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LECTURE PRESENTATIONS For CAMPBELL BIOLOGY, NINTH EDITION Jane B. Reece, Lisa A. Urry, Michael L. Cain, Steven A. Wasserman, Peter V. Minorsky, Robert.

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Presentation on theme: "LECTURE PRESENTATIONS For CAMPBELL BIOLOGY, NINTH EDITION Jane B. Reece, Lisa A. Urry, Michael L. Cain, Steven A. Wasserman, Peter V. Minorsky, Robert."— Presentation transcript:

1 LECTURE PRESENTATIONS For CAMPBELL BIOLOGY, NINTH EDITION Jane B. Reece, Lisa A. Urry, Michael L. Cain, Steven A. Wasserman, Peter V. Minorsky, Robert B. Jackson © 2011 Pearson Education, Inc. Lectures by Erin Barley Kathleen Fitzpatrick Population Ecology Chapter 53

2 Figure 53.1 Population ecology: the study of populations in relation to their environment, including environmental influences on density and distribution, age structure, and population size

3 Population: a group of individuals of a single species living in the same general area –Described by their boundaries and size Density: the number of individuals per unit area or volume Dispersion: the pattern of spacing among individuals within the boundaries of the population © 2011 Pearson Education, Inc.

4 Density: A Dynamic Perspective Usually it is impractical/impossible to census Sampling techniques  used to estimate densities and total population sizes Population size can be estimated by either: –extrapolation from small samples, an index of population size (e.g., number of nests), –or the mark-recapture method © 2011 Pearson Education, Inc.

5 Mark-recapture method –Capture, tag, and release a random sample of individuals (s) in a population –Allow time to mix back into the population –Scientists capture a second sample of individuals (n), and note how many of them are marked (x) –Population size (N) is estimated by… © 2011 Pearson Education, Inc. sn x N 

6 Figure 53.3 BirthsDeaths ImmigrationEmigration Births and immigration add individuals to a population. Deaths and emigration remove individuals from a population.

7 © 2011 Pearson Education, Inc. Video: Flapping Geese (Clumped)

8 Figure 53.4 (a) Clumped (b) Uniform (c) Random Dispersion is affected by environmental and social factors

9 Figure 53.4a Clumped

10 © 2011 Pearson Education, Inc. Video: Albatross Courtship (Uniform)

11 Figure 53.4b Uniform – may result from territoriality

12 © 2011 Pearson Education, Inc. Video: Prokaryotic Flagella (Salmonella typhimurium) (Random)

13 Figure 53.4c Random: no strong attractions or repulsions

14 Demographics Demography: 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 © 2011 Pearson Education, Inc.

15 Table 53.1 Life table: an age-specific summary of the survival pattern of a population Often follows a cohort, a group of individuals of the same age

16 Males Females 1, Age (years) Number of survivors (log scale) Survivorship curve: represents the data in a life table

17 Survivorship curves can be classified into three general types –Type I: low death rates during early and middle life and an increase in death rates among older age groups –Type II: a constant death rate over the organism’s life span –Type III: high death rates for the young and a lower death rate for survivors Many species are intermediate to these curves © 2011 Pearson Education, Inc.

18 Figure ,000 III II I Percentage of maximum life span Number of survivors (log scale)

19 Table 53.2 Reproductive Tables Demographers study reproductive rates of females in sexually reproducing species.

20 Concept 53.2: The exponential model describes population growth in an idealized, unlimited environment Idealized situations help us understand the capacity of species to increase and the conditions that may facilitate this growth © 2011 Pearson Education, Inc.

21 Per Capita Rate of Increase © 2011 Pearson Education, Inc. Change in population Size Births Immigrants entering population Deaths Emigrants leaving population  

22 Population growth rate: © 2011 Pearson Education, Inc.  N: change in population size,  t: time interval, B: number of births, D: is the number of deaths

23 © 2011 Pearson Education, Inc. b: annual per capita birth rate, m (for mortality): annual per capita death rate N: population size Births per year, B  bN Death per year, D  mN

24 The population growth equation can be revised © 2011 Pearson Education, Inc. = N (b – m)

25 The per capita rate of increase (r) is given by © 2011 Pearson Education, Inc. r  b  mr  b  m Zero population growth (ZPG): birth rate equals the death rate (r  0) NN tt  rN

26 Summary  N/  t = B – D  N/  t = bN – mN  N/  t = N (b – m)  N/  t = rN N = population size t = time B = # births D = # deaths b = annual per capita birth rate m = annual per capita death/mortality rate r = per capita rate of increase = b - m

27 Exponential Growth Exponential population growth: population increase under idealized conditions © 2011 Pearson Education, Inc. dN dt  r max N Results in a J-shaped curve Cannot be sustained for long in any population

28 Number of generations Population size (N) ,000 1,500 1, dN dt = 1.0N = 0.5N Figure 53.7

29 The J-shaped curve of exponential growth characterizes some rebounding populations –For example, the elephant population in Kruger National Park, South Africa, grew exponentially after hunting was banned © 2011 Pearson Education, Inc.

30 Figure 53.8 Year Elephant population 8,000 6,000 4,000 2,

31 Concept 53.3: The logistic model describes how a population grows more slowly as it nears its carrying capacity A more realistic population model limits growth by incorporating carrying capacity Carrying capacity (K): the maximum population size the environment can support –Varies with the abundance of limiting resources © 2011 Pearson Education, Inc.

32 The Logistic Growth Model The per capita rate of increase (r) declines as carrying capacity is reached The logistic model starts with the exponential model and adds an expression that reduces per capita rate of increase as N approaches K dN dt  (K  N) K r max N © 2011 Pearson Education, Inc.

33 Below carrying capacity: K – N/K = 40 – 2/40 = 0.95, little effect on rN Above carrying capacity: K – N/K = 40 – 39/40 = 0.025, drastically reduces rN

34 Table 53.3

35 Number of generations Population growth begins slowing here. Exponential growth Logistic growth Population size (N) ,000 1,500 1, K = 1,500 dN dt = 1.0N dN dt = 1.0N 1,500 – N 1,500 () Figure 53.9 The logistic model produces a sigmoid (S- shaped) curve

36 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 © 2011 Pearson Education, Inc. Some populations overshoot K before settling down to a relatively stable density

37 Figure Time (days) (a) A Paramecium population in the lab (b) A Daphnia population in the lab Number of Paramecium/mL Number of Daphnia/50 mL 1,

38 Some populations fluctuate greatly and make it difficult to define K Allee effect: individuals have a more difficult time surviving or reproducing if the population size is too small The logistic model fits few real populations but is useful for estimating possible growth © 2011 Pearson Education, Inc.

39 Concept 53.4: Life history traits are products of natural selection An organism’s life history comprises the traits that affect its schedule of reproduction and survival –The age at which reproduction begins –How often the organism reproduces –How many offspring are produced during each reproductive cycle  evolutionary outcomes reflected in the development, physiology, and behavior of an organism © 2011 Pearson Education, Inc.

40 Evolution and Life History Diversity Semelparity, or big- bang reproduction: reproduce once and die. Favored in variable/unpredictable environments. Iteroparity, or repeated reproduction: produce offspring repeatedly. Favored in stable/predictable environments. © 2011 Pearson Education, Inc.

41 “Trade-offs” and Life Histories Organisms have finite resources, which may lead to trade-offs between survival and reproduction –For example, there is a trade-off between survival and paternal care in European kestrels © 2011 Pearson Education, Inc.

42 Figure Male Female RESULTS Reduced brood size Normal brood size Enlarged brood size Parents surviving the following winter (%)

43 K-selection, or density-dependent selection, selects for life history traits that are sensitive to population density. More common in populations living close to K; puts a greater investment in the offspring. r-selection, or density-independent selection, selects for life history traits that maximize reproduction. More common in colonizing species. These are two extremes on a spectrum © 2011 Pearson Education, Inc.

44 Figure (a) Dandelion: r strategist (b) Brazil nut tree (right) and seeds in pod (above): K strategist

45 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? © 2011 Pearson Education, Inc.

46 Population Change and Population Density In density-independent populations, birth rate and death rate do not change with population density In density-dependent populations, birth rates fall and death rates rise with population density © 2011 Pearson Education, Inc.

47 When population density is low, b > m. As a result, the population grows until the density reaches Q. When population density is high, m > b, and the population shrinks until the density reaches Q. Equilibrium density (Q) Density-independent death rate (m) Density-dependent birth rate (b) Population density Birth or death rate per capita Figure 53.15

48 Mechanisms of Density-Dependent Population Regulation Density-dependent birth and death rates are an example of negative feedback that regulates population growth Affected by many factors such as: – competition for resources, –territoriality, –disease, –predation, –toxic wastes, –and intrinsic factors © 2011 Pearson Education, Inc.

49 Figure Population size % of young sheep producing lambs

50 Population Dynamics The study of population dynamics focuses on the complex interactions between biotic and abiotic factors that cause variation in population size © 2011 Pearson Education, Inc.

51 Stability and Fluctuation Long-term population studies have challenged the hypothesis that populations of large mammals are relatively stable over time Both weather and predator population can affect population size over time –For example, the moose population on Isle Royale collapsed during a harsh winter, and when wolf numbers peaked © 2011 Pearson Education, Inc.

52 WolvesMoose Year Number of wolves Number of moose ,500 2,000 1,500 1, Figure 53.18

53 Population Cycles: Scientific Inquiry Some populations undergo regular boom-and-bust cycles Lynx populations follow the 10-year boom-and- bust cycle of hare populations Three hypotheses have been proposed to explain the hare’s 10-year interval © 2011 Pearson Education, Inc.

54 Snowshoe hare Lynx Year Number of hares (thousands) Number of lynx (thousands) Figure 53.19

55 Hypothesis 1: The hare’s population cycle follows a cycle of winter food supply If this hypothesis is correct, then the cycles should stop if the food supply is increased Additional food was provided experimentally to a hare population, and the whole population increased in size but continued to cycle These data do not support the first hypothesis © 2011 Pearson Education, Inc.

56 Hypothesis 2: The hare’s population cycle is driven by pressure from other predators In a study conducted by field ecologists, 90% of the hares were killed by predators These data support the second hypothesis © 2011 Pearson Education, Inc.

57 Hypothesis 3: The hare’s population cycle is linked to sunspot cycles Sunspot activity affects light quality, which in turn affects the quality of the hares’ food There is good correlation between sunspot activity and hare population size © 2011 Pearson Education, Inc.

58 The results of all these experiments suggest that both predation and sunspot activity regulate hare numbers and that food availability plays a less important role © 2011 Pearson Education, Inc.

59

60 Figure Topsoil Bacteria EXPERIMENT Dictyostelium amoebas Dictyostelium discoideum slug 200  m Immigration, Emigration, and Metapopulations A group of Dictyostelium amoebas can emigrate and forage better than individual amoebas

61 Metapopulations: groups of populations linked by immigration and emigration High levels of immigration combined with higher survival can result in greater stability in populations © 2011 Pearson Education, Inc.

62 Aland Islands ˚ EUROPE Occupied patch Unoccupied patch 5 km Figure 53.21

63 Concept 53.6: The human population is no longer growing exponentially but is still increasing rapidly No population can grow indefinitely, and humans are no exception The human population increased relatively slowly until about 1650 and then began to grow exponentially © 2011 Pearson Education, Inc.

64 Figure The Plague Human population (billions) 8000 BCE 4000 BCE 2000 CE 1000 BCE 2000 BCE 3000 BCE 1000 CE

65 Projected data 2009 Annual percent increase Year Figure The rate of growth began to slow during the 1960s

66 Regional Patterns of Population Change To maintain population stability, a regional human population can exist in one of two configurations –Zero population growth = High birth rate – High death rate –Zero population growth = Low birth rate – Low death rate The demographic transition is the move from the first state to the second state © 2011 Pearson Education, Inc.

67 The demographic transition is associated with increase in the quality of health care and improved access to education, especially for women Most of the current global population growth is concentrated in developing countries where the demographic transition is not complete. © 2011 Pearson Education, Inc.

68 Age Structure One important demographic factor in present and future growth trends is a country’s age structure Age structure is the relative number of individuals at each age © 2011 Pearson Education, Inc.

69 Figure Percent of population Rapid growth Afghanistan Slow growth United States No growth Italy Male Female Age –84 75–79 70–74 65–69 60–64 55–59 50–54 45–49 40–44 35–39 30–34 25–29 20–24 15–19 10–14 5–9 0–4 Age –84 75–79 70–74 65–69 60–64 55–59 50–54 45–49 40–44 35–39 30–34 25–29 20–24 15–19 10–14 5–9 0–

70 Infant Mortality and Life Expectancy Infant mortality and life expectancy at birth vary greatly among developed and developing countries but do not capture the wide range of the human condition © 2011 Pearson Education, Inc.

71 Indus- trialized countries Less indus- trialized countries Infant mortality (deaths per 1,000 births) Life expectancy (years) Figure 53.25

72 Global Carrying Capacity How many humans can the biosphere support? Population ecologists predict a global population of 7.8  10.8 billion people in 2050 The carrying capacity of Earth for humans is uncertain: The average estimate is 10–15 billion © 2011 Pearson Education, Inc.

73 Limits on Human Population Size The ecological footprint concept summarizes the aggregate land and water area needed to sustain the people of a nation It is one measure of how close we are to the carrying capacity of Earth Countries vary greatly in footprint size and available ecological capacity © 2011 Pearson Education, Inc.

74 Figure Gigajoules > –300 50–150 10–50 < 10

75 Our carrying capacity could potentially be limited by food, space, nonrenewable resources, or buildup of wastes Unlike other organisms, we can regulate our population growth through social changes © 2011 Pearson Education, Inc.

76 Figure 53.UN01 Patterns of dispersion ClumpedUniformRandom

77 Figure 53.UN02 Number of generations Population size (N) = r max N dN dt

78 Figure 53.UN03 Number of generations Population size (N) = r max N dN dt K – N K K = carrying capacity ()

79 Figure 53.UN04

80 Figure 53.UN05

81 Figure 53.UN06


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