The study of evolutionary change, from allele frequencies to phenotypes, is our biggest obsession. We are particularly interested in life history evolution – what makes individuals change as they age, how and why individuals differ, and how these differences evolve. The topics below are all of current interest and we've published recent papers on all of them.
Honeybees and Hierarchical Populations
Social species have organizational structure, such as the colony-queen-worker lifecycle in honeybees. This “meta” structure affects how individual and collective traits evolve. A start on the demography of such systems is made in this paper. We’re working with Rob Page and Gro Amdam to extend this.
Al-Khafaji, K., Tuljapurkar, S., Carey, J., and Page, R. 2009. Hierarchical demography: a general approach with an application to honeybees. Ecology 90:556-566. PMID: 19323239
Phenotypic Dynamics and Evolution
Everyone knows that selection acts on phenotypes, and that a genotype-phenotype map connects phenotypic selection to the rates of change in allele frequencies. In practice, quantitative genetics uses heritabilities to make this connection via the breeders’ equation. This connection often doesn’t work in natural populations. Phenotypic selection varies over the life cycle, and parent-offspring relationships depend on the state of the parent. Working with Tim Coulson we have developed new and powerful tools (based on the Price equation) to describe phenotypic change and inter-generational transmission.
Ozgul, A., S. Tuljapurkar, T. G. Benton, J. M. Pemberton, T. H.Clutton-Brock and T. Coulson. 2009. The Dynamics of Phenotypic Change and the Shrinking Sheep of St. Kilda. Science 325, 464-467. PMID: 19574350
Coulson, T. and Tuljapurkar, S. 2008. The dynamics of a quantitative trait in an age-structured population living in a variable environment. American Naturalist 172: 599-612. PMID: 18840061
Pelletier, F., Clutton-Brock, T., Pemberton, J., Tuljapurkar, S., and Coulson, T., 2007. The Evolutionary Demography of Ecological Change: Linking Trait Variation and Population Growth. Science:315, 1571:1574. PMID: 17363672
Dynamic Heterogeneity and Life Histories
We have analyzed longitudinal data on many natural populations and found that individual life histories are not described by smooth age-patterns of survival and reproduction but in fact are stochastic sequences of transitions through stages (such as developmental stage, size, and levels of reproduction). We have discovered that there is tremendous variation among individuals in natural populations, have developed ways to measure this variation, and are studying the implications for the rate of progress of natural selection.
Tuljapurkar, Shripad; Steiner, Ulrich; Orzack, Steven. 2009. Dynamic Heterogeneity In Life Histories. Ecology Letters 12: 93-106. PMID: 19016825
Tuljapurkar, S., U. Steiner. 2009. Dynamic heterogeneity and life histories. Annals NY Acad Sci. (To appear)
Life History Evolution in Variable Environments
Changing environments pose a challenge to which life histories must adapt. The evolution of life histories reflects variability in fundamental ways. We have developed fundamental tools for the analysis of environmental variability and are applying them to understand life history evolution.
Tuljapurkar, Shripad, Gaillard, Jean-Michel, Coulson, T. 2009. From stochastic demography to life histories and back. Phil. Trans. Roy. Soc. B. 364:1499-1509. PMID: 19414465
Koons, D., Metcalf, C. J. E. and Tuljapurkar, S. 2008. Evolution of delayed reproduction in uncertain environments: a life history perspective. American Naturalist 172(6):797-805. PMID: 18959491
Orzack, S.H., Tuljapurkar, S., 2001. Reproductive effort in variable environments or environmental variation is for the birds. Ecology 82: 2659-2665.
Tuljapurkar, S. and Wiener, P., 2000. Escape in time: Stay young or age gracefully, Ecological Modeling, 133: 143-159.
Tuljapurkar, S. and Wiener, P. 1994. Migration in variable environments: exploring life history evolution using structured population models. Journal of Theoretical Biology 166: 75-90.
Van Gronendael, J., de Kroon, H., Kalisz, S. and Tuljapurkar, S. 1994. Loop analysis: Evaluating life history trade-offs in population projection matrices. Ecology 75: 2410-2415.
Tuljapurkar, S. and Istock, C. 1993. Environmental uncertainty and variable diapause. Theoretical Population Biology 43: 251-280.
Orzack, S., and Tuljapurkar, S. 1989. Population dynamics in variable environments. VII. Demography and evolution of iteroparity. American Naturalist 133: 901-923.
Evolution of Senescence
How and why does senescence evolve? This basic question has become especially relevant to human aging, given that human life spans have more than doubled in the past century. There are many “holes” in our understanding of the mechanisms and evolution of aging, empirical and theoretical. The first paper below is a broad overview of existing theory, especially its many limitations. The second paper below takes a novel look at how male-female differences could have produced the evolution of post-reproductive life in humans. The third does a comparative analysis of aging in fitness components using data on many species. The fourth paper below established tools to study the nature of senescence in stage-structured populations. The fifth used these tools to describe general patterns of senescence in plants.
Tuljapurkar, S. 1997. Theoretical Perspectives on the Evolution of Senescence. In: C. Finch and K. W. Wachter (Eds.), From Zeus to the Salmon:: Biodemography of Aging. National Academy Press, 1997. Tuljapurkar, S., Puleston, C.O., Gurven, M.D. 2007 Why Men Matter: Mating Patterns Drive Evolution of Human Lifespan. PLoS ONE 2(8): e785. doi:10.1371.
Jones, Owen, Gaillard, J-M., Tuljapurkar, S., et al 2008. Senescence rates are determined by ranking on the fast-slow life-history continuum. Ecology Letters 11: 664 673.
Tuljapurkar, S. and Horvitz, C. 2006. From Stage To Age In Variable Environments: Life Expectancy And Survivorship. Ecology 87: 1497-1509
Horvitz, C.C. and Tuljapurkar, S., 2008. State Dynamics, Period Survival And Mortality Plateaus. American Naturalist 172: 203-215.
Disturbance, growth and stage structure
Disturbance in environments is a fact of life in many environments, especially for tropical plants. We have shown that stage-structured demography provides a powerful way to qantify how disturbance shapes growth and ecological interactions.
Metcalf, C. J. E., Horvitz, C. C., Tuljapurkar S., and Clark, D. A. 2009. A time to grow and a time to die: a new way to analyze the dynamics of size, light, age and death of tropical trees. Ecology (in press).
Horvitz, C. C., Tuljapurkar, S. and Pascarella, J.B. 2006. Plant–Animal Interactions in Random Environments: Habitat-Stage Elasticity, Seed Predators, And Hurricanes. Ecology 86:3312-3322.
Changing Climates, Life Histories and Dynamics
Have life histories evolved in ways that “buffer” climate change? What will happen to populations as climatic averages and variances change?
Morris, W., Pfister, C., Tuljapurkar, S., Haridas, C., Boggs, C., Boyce, M., Bruna, E., Church, D., Coulson, T.,, Doak, D., Forsythe, S., Gaillard, J-M., Horvitz, C., Kalisz, S., Kendall, B., Knight, T., Lee, C. T., and Menges, E. 2008. Longevity can buffer plant and animal populations against changing climatic variability. Ecology:89(1) pp 19-25.
Al-Khafaji K., Tuljapurkar, S., Horvitz, C. and Koop, A., 2007. Detecting variability in demographic rates: Randomization with the Kullback-Leibler distance. Journal of Ecology 95:1370-1380.
Morris, W.F., Tuljapurkar, S., Haridas, C.V., Menges, E.S., Horvitz, C.C., and Pfister, C.A. 2006. Sensitivity of the population growth rate to demographic variability within and between phases of the disturbance cycle. Ecology Letters 9:1331-1341.
Boyce, M. S., Haridas, C., Lee, C., and Tuljapurkar, S, et al. 2006. The NCEAS Stochastic Demography Working Group. Demography in an increasingly variable world. TREE 21: 141-148.
Stochastic Population Dynamics
How do we describe and analyze the dynamics of populations whose vital rates vary stochastically (i.e., in some sense, randomly)? We have developed and applied a suite of theoretical methods that do the job and are widely used.
Haridas, C,. Tuljapurkar, S., and Coulson, T. 2009. Estimating stochastic elasticities directly from longitudinal data. Ecology Letters 12: 806-812.
Haridas, C. V. and Tuljapurkar, S., 2007. Time, transients and elasticity: Ecology Letters 10: 1143-1153.
Tuljapurkar, S. and Haridas, C.V. 2006. Temporal autocorrelation and stochastic population growth. Ecology Letters 9: 327-337
Tuljapurkar, S., Horvitz, C. and Pascarella, J. 2004. The Many Growth Rates and Elasticities of Populations in Random Environments: Correction. American Naturalist 164: 821-823.
Tuljapurkar, S. 1985. Population dynamics in variable environments. VI. Cyclical environments, Theoretical Population Biology 27: 1-17.
Tuljapurkar, S. 1984. Demography in Stochastic Environments. I. Exact Distributions of Age Structure, Journal of Mathematical Biology 19: 335-350.
Tuljapurkar, S. 1982. Population dynamics in variable environments. II. Correlated environments, sensitivity analysis and dynamics, Theoretical Population Biology 21: 114-140.
Tuljapurkar, S. 1982. Population dynamics in variable environments. III. Evolutionary dynamics of r-selection, Theoretical Population Biology 21: 141-165.
Tuljapurkar, S. and Orzack, S.H. 1980. Population dynamics in variable environments. I. Long-run growth rates and extinction, Theoretical Population Biology 18: 314-342
Density-dependence and population cycles
Density-dependence can be critical for many populations. We have developed a suite of density-dependent models for Human Prehistory. In the papers below, we’ve studied the effects of density in driving sustained (limit) cycles in a range of species.
Tuljapurkar, S., Boe, C. and Wachter, K. 1994. Nonlinear feedback dynamics in fisheries: analysis of the DeRiso-Schnute model. Canadian J. Fisheries Aquatic. Sci. 51: 1462-1473.
Possingham, H., Tuljapurkar, S., Roughgarden, J. and Wilks, M. 1994. Population cycling in space-limited organisms subject to density-dependent predation. American Naturalist 143: 563-582.
Tuljapurkar, S. 1987. Cycles in nonlinear population models. I. Renewal equations. Theoretical Population Biology 32: 26-41.