Lecturer: Franjo Weissing
Fields: Ecology & Evolution, Behavioural Biology
Content
Biological organisms have to cope with ever-changing environmental conditions. They have been ‘designed’ for this task in a long evolutionary history, but how evolution by natural selection has achieved this is far from clear. Two properties are crucial for long-term survival in a changing world: ‘robustness’ (the ability to build the same phenotype under very different conditions) and ‘evolvability’ (the ability to rapidly respond to changing conditions by adaptive evolution). The conundrum is that these properties seem to be contradictory: doesn’t a robust design impede evolvability, and doesn’t evolvability require a flexible design? A second problem is that ‘evolutionary design’ is fundamentally different from the ‘engineered design’. While an engineer has foresight, adaptive evolution resembles a ‘blind watchmaker’ (Dawkins 1986) in that it is driven by short-term selection pressures. We all know that following short-term incentives often has negative implications in the longer term. How, then, can long-term properties like robustness and evolvability be shaped by a myopic process like natural selection?
Questions like these will be addressed in four sessions. The first two sessions will illustrate the dynamic complexity of apparently simple ecological and evolutionary systems. We will see that such systems can be ‘fundamentally unpredictable’ and that adaptive evolution can, in principle, drive a population to extinction (‘evolutionary suicide’). In the last two sessions, we will sketch a new way of evolutionary thinking that may (partly) resolve issues like these. We will see that the evolution of ‘responsive strategies’ (strategies that respond to the local environmental conditions) is fundamentally different from the evolution of non-responsive strategies. The reciprocal causality inherent to these strategies speeds up evolution by orders of magnitude and leads to quite different evolutionary outcomes. In biological organisms, responsive strategies are often implemented via regulatory networks (e.g., gene regulation networks or neural networks). It turns out that the evolution of such networks shares various properties with learning (by machines or intelligent agents).
Literature
- Session 1: Out of equilibrium
- Huisman, J. & Weissing, F.J. 1999. Biodiversity of plankton by species oscillations and chaos. Nature 402: 407-410, doi: 10.1038/46540.
- Huisman, J. & Weissing, F.J. 2001. Fundamental unpredictability in multispecies competition. American Naturalist 157: 488-494, doi: 10.1086/319929.
- Session 2: Conflict and cooperation
- Baldauf, S.A., Engqvist, L. & Weissing, F.J. 2014. Diversifying evolution of competitiveness. Nature Communications 5: 5233, doi: 10.1038/ncomms6233.
- Long, X. & Weissing, F.J. 2023. Transient polymorphisms in parental care strategies drive divergence of sex roles. Nature Communications 14: 6805, doi: 10.1038/s41467-023-42607-6.
- Session 3: The reciprocal causality of responsive strategies
- Quiñones, A.E., Van Doorn, G.S., Pen, I., Weissing, F.J. & Taborsky, M. 2016. Negotiation and appeasement can be more effective drivers of sociality than kin selection. Phil. Trans. R. Soc. B 371:20150089, doi:10.1098/rstb.2015.0089.
- Netz, C., Hildenbrandt, H. & Weissing, F.J. 2022. Complex eco-evolutionary dynamics induced by the coevolution of predator-prey movement strategies. Evol. Ecol. 36: 1-17, doi: 10.1007/s10682-021-10140-x.
- Gupte, P.R., Albery, G.F., Gismann, J., Sweeny, A.R. & Weissing, F.J. 2023. Novel pathogen introduction triggers rapid evolution in animal social movement strategies. eLife, 12: e81805, doi: 10.7554/eLife.81805.
- Session 4: Robustness and evolvability
- Wagner, A. 2008. Robustness and evolvability: a paradox resolved. Proc. Royal Society B 275: 91-100, doi: 10.1098.rspb.2007.1137.
- Watson, R.A. & Szathmáry, E. 2016. How can evolution learn? Trends in Ecology & Evolution 31: 147-157, doi: 10.1016/j.tree.2015.11.009
- Van Gestel, J. & Weissing, F.J. 2016. Regulatory mechanisms link phenotypic plasticity to evolvability. Scientific Reports 6:24524, doi: 10.1038/srep24524.
Lecturer
After studying mathematics and biology at the University of Bielefeld, I did my PhD work at the Centre for Interdisciplinary Studies (ZiF Bielefeld), where I co-organised the research year ‘Game Theory in the Behavioural Sciences’. Together with the later Nobel laureate Elinor Ostrom, I pioneered the introduction of ‘evolutionary thinking’ into the political sciences. In 1989, I moved to the University of Groningen (Netherlands), where I tackled a wide variety of eco-evolutionary questions with a combined theoretical and empirical approach. My area of expertise lies in the development and analysis of mathematical and computational models. Our emphasis is on ‘mechanistic models of intermediate complexity’ that lead to insights and predictions that can be tested in close collaboration with empirical biologists. In my research, I strive to understand the emergence of diversity at all levels of biological organisation (e.g., differences between cells, individuals, the sexes, groups, species, and ecosystems) and the implications of diversity for the evolution and functioning of biological systems. In the last ten years, I have broadened my research again to other disciplines. As a Distinguished Lorentz Fellow, I spent a research year at the Institute of Advanced Study in the Humanities and Social Sciences in Amsterdam, where I critically investigated the foundations of cultural evolution theory. I became convinced that new approaches are needed that do justice to the differences between genetic and cultural evolution. To this end, we are currently working on a new framework for the evolution of individual and social learning, which is based on the evolution of neural networks.
Affiliation: University of Groningen
Homepage: https://research.rug.nl/en/organisations/weissing-group-theoretical-biology