Chapter 53

1. Chapter 53 Population Ecology

2. Overview: Counting Sheep • A small population of Soay sheep were introduced to Hirta Island in 1932 • They provide an ideal opportunity to study changes in population size on an isolated island with abundant food and no predators They were studied for over 50 yrs. Their numbers were found to vary greatly from year to year. Why? Think of possible reasons.

3. Population ecology is the study of populations in relation to environment, including environmental influences on density and distribution, age structure, and population size • A populationis a group of individuals of a single species living in the same general area. • Three characteristics of a population are its density, dispersion, and demographics. http://wild-facts.com/wp-content/uploads/2009/11/meerkats1.jpg

4. Density and Dispersion • Densityis the number of individuals per unit area or volume • Ex: Number of maple trees in Cambria County • Dispersionis the pattern of spacing among individuals within the boundaries of the population • Demographicsis the study of the vital statistics of a population and how they change over time

5. Density: A Dynamic Perspective • In most cases, it is impractical or impossible to count all individuals in a population • Sampling techniques can be used to estimate densities and total population sizes • Population size can be estimated by either extrapolationfrom small samples, an index of population size, or the mark-recapture method

6. Fig. 53-2 • APPLICATION • This rare species of dolphin was studied by ecologists in New Zealand using the mark-recapture method. • 1. Start by capturing and tagging • (or photograph distinctive • markings) to identify a sample of • dolphins. 180 were identified • 2. Release them—they mix back into • the population. • 3. Recapture a second sample • about a week later. (44 were • captured—7 already tagged or • identified by photographs) • 4. x = m solve for N N = mn • n N x • N= (180)(44)/7 = 1, 131 individuals • Hector’s dolphins • X = number of marked animals recaptured • n = total number of animals recaptured • m = number of individuals marked at beginning • N= estimated population size • Note: This is truly an estimate because it assumes that • marked and unmarked individuals have the same • probability of being captured, that the marked individuals • have completely mixed back into the population, • and that no individuals are born, die, immigrate or emigrate • during the study.

7. Density is the result of an interplay between processes that addindividuals to a population and those that removeindividuals • Immigrationis the influx of new individuals from other areas • Emigrationis the movement of individuals out of a population • Birth rate is the number of new individuals born into a population • Death rate is the number of individuals that die in a population Deaths Births Remove individuals from populations Add individuals to populations Emigration Immigration

8. Patterns of Dispersion • Environmental and social factors influence spacing of individuals in a population • In a clumped dispersion, individuals aggregate in patches. It is most common. • A clumped dispersion may be influenced by resource availability and behavior Clumped Dispersion Video: Flapping Geese (Clumped)

9. A uniform dispersion is one in which individuals are evenly distributed. Not as common as clumped. • It may be influenced by social interactions such as territoriality Uniform Dispersion Video: Albatross Courtship (Uniform)

10. In a random dispersion, the position of each individual is independent of other individuals. Wind-blown seeds for example, are randomly dispersed. This is not real common in nature. • It occurs in the absence of strong attractions or repulsions Random Dispersion Video: Prokaryotic Flagella (Salmonella typhimurium) (Random)

11. Demographics • Demographyis the study of the vital statistics of a population and how they change over time • Death rates and birth rates are of particular interest to demographers • A life table is an age-specific summary of the survival pattern of a population • It is best made by following the fate of a cohort, a group of individuals of the same age • The life table of Belding’s ground squirrels reveals many things about this population

12. Table 53-1

13. Survivorship Curves • A survivorship curve is a graphic way of representing the data in a life table • The survivorship curve for Belding’s ground squirrels shows a relatively constant death rate

14. Fig. 53-5 • 1,000 • 100 • Number of survivors (log scale) • Females • 10 • Males • 1 • 8 • 10 • 0 • 2 • 4 • 6 • Age (years)

15. Survivorship curves can be classified into three general types: • Type I: low death rates during early and middle life, then an increase among older age groups • Type II: the death rate is constant over the organism’s life span • Type III: high death rates for the young, then a slower death rate for survivors • Many species actually fall somewhere between these basic types of curves and show more complex patterns.

16. Fig. 53-6 • Common in animals that have few young and provide extensive care for them • 1,000 • I • Common in a few species of rodents, • Lizards, invertebrates and annual plants. • 100 • II • Number of survivors (log scale) • Common in animals that have many young • and provide little or no care for them. • 10 • III • 1 • 0 • 50 • 100 • Percentage of maximum life span

17. Concept 53.2: Life history traits are products of natural selection • An organism’s life history comprises the traits that affect its schedule of reproduction and survival: It entails three basic variables: • The age at which reproduction begins • How often the organism reproduces • How many offspring are produced during each reproductive cycle • Life history traits are evolutionary outcomes reflected in the development, physiology, and behavior of an organism (i.e. except for humans, organisms do not really choose when to reproduce or how many offspring to have.)

18. Evolution and Life History Diversity • Life histories are very diverse. • Some species that exhibit semelparity, or big-bang reproduction, reproduce once and die. Ex: Pacific Salmon • Some species that exhibit iteroparity, or repeated reproduction, produce offspring repeatedly. Ex: Animals with seasonal mating seasons. • Highly variable or unpredictable environments likely favor big-bang reproduction. Survival rate is low—even for adults. So they produce a lot of young once—in hopes that a few will survive to reproduce. • Dependable environments may favor repeated reproduction. Survival rate is good—for young and adults. The adults will survive to reproduce again. A few large young should also be able to survive to reproductive age.

19. Concept 53.3: The exponential model describes population growth in an idealized, unlimited environment • It is useful to study population growth in an idealized situation • Idealized situations help us understand the capacity of species to increase and the conditions that may facilitate this growth

20. Per Capita Rate of Increase • If immigration and emigration are ignored, a population’s growth rate (per capita increase) equals birth rate minus death rate

21. N  rN t • Zero population growth occurs when the birth rate equals the death rate • Most ecologists use differential calculus to express population growth as growth rate at a particular instant in time: where N = population size, t = time, and r = per capita rate of increase = birth – death

22. Exponential Growth • Exponential population growth is population increase under idealized conditions • Under these conditions, the rate of reproduction is at its maximum, called the intrinsic rate of increase

23. dN  rmaxN dt • Equationof exponential population growth:

24. Exponential population growth results in a J-shaped curve

25. Fig. 53-10 • 2,000 • dN • 1.0N • = • dt • 1,500 • dN • 0.5N • = • dt • Population size (N) • 1,000 • 500 • 0 • 0 • 5 • 10 • 15 • Number of generations

26. The J-shaped curve of exponential growth characterizes some rebounding populations

27. Fig. 53-11 • 8,000 • 6,000 • Elephant population • 4,000 • 2,000 • 0 • 1900 • 1920 • 1940 • 1960 • 1980 • Year

28. Concept 53.4: The logistic model describes how a population grows more slowly as it nears its carrying capacity • Exponential growth cannot be sustained for long in any population • A more realistic population model limits growth by incorporating carrying capacity • Carrying capacity (K) is the maximum population size the environment can support

29. (K  N) dN  rmax N dt K The Logistic Growth Model • In the logistic population growth model, the per capita rate of increase declines as carrying capacity is reached • We construct the logistic model by starting with the exponential model and adding an expression that reduces per capita rate of increase as N approaches K

30. Table 53-3

31. The logistic model of population growth produces a sigmoid (S-shaped) curve

32. Fig. 53-12 • Exponential • growth • 2,000 • dN • 1.0N • = • dt • 1,500 • K = 1,500 • Population size (N) • Logistic growth • 1,000 • 1,500 – N • dN • 1.0N • = • 1,500 • dt • 500 • 0 • 0 • 5 • 10 • 15 • Number of generations

33. The Logistic Model and Real Populations • The growth of laboratory populations of paramecia fits an S-shaped curve • These organisms are grown in a constant environment lacking predators and competitors

34. Fig. 53-13a • 1,000 • 800 • Number of Paramecium/mL • 600 • 400 • 200 • 0 • 0 • 5 • 15 • 10 • Time (days) • (a) A Paramecium population in the lab

35. Some populations overshoot K before settling down to a relatively stable density

36. Fig. 53-13b • 180 • 150 • 120 • Number of Daphnia/50 mL • 90 • 60 • 30 • 0 • 0 • 20 • 40 • 80 • 100 • 120 • 140 • 160 • 60 • Time (days) • (b) A Daphnia population in the lab

37. Some populations fluctuate greatly and make it difficult to define K • Some populations show an Allee effect, in which individuals have a more difficult time surviving or reproducing if the population size is too small

38. The logistic model fits few real populations but is useful for estimating possible growth Scientists can use it to estimate the critical size below which a population, like the white rhino, may become extinct

39. The Logistic Model and Life Histories • Life history traits favored by natural selection may vary with population density and environmental conditions • K-selection, or density-dependent selection, selects for life history traits that are sensitive to population density • r-selection, or density-independent selection, selects for life history traits that maximize reproduction • The concepts of K-selection and r-selection are oversimplifications but have stimulated alternative hypotheses of life history evolution

40. Concept 53.5: Many factors that regulate population growth are density dependent • There are two general questions about regulation of population growth: • What environmental factors stop a population from growing indefinitely? • Why do some populations show radical fluctuations in size over time, while others remain stable?

41. Limiting Factors • Environmental factors that work to keep populations in check are known as limiting factors. • Examples: • Food • Temperature • Access to mates • Space

42. Population Change and Population Density • A birth rate or death rate that does not change with population density is said to be density-independent. • Density-independent limiting factors include things like natural catastrophies (volcanic eruption or flood) • A birth rate or death rate that does change with population density is said to be density-dependentpopulations. • Density-dependent limiting factors include competition for resources, territoriality, disease, predation, toxic wastes, and intrinsic factors • They are all examples of negative feedback that controls growth.

43. Competition for Resources • As population density increases, individuals compete more intensely for food, water and other nutrients. • Such competition helps to establish carrying capacity.

44. Territoriality • In many vertebrates and some invertebrates, competition for territory may limit density Ex: Cheetahs are highly territorial, using chemical communication to warn other cheetahs of their boundaries Oceanic birds exhibit territoriality in nesting behavior

45. Disease • Population density can influence the health and survival of organisms • In dense populations, pathogens can spread more rapidly This fungal infection will spread more rapidly in a densely populated garden http://4.bp.blogspot.com/_EWuR-VzwybY/TEpDPy00V9I/AAAAAAAAENU/1_qfBjN8Xus/s1600/P1015286.JPG

46. Predation • As a prey population builds up, predators may feed preferentially on that species

47. Toxic Wastes • Accumulation of toxic wastes can contribute to density-dependent regulation of population size Example: Ethanol accumulates as a byproduct of yeast fermentation. Most wine is less than 13% alcohol because that is the maximum concentration of ethanol that most wine-producing yeast cells can tolerate.

48. Intrinsic Factors • For some populations, intrinsic (physiological) factors appear to regulate population size Mice in high density populations will become more aggressive. The aggression will create a stress syndrome that causes hormonal changes which delay sexual maturation and depress the immune system. Thus, birth rates decrease and death rates increase

49. Population Dynamics • The study of population dynamics focuses on the complex interactions between biotic and abiotic factors that cause variation in population size • Two examples: • Long-term population studies have challenged the hypothesis that populations of large mammals are relatively stable over time. The sheep mentioned at the beginning of the chapter are an example of this. Weatherseems to greatly affect their population size over time as well as disease spread by parasites.

50. Fig. 53-18 • 2,100 • Most of the population drops coincide with harsh, cold • winters. A few are due to parasites spreading quickly when the • population is dense. • 1,900 • 1,700 • 1,500 • 1,300 • Number of sheep • 1,100 • 900 • 700 • 500 • 0 • 1955 • 1965 • 1975 • 1985 • 1995 • 2005 • Year