Demography and Population Dynamics

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Demography and Population Dynamics

• Peter B. McEvoy
• Oregon State University

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Outline

• Construct a model of intergeneration change
• Develop a sampling program and estimate number of
  individuals passing through each stage in life
  cycle
• Construct a life table, calculate and interpret
  lx, mx, rm, Ro, ?
• Compare k-factor analysis and Life Table Response
  Experiments (LTREs) as ways to discover factors
  causing population change
• Distinguish major mortality factors, key factors,
  density-regulating factors
• Distinguish direct density dependence
  (over-compensating, perfectly compensating, or
  under-compensating), inverse density dependence,
  delayed density dependence, density independence
• Critically evaluate methods for assessing role of
  density-dependent and density-independent factors
  in population dynamics

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Changes in Species Abundance

• External and internal causes. Fluctuations in
  abundance may have both external and internal
  causes
• Density dependent (DD) and density independent
  (DI) factors. All abundances reflect both
  density-dependent and density-independent
  factors, but the relative importance and
  frequency of action of the two can vary greatly
• Evidence of DD or DI. Disagreement persists about
  the relative strength and frequency of DD or DI,
  and on the reliability of techniques for
  detecting DD. Requires data that is structured
  in space as well as time
• Scales of observation. Description of density
  relations depends on the scale of observation.
  Close to equilibrium ? DI, farther from
  equilibrium ?DD

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Case study of k-factor analysis

• Focal species. Colorado potato beetle
  Leptinotarsa decemlineata (Col Chrysomelidae)
• Investigator. Harcourt (1964, 1971) reviewed in
  Begon et al. 1996
• Aims of exercise
• Distinction between determination and regulation
  of insect abundance
• Modeling changes in abundance in terms of changes
  in so-called viatal rates (age-specific
  survivorship, fecundity, and migration)

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Colorado Potato BeetleLeptinotarsa decemlineata
(Coleoptera Chrysomelidae)

• Life History
• Univoltine in Ontario
• Spring adults emerge from hibernacula in mid
  June
• Oviposition peaks in early July
• 4 Larval instars and Pupa
• Emergence of summer adults from puparia

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Myiopharus doryphorae Diptera
TachinidaeParasitoid of Colorado Potato Beetle
Life Cycle
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Classical Approach Key Factor Analysis

• Study life history and develop methods of census
  for each stage
• Construct a life table that is as complete as
  possible, expressing the "killing power" of
  mortality factors as k-values
• Accumulate many life tables
• Plot generation curves and mortalities
• Assess the key-factors which make the biggest
  contribution to change in generation mortality
• Determine the relationship of component
  mortalities to density
• Follow up with intensive studies of key factors
• Make predictions using the model

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Concept Alert!

• Major mortality factor makes a large
  contribution to mortality within a generation
  (large k)
• Key factors contribute to changes in abundance
  between generations (component k most correlated
  with generation Ktotal)
• Density-regulating factors are those k-values
  that increase with density of the stage on which
  they act.
• Population regulation. A regulated population
  is one that tends to return to equilibrium
  density or cycle when perturbed from this level
  or cycle. Precise DD requires that DD factors
  not be too strong or too weak.

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Step 1 Study Life History and Develop Methods of
Census for Each Stage

• Life History
• Adult emergence from hibernacula
• Oviposition
• Larvae and Pupae
• Emergence of adults from puparia
• Sampling decisions
• Subdivision of the habitat
• Selection of the sampling unit
• Number of samples
• Placement of samples
• Timing of sampling

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Step 2 Construct a Life Table That Is As
Complete As Possiblerefer to handout

• Designate stage intervals how?
• Estimate mortality assuming factors act
  sequentially, not simultaneously what are the
  implications?
• Estimate k-values as difference between
  logarithms of the population before and after
  mortality acts

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Life Table for Colorado Potato Beetle
major mortality factor is emigration of summer
adults
Tachinid parasite, Myiopharus (Dorpyphorophaga)
doryphorae
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Step 4 Accumulate Many Life Tables

• Major mortality factors make a large
  contribution to generation mortality
• In this example, major mortality factor is
  emigration of summer adults
• How can emigration regulate a local population?
  What is the fate of emigrating insects? What are
  the implications for an ensemble of local
  populations in a region?

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Step 4 Plot Generation Curves and Mortalities

• Key factors contribute to changes in abundance
  from generation to generation
• Assess key factor by inspection
• Assess key factor by regressionbut beware

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Step 4 Plot Generation Curves and Mortalities
k6 adult emigration k3 larval starvation k4
parasitism
ktotal
k6
k3
k4
Site 1 Site 2 Site 3 Year
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Summary of Life-table Analysismajor mortality
factors and key factors
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Step 5. Test for Density Dependence1.
Strength2. Sign3. Time Delay
(Population sizes for each time are serially
linked)
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Population regulation

• Equilibrium Line crosses x-axis where growth
  rate is zero
• Direct DD Negative slope

Royama 1992
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Step 5 Plotting K-value Against Density of Stage
on Which the Mortality Acts
nonlinear
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Summary of Life-table Analysismajor mortality
factors, key factors, and density-dependent
factors
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Problem of Scale

• If local population is regulated by
  density-dependent emigration, what is the fate of
  emigrants?
• Do they form new populations or augment existing
  ones?
• How is the ensemble of populations in the region
  (Metapopulation) regulated?

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Another Case study Winter MothOperophtera
brumataGeometridae
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Step 6 Follow up With Intensive Study of Key
Factors (winter moth)

• How often does it happen that the key factor is
  the least well known, least managable? Winter
  disappearance (WM)? Adult emigration (CPB)?

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Step 7 Comparing Observed and Predicted
Population Sizes
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Critique of Key Factor Analysis

• May fail to detect factors acting irregularly in
  time
• Factors are actually transitions in the life
  cycle (not abiotic or biotic factors)
• Limited range of organisms to which technique can
  be applied (univoltine insects with
  non-overlapping generations)
• May fail to detect DD when densities are variable
  in space and time