Evaluating the Controls on Population Size

1
Evaluating the Controls on Population Size

• Chapter 13

2
Introduction

• Migrating wildebeest
• The most common herbivore in Africa.

3
Introduction

• Wildebeest population in the Serengeti region of
  Tanzania.
• Decline in population from 1.2 million to 0.9
  million. Decrease due to
• Increased poaching
• Change in climate
• Renewed drought
• Changes in the rate of predation

4
Introduction

• 40 year study (Mduma et al. 1999)
• Predation played a minor role in decline (gt3).
• Illegal harvesting accounted for 20,000 per year.
• Main cause of mortality malnutrition brought on
  by drought.

5
Introduction

• Long-term detailed studies are needed to
  disentangle the effects of multiple factors on
  populations.

6
Comparing the Strengths of Mortality Factors

• A number of factors can potentially affect
  populations away from the mean or equilibrium
  levels.
• Common biotic forces.
• Ex. competition, predation, parasitism,
  herbivory, and mutualism.
• If biotic forces are of overriding importance,
  then communities may be tightly knit.

7
Comparing the Strengths of Mortality Factors

• Climate and weather.
• If abiotic forces are the most influential, then
  community structure may be loose and ephemeral.

8
Comparing the Strengths of Mortality Factors

• Terms for population change.
• Extrinsic factors, such as weather, parasitism,
  and predation.
• Intrinsic factors, such as endocrine and immune
  systems.

9
Comparing the Strengths of Mortality Factors

• Evaluating controls on population change involves
  determining the relative killing power of each
  type of mortality.
• A comparison of mortality factors is essential to
  both population and community ecology theory.

10
Comparing the Strengths of Mortality Factors

• Key factors those factors which can disturb a
  population away from the mean or equilibrium.

11
Comparing the Strengths of Mortality Factors

• Key-factor analysis technique for determining
  the importance of a variety of factors affecting
  a population.
• Requires detailed information on the fate of a
  cohort of individuals.
• Total mortality of a generation or cohort (K) is
  divided into various causes, and the relative
  importance of these causes are compared.

12
Comparing the Strengths of Mortality Factors

• Ex. Oak winter moth (Varley and Gradwell).
• Number of females and eggs laid were estimated.
• Many caterpillars lost while dispersing to trees
  with leaves overwintering loss.
• Estimated caterpillars based on the number of
  silken threads they put down to pupate on the
  soil.
• Used metal traps on the soil, to estimate the
  number of caterpillars per m2.
• Determined number of healthy vs. sick
  caterpillars. Some sick caterpillars were
  infected with a parasite. Number of sick
  caterpillars was usually less than 10.

13
(No Transcript)
14
Comparing the Strengths of Mortality Factors

• Some caterpillars died after they arrived in the
  soil to pupate.
• Another parasite caused substantial mortality,
  between 40-50.
• Predators (shrews and beetles) can eat between
  60-70 of the pupae.
• Number of healthy pupae was determined by using
  inverted metal traps to estimate the number of
  emerging moths per m2.

15
(No Transcript)
16
Comparing the Strengths of Mortality Factors

• At each stage, the number of deaths and cause of
  death was recorded.
• The importance of each mortality factor (k) was
  estimated by calculating the amount that the
  factor reduced the population.
• Overwintering loss is the key factor.

17
(No Transcript)
18
Comparing the Strengths of Mortality Factors

• Other species and key factors.
• No key factor of overriding importance.

19
Comparing the Strengths of Mortality Factors

• Criticisms of key factor analysis
• Key factors cannot always be precisely linked to
  specific mortality agents.
• Intricate interactions between natural enemies,
  including hyperparasitoids, and such factors fail
  to show up in a key factor analysis.
• Populations can be influenced greatly by
  egg-bearing females that disperse into a
  population and that also never show up in key
  factor analysis.

20
Comparing the Strengths of Mortality Factors

• There are various other ways to look at life
  table data.

21
Comparing the Strengths of Mortality Factors

• Real mortality Mortality of a population
  compared with the population size at the
  beginning of the generation.
• Useful in comparing population factors within the
  same generation.
• Real mortality is generally greater for factors
  that operate early in the organism's life cycle,
  because more individuals are actually killed.

22
Comparing the Strengths of Mortality Factors

• Indispensable (or irreplaceable mortality) Part
  of the generational mortality which would not
  occur if a mortality factor was removed from the
  life system.

23
Comparing the Strengths of Mortality Factors

• Example Conservation of sea turtles
• 1000 eggs are laid.
• Predators (seagulls) eat 500 (50) hatchlings.
• Predators (fish predators) eat 400 of the
  remaining 500 turtles (80) before they become
  reproductive adults.
• 90 out of the remaining 100 reproductive adults
  (90)are lost in fishing nets.
• 10 surviving adults.

24
(No Transcript)
25
Comparing the Strengths of Mortality Factors

• Best method for conserving turtles
• Protecting turtle eggs, allowing 1000 turtles to
  make it to the sea where predators would still
  kill 80 of the turtles leaving 200.
• 90 of the 200 caught in nets leaving 20.
• Better to protect adults from fishing nets - 100
  surviving adults.

26
Comparing the Strengths of Mortality Factors

• Mortality-survivor ratio The increase in
  population that would have occurred if the factor
  in question had been absent.
• If the final population is multiplied by the
  mortality-survivor ratio, then the resulting
  value represents, in individuals, the
  indispensable mortality due to factor.

27
Density Dependence

• Predation, parasitism, competition, and abiotic
  factors can affect population densities.

28
Density Dependence

• Population densities can remain stable for long
  periods of time, therefore there must be factors
  that stabilize population density.
• Ex. Lake trout in Lake Michigan existed at the
  same densities for at least 20 years before the
  introduction of lampreys.

29
Density Dependence

• Difficulty in determining these density
  stabilizing factors.
• It is necessary to compare different mortality
  factors.
• It is appropriate to determine which factors act
  in a density dependent manner.
• The factor kills more of a population when
  densities are higher less when densities are
  lower.

30
Density Dependence

• Density dependence can be determined graphically.
• Positive slope (population density vs. percent
  mortality).
• Mortality increases with density.
• Mortality factor is acting in a density dependent
  manner.

31
Density Dependence

• Positive slope
• Density dependence.
• Ex. Overwintering moth.
• Factors that do not change with density are
  termed density independent.
• Ex. Bacteria infect 10 of the population,
  regardless of density.

32
(No Transcript)
33
Density Dependence

• Sources of mortality that decrease with
  increasing population size are termed inversely
  density dependent.
• A lion will take the same number of prey,
  regardless of density, because it is territorial.

34
Density Dependence

• Determining which factors act in a density
  dependent manner.
• Ex. Examining 58 species of insects (Stiling
  1988).
• The most important density dependent mortality
  factor was different for different species at
  different times.
• Generalizations are difficult to make.

35
Density Dependence

• Ex. Review of 51 populations of insects, 82 of
  large mammals, 36 of small mammals and birds
  (Sinclair, 1989).
• Insects showed a wide variety of causes of
  density dependence.
• Larger taxonomic groups.
• Food was important for large mammals.
• Space and social interactions were important to
  small mammals and birds.

36
(No Transcript)
37
Density Dependence

• r-selected species with very high reproductive
  rates and Type III survivorship curves (insects
  and fish) have early juvenile density-dependent
  mortality.

38
Density Dependence

• Species with intermediate reproductive rates and
  Type II survivorship curves (birds and small
  mammals) have late juvenile and prebreeding
  regulation.

39
Density Dependence

• K-selected species with low reproductive rates
  and Type I survivorship curves (large mammals)
  are at least partly regulated through changes in
  fertility.

40
Density Dependence

• Spreading the risk may be a good explanation for
  the apparent random patterns relating mortality
  to density.
• Ex. Parasitoid wasp oviposits on several solitary
  caterpillars rather than on caterpillars in a
  dense group.

41
Density Dependence

• No consensus on the frequency of density
  dependence in nature.
• When found, it offers control of population.
• No simple answer as to which factor will most
  likely to affect population densities.
• A complex of factors participates in the
  regulation of most organisms.

42
Metapopulations

• A Metapopulation is a series of small, separate
  populations that mutually affect one another.
• If one population goes extinct, others survive
  and supply colonizing individuals to reestablish
  the patch where the population went extinct.

43
Metapopulations

• Relevance to population biology populations
  could be maintained by a balance between local
  extinction and colonization. There is no mean or
  equilibrium, local extinction could occur at any
  time, and density dependence is irrelevant.

44
Metapopulations

• Persistence depends on factors affecting
  extinction and colonization.
• Interpatch distances.
• Species dispersal abilities.
• Number of patches in the metapopulation.

45
Metapopulations

• Harrisons (1991) review of empirical literature
  revealed few situations that fit classical
  description of a metapopulation.

46
(No Transcript)
47
Metapopulations

• More common were
• Core-satellite, source-sink, or mainland-island
  populations, in which persistence depended on the
  existence of one or more extinction resistant
  populations.

48
Metapopulations

• Patchy populations, in which dispersal between
  patches or populations was so high, that
  colonists always "rescued" populations from
  extinction.

49
Metapopulations

• Nonequilibrium metapopulations, in which local
  extinctions occurred in the course of species'
  overall regional decline.
• The failure of populations to disperse
  effectively eliminates a true metapopulation
  scenario.

50
Metapopulations

• Metapopulation study Bay checkerspot butterfly
  (Harrison et al. 1988).
• Metapopulation consisted of a population of 106
  adult butterflies on a 2,000 ha habitat (Jasper
  Ridge near Stanford University), and nine
  populations of 10 to 350 adult butterflies on
  patches of 1 to 250 ha.

51
Metapopulations

• Of 27 small patches that were suitable for
  populations to live in, only those close to the
  large patch were occupied.
• Difference could not be explained by the quality
  of habitats.

52
Metapopulations

• Distance effect appeared to indicate that the
  butterflies' capacity for dispersal was limited.
• Large patch was dominant source of colonists to
  the small patches.
• Persistence in this population was relatively
  unaffected by turnover in small populations.
• Small populations acted as sink populations.
• In 1996, the population disappeared from Jasper
  Ridge.

53
Metapopulations

• Metapopulation theory has had a long history.
• A study of a phytophagous mite as prey, and a
  predatory mite (Huffaker 1958, and Huffaker et al
  1963).
• Laboratory study, examining spatial heterogeneity
  using oranges and Vaseline barriers.

54
Metapopulations

• Prey was able to keep one step ahead of
  predators.
• At any one time, there was a mosaic of
• Unoccupied patches.
• Patches of prey and predators headed for
  extinction.
• Patches of thriving prey.
• The mosaic was capable of maintaining persistent
  populations of predators and prey.

55
(No Transcript)
56
Conceptual Models of Population Control

• Bottom-up factors
• Factors that act from the bottom of the food
  chain, for example food.
• Tropic-level concept or trophodynamics (Lindeman
  1942).
• Explained the height of the trophic pyramid by
  reference to a progressive attenuation of energy
  passing up through trophic levels.
• Based on the thermodynamic properties of energy
  transfer.

57
(No Transcript)
58
Conceptual Models of Population Control

• Top-down factors
• Factors which percolate down from the top of the
  food chain, for example natural enemies.

59
Conceptual Models of Population Control

• Hairston, Smith and Slobodkin's (1960) hypothesis
  (HSS) that because the earth appears green,
  herbivores must have little impact on plant
  abundance.
• Herbivores must be limited by predators rather
  than food supply.
• Plants are so common they endure severe
  competition.
• Natural enemies, by contrast are limited only by
  the availability of prey.

60
Conceptual Models of Population Control

• Ecosystem exploitation hypothesis (EEH Oksanen
  et al. 1981).
• Strength of various types of mortalities on
  different trophic levels varied with the type of
  system involved.
• As plant productivity increases, more herbivores
  are supported.

61
Conceptual Models of Population Control

• In the absence of carnivores, herbivores will be
  limited by plant resources.
• Primary productivity will reach a point where
  there are sufficient herbivores to support
  carnivores. This is the HSS scenario.

62
Conceptual Models of Population Control

• As productivity is increased further, secondary
  carnivores are supported.
• The importance of competition or predation on a
  given trophic level alternates as the number of
  trophic levels increase.

63
(No Transcript)
64
Conceptual Models of Population Control

• In theory, HSS and EEH offer plausible mechanisms
  to estimate under which circumstances mortality
  factors are most important.
• However, there is little data to support these
  top-down approach theories so far.

65
Conceptual Models of Population Control

• Environmental Stress hypothesis originated by
  Menge and Sutherland (1987).
• Postulates that the strength of various
  mortalities is governed by environmental stress.
• In stressful habitats, higher trophic levels have
  little effect because they are rare or absent,
  and plants are mainly affected by environmental
  stress.

66
Conceptual Models of Population Control

• In habitats of moderate stress, there would be a
  little herbivory, but not enough to affect
  population densities. Plant densities are higher
  and are affected by competition.
• In benign environments, there are many
  herbivores, and herbivory, not competition or
  environmental stress, controls plant abundance.

67
Summary

• Mortalities that perturb populations away from
  mean levels are termed key factors. Key factors
  can be identified through key factor analysis.
  There are many key factors for plants and
  animals, and no generalizations can be made as to
  which key factors are important.

68
Summary

• Factors that return populations to equilibrium
  are called density dependent factors. Few
  generalizations can be made as to which factors
  commonly act in a density dependent fashion.

69
Summary

• Sometimes density dependence does not occur
  because populations exist in interdependent
  groups (metapopulations). In these groups,
  dispersal is the key to understanding their
  dynamics.

70
Summary

• Several different models have been proposed to
  describe the types of mortality factors that
  should be most important in different systems.
• Trophodynamics, a bottom-up approach, suggests
  that populations are severely limited by their
  food supplies. The attenuation of energy
  severely limits the number of trophic levels.
• Top-down approaches, such as HSS and EEH have
  little support so far.

71
Summary

• Environmental stress hypothesis as an alternative
  hypothesis. Hypothesis postulates that biotic
  complexity decreases with increasing stress.