shapes of demographers’ curves for Sweden, Mexico, and the U.S. What do the differences tell us?

17. 1. The ‘curve’ or histogram is divided into 3 regions: pre-reproductive (ages < puberty) reproductive (ages from puberty to menopause) post-reproductive 2. The shape tells us about population growth: If the curve is wider (a larger fraction) in the young ages then more will be becoming reproductive than ceasing reproduction - more babies means a growing population. If the curve is straight along the edges, a similar number is beginning and stopping reproduction; population size will remain constant, called zero population growth.

18. 3. When there’s a ‘hump’ in the curve (go back and look at the curve for the U.S.), it indicates a transient increase in the number of babies - a “baby boom”. These data clearly show the post- WWII boomers.

19. Now the quantitative approach: structure and use of a life table. In a life table we follow a cohort (a group of organisms born at the same time), recording how many are alive at each time interval, and how many young each female has during the interval. The numbers in the life table are: a) age structure - the age classes b) survivorship - how many from the cohort (what fraction?) are still alive at age x? c) age specific natality - how many young are born to females of each age?

20. Here’s what a life table looks like: fraction alive fecundity Age #alive lx #dying mx 0 1000 1 500 0 1 500 .5 250 2 2 250 .25 125 4 3 125 .125 62 4 4 62 .0625 62 1 From it many things can be calculated:

21. 1) population growth rate (r) - combines survivorship and natality (births) into an instantaneous growth rate. It is analogous to the interest rate on your bank account (if the bank compounded instantaneously). However, no bank compounds instantaneously; the best available is daily compounding. 2) how much longer is an organism already x years old likely to live? This is what your insurance needs to know. It is called ex (or life expectancy).

22. There are typical patterns for a curve of survivorship over the lifespans of organisms. There are 3 patterns representing a continuum of possibilities:

23. Type I - organisms live out a very large fraction of their genetically programmed maximum lifespan. Humans and other large mammals have this survivorship pattern. Type II - organisms suffer a constant proportional mortality over time, e.g. the sample life table. Perching birds and bats are good examples of this survivorship. Type III - suffer very high mortality in initial periods of life, but have high survivorship thereafter. A maple tree, oyster, or salmon are good examples here.

24. There are also characteristic patterns of natality, and they are associated with the survivorships. In sum, patterns in survivorship and natality are called: Life History Patterns Again there is a continuum, but we recognize differences between the extremes: opportunistic (r-selected, weedy) species - typically capable of rapid growth when conditions are good versus equilibrial (K-selected, climax) species - grow only slowly, but maintain populations near carrying capacity, K.

25. Characteristics OpportunisticEquilibrial Age of 1st early, low later, older reproduction litter size large small size of offspring small large parental care no variable, some yes organism size typically small typically large population size variable - small stable - high growth pattern frequently looks logistic exponential