Research Interests

My general interests are in theoretical population biology and evolutionary ecology. I am especially interested in using dynamic models to identify and understand the mechanisms that drive ecological and evolutionary dynamics. I believe that useful theory must prove itself by leading to greater understanding of empirical results.

 If my research group had a motto, it might be (paraphrasing Benjamin Franklin): "Nothing is certain but predators and parasites". Nature abhors an uneaten meal, so the fundamental links in communities are between organisms and the other organisms that try to consume them, either all at once or slowly. Our other motto might be "Evolution changes everything". These are not unrelated. Becoming somebody else's dinner, or being turned into a factory for producing the next generation of Mycobacterium tuberculosis are very strong natural selection. It’s hard to imagine those things happening without evolutionary consequences. But exactly that thinking is embedded in most of the ecological theory underpinning environmental and natural resource management.

 My students develop their own research projects, many of which are only loosely tied to my own. See my “Notes to Prospective Students” page to learn about those.

Integral Projection Models for Populations with Continuous Structure (ongoing)

There are now many researchers applying and extending what Philip Dixon, Mike Easterling and I started in the late 1990's by arguing that plants don't just come in Small, Medium, and Large. This turns the matrix projection model into the Integral Projection Model (IPM). The push to get IPMs into the hands of empirical ecologists was launched by Mark Rees and Dylan Childs (U. Sheffield); if not for them, the start (Easterling et al. 2000) would also have been the end. More recently,

*  I, Peter Adler, Robin Snyder, and Giles Hooker had an NSF-funded project on stochastic IPMs, focusing on better approaches to demographic modeling when vital rates are affected by measured environmental variables. We are also working on new analytic approaches for connecting individual-level variability to environmental variability and trait variability within populations.

*  Peter, Robin, and I developed computational approaches to “modern coexistence theory” (the Chesson framework) that made it more easily applicable to more realistic models for population interactions, including IPMs.

*  Mark Rees and I are studying models for individuals cross-classified by size and a heritable quantitative trait. There are surprises. For one, at evolutionary steady-state you expect to see directional selection on traits affecting demographic rates, because equilibrium holds when selection mediated by demographic differences is equal, and opposite to, selection mediated by population structure. For another, quantitative trait models predictions about maintenance of genetic variation through fluctuating selection that are very different from the predictions of Adaptive Dynamics theory. The reason for this is still a mystery.

There is a lot to be done: eco-evolutionary models; models grounded in physiological mechanisms and constraints; making better use of imperfect data (mark-recapture studies, count data on unmarked individuals, inexact measurements); nonparametric estimates of demographic kernels; incorporating demographic stochasticity in a tractable way. 

 Selected Publications

*  M. Rees and S.P. Ellner. Why so variable: can genetic variance in flowering thresholds be maintained by fluctuating selection? American Naturalist, in press.

*  S.P. Ellner, R.E. Snyder, P.B. Adler, and G. J. Hooker 2019. An expanded Modern Coexistence Theory for empirical applications. Ecology Letters 22: 3-18. doi:10.1111/ele.13159.

*  P.B. Adler, A. Kleinhesselink, G. Hooker, J. B. Taylor, B. Teller, and S.P. Ellner. 2018. Weak interspecific interactions in a sagebrush steppe? Conflicting evidence from observations and experiments. Ecology 99: 1621–1632.

*  R.E. Snyder and S.P. Ellner. 2018. Pluck or luck: does trait variation or chance drive variation in lifetime reproductive success? American Naturalist 191: E90-E107.

*  R.E. Snyder and S.P. Ellner. 2016. We happy few: using structured population models to identify the decisive events in the lives of exceptional individuals. American Naturalist 188: E28-E45.

*  S.P. Ellner, D.Z. Childs and M. Rees. 2016. Data-driven Modelling of Structured Populations: A Practical Guide to the Integral Projection Model. Springer. 

*  M. Rees, D. Z. Childs and S. P. Ellner. 2014. Building integral projection models: a user's guide. Journal of Animal Ecology 83: 528–545.  doi: 10.1111/1365-2656.12178

*  S.P. Ellner and S.J. Schreiber. 2012. Temporally variable dispersal and demography can accelerate the spread of invading species. Theoretical Population Biology 82: 283-298.

*  P. B. Adler, S.P. Ellner and J. M. Levine. Coexistence of perennial plants: an embarrassment of niches. 2010. Ecology Letters 13: 1019-1029.

*  M. Rees and S.P. Ellner. 2009. Integral projection models for populations in temporally varying environments. Ecological Monographs 79: 575-594.

*  S.P. Ellner and M. Rees. 2007. Stochastic stable population growth in integral projection models. Journal of Mathematical Biology 54:227–256

*  S.P. Ellner and M. Rees. 2006. Integral projection models for species with complex demography. American Naturalist 167: 410-428.

*  D. Z. Childs, M. Rees, K.E. Rose, P.J. Grubb, and S.P. Ellner. 2004. Evolution of size-dependent flowering in a variable environment: construction and analysis of a stochastic integral projection model. Proceedings of the Royal Society of London Series B 271: 425-434. 

*  M. R. Easterling, S.P. Ellner, and P. Dixon. Size-specific sensitivity: applying a new structured population model. Ecology 81: 694-708.

 Pathogens of Pollinators (new) 

A new project funded by the NSF/NIH program on Ecology and Evolution of Infectious Diseases is focused on pathogens of bees that are spread, in whole or in part, through pathogen transmission from bees to flowers that they forage on for nectar, and from those flowers to other bees. Collaborators are Scott McArt (lead PI, Cornell Entomology), Chris Myers (Cornell Physics), Lynn Adler (U. Mass Amherst), Rebecca Irwin (NC State), and Quinn McFrederick (UC Irvine). In addition to its practical importance for maintaining pollinator populations, we view the project as a model system for spread of diseases with multiple vectors and hosts, and for testing trait-based approaches for parameterizing infectious disease models and designing disease control strategies.


*  L.S. Adler, K.M. Michaud, S.P. Ellner, S.H. McArt, P.C. Stevenson and R.E. Irwin. 2018. Disease where you dine: plant species and floral traits associated with pathogen transmission in bumble bees.  Ecology 99: 2535-2545.

 *  L.L. Truitt, S.H. McArt, A.H. Vaughn, and S.P. Ellner. 2019. Trait-based modeling of multi-host pathogen transmission: Plant-pollinator networks. American Naturalist.

 Rapid Evolution as an Ecological Process

For 15 years we (I and Nelson G. Hairston, Jr.) used a simple experimental system to study interactions between ecological and evolutionary dynamics: a chemostat (flow-through glass vessel) in which predators (rotifers, or rotifers and flagellates) consume prey (algae) who require a limiting nutrient (N supplied as nitrate) that is supplied in the inflowing culture medium. This was our introduction to how  rapid evolution could literally "change everything" about a classic and supposedly well-understood phenomena in ecology, predator-prey cycles.

 Our chemostat lab is now shut down, but our former students and postdocs, and their students and postdocs, continue to use our model system and others like it for further studies of how rapid evolution occurs, and what its consequences are.

 Our final project looked at rapid evolution in a natural system: the seasonal dynamics of Daphnia (water fleas) in Lake Onondaga. Our hypothesis was that seasonal changes in prey availability and other conditions create fluctuating selection, that maintains within-species genetic variability which affects the seasonal dynamics of the species. Thanks to grad student Lindsay Schaffner, we got fine-scale data on allele frequency changes at a set of microsatellite loci over the spring and summer, and lab comparisons that link the trends in genotype frequency to differences in juvenile growth rate on different food-types.

 Selected Publications

*  T. Hiltunen, N. G.Hairston Jr, G. Hooker, L.E. Jones and S. P. Ellner. 2014. A newly discovered role of evolution in previously published consumer-resource dynamics. Ecology Letters. Published online 12 May 2014. doi: 10.1111/ele.12291

*  T. Hiltunen, S.P. Ellner, G. Hooker, L.E. Jones, and N.G. Hairston, Jr.. 2014. Eco-Evolutionary Dynamics in a Three-Species Food Web with Intraguild Predation: Intriguingly Complex. Advances in Ecological Research 50: 41-73.

*  T. Hiltunen, L.E. Jones, S.P. Ellner, and N.G. Hairston, Jr. 2013. Temporal dynamics of a simple community with intraguild predation: an experimental test. Ecology 94, 773-779.

*  S.P. Ellner. 2013. Rapid evolution: from genes to communities, and back again? Functional Ecology 27: 1087-1099.

*  R. J. Tien and S.P. Ellner. 2012. Variable cost of prey defense and coevolution in predator–prey systems. Ecological Monographs 82: 491–504.

*  L. Becks, S.P. Ellner, L.E. Jones, and N.G. Hairston, Jr. 2012. The functional genomics of an eco-evolutionary feedback loop: linking gene expression, trait evolution, and community dynamics. Ecology Letters 15: 492-501.   

*  L. Becks, S. P. Ellner, L. E. Jones, and N. G. Hairston, Jr. 2010. Reduction of adaptive genetic diversity radically alters eco-evolutionary community dynamics. Ecology Letters 13: 989 – 997.

*  M.H. Cortez and S.P. Ellner. 2010. Understanding rapid evolution in predator-prey interactions using the theory of fast-slow dynamical systems. American Naturalist 176: E109–E127.

*  T. Yoshida, S. P. Ellner, L. E. Jones, B. J. M. Bohannan, R. E. Lenski, N. G. Hairston Jr. 2007. Cryptic population dynamics: rapid evolution masks trophic interactions. PloS Biology 5: e235. 

*  L.E. Jones and S.P. Ellner. 2007. Effects of rapid prey evolution on predator-prey cycles. Journal of Mathematical Biology 55:541–573.

*  J.R. Meyer, Stephen P. Ellner, Nelson G. Hairston, Jr., Laura E. Jones, and Takehito Yoshida. 2006. Prey evolution on the time scale of predator–prey dynamics revealed by allele-specific quantitative PCR. PNAS USA 103: 10690–10695.

*  N. G. Hairston, Jr., S. P. Ellner, M. A. Geber, T. Yoshida, and J. A. Fox. 2005. Rapid evolution and the convergence of ecological and evolutionary time. Ecology Letters 8: 1114-1127.

*  T.Y. Yoshida, N.G. Hairston, Jr., and S.P. Ellner. Evolutionary tradeoff between defence against grazing and competetive ability in a simple unicellular alga, Chlorella vulgaris. Proceedings of the Royal Society of London Series B 271: 1947 - 1953. 

*  T. Yoshida, L.E. Jones, S.P. Ellner, G.F. Fussmann, and N. G. Hairston, Jr. 2003. Rapid evolution drives ecological dynamics in a predator-prey system. Nature 424: 303-306.

*  G. F. Fussmann, S.P. Ellner, and N.G. Hairston, Jr. 2003. Evolution as a critical component of plankton dynamics. Proceedings of the Royal Society of London Series B 270: 1015-1022.

*  K.W. Shertzer and S.P. Ellner. 2002. State-dependent energy allocation in variable environments: life history evolution of a rotifer. Ecology 83: 2181–2193.

*  K. W. Shertzer, S.P. Ellner, G.F. Fussmann, and N.G. Hairston, Jr. . 2002. Predator-prey cycles in an aquatic microcosm: testing hypotheses of mechanism. Journal of Animal Ecology 71: 802–815.

*  P. Schliekelman and S.P. Ellner. 2001 Egg size evolution and energetic constraints on population dynamics. Theoretical Population Biology 60: 73-92.

*  G. F. Fussmann, S.P. Ellner, K.W. Shertzer, and N.G. Hairston, Jr. 2000. Crossing the Hopf bifurcation in a live predator-prey system. Science 290: 1358-1360.