Demography and Population Dynamics

1. Demography and Population Dynamics Peter B. McEvoy Oregon State University

2. 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, λ • 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

3. 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

4. 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)

5. 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

6. Myiopharus doryphoraeDiptera: TachinidaeParasitoid of Colorado Potato Beetle Life Cycle

7. 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

8. 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.

9. 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

10. 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

11. Life Table for Colorado Potato Beetle major mortality factor is emigration of summer adults Tachinid parasite, Myiopharus (Dorpyphorophaga) doryphorae

12. 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?

13. 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 regression…but beware

14. 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

15. Key factor Major mortality factor Direct, overcompensating Density, dependent Summary of Life-table Analysis:major mortality factors and key factors

16. Step 5. Test for Density Dependence1. Strength2. Sign3. Time Delay (Population sizes for each time are serially linked)

17. Population regulation • Equilibrium: Line crosses x-axis where growth rate is zero • Direct DD: Negative slope Royama 1992

18. Step 5: Plotting K-value Against Density of Stage on Which the Mortality Acts nonlinear

19. Key factor Major mortality factor Direct, overcompensating Density, dependent Summary of Life-table Analysis:major mortality factors, key factors, and density-dependent factors

20. 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?

21. Another Case studyWinter MothOperophtera brumataGeometridae

22. 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)?

23. Step 7: Comparing Observed and Predicted Population Sizes

24. 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