I am broadly interested in evolutionary ecology, particularly how the environment and developmental processes affect evolution. Different forms of phenotypic plasticity are therefore reoccurring in my work. My previous work focused mainly on how genes affect (social) behavior and the other way around. I am now broadening my scope into the exciting fields of developmental plasticity and epigenetics. I am rounding-up a project on maternal effects in Daphnia and start to work on the importance of developmental bias in evolution.
The relative importance of development bias in evolution
developmental bias - genetics - epigenetics
In the traditional framework of evolution, mutations are regarded as the ultimate source of genetic variation and natural selection as the filtering process determining which variants are conserved for future generations and which are not. Effects of the environment as well as developmental processes on the way genotypes translate to phenotypes are regarded random and not affecting evolution. However, the developmental system organisms are subject to is also shaped by evolution, therefore the way genes and the environment affect the phenotype are coordinated and can be directional and even functional. The evolutionary trajectory a species takes is therefore affected by development. This developmental bias is often marginalized as anecdotal. In this project we will use meta-analysis to investigate how widespread developmental bias is, whether cryptic genetic variation — which is released in novel environments — is non-random and how phenotypic plasticity plays a role in these processes. This project is part of the Extended Evolutionary Synthesis research program.
Uller T, Feiner N, Radersma R, Jackson I, Rago A, 2019. Developmental plasticity and evolutionary explanations. Evolution & Development. : e12314.
Noble DWA*, Radersma R*, Uller T, 2019. Plastic responses to novel environments are biased towards phenotype dimensions with high additive genetic variation. Proceedings of the National Academy of Sciences of the United States of America. 116: 13452-13461.
Bayesian methods in ecology and evolution
social networks - genetics - epigenetics
The amounts of genomic and phenotypic data collected in ecology and evolution are surging. The collection of more and new types of data require new analytical techniques; generalized linear mixed models do not always offer the right tools and therefore more flexibility is needed. Among other benefits, Bayesian statistical techniques offer tremendous flexibility of model structure and the integration of uncertainty. I use Stan, a general-purpose Bayesian inference library and language, which utilizes novel algorithms to efficiently deal with highly dependent data structures. I develop and use novel model structures to analyze high dimensional dependent data structures to for instance infer extra-genetic or indirect genetic effects.
4/2018: Presentation. Inference in ecology and evolution beyond generalised linear mixed models. Presentation at Bayes@Lund 2018 conference. Presentation at a conference on applied Bayesian statistics, for researchers and professionals of all disiplines working with or interested in Bayesian methods.
The interplay between genes and social structure
genetics - social networks - life history
Currently I am working on genetics and social structure in tit species. A particular dynamic relationship is present between genes and social structure, because gene flow is strongly influenced by the social structure of a species while genes have the ability to dictate social behaviour. We collect data on the social structure of various passerines (Great tits, Blue tits, Marsh tits and Nuthatches) by registering visits to feeding stations at the individual level with radio frequency identification tags. We translate the social structure into social networks to quantify "social phenotypes". For Great tits a SNP-chip was developed and for two years most breeding pairs were genotyped. We also have pedigree data for both Great tits and Blue tits, which enables us to use "animal models" to infer genetic effects. Combining the social network data and the genotypes enables us to address questions regarding genetics and social structure. I focus on investigating the effect of social behaviour on the spatial distribution of genotypes, mate choice and genetics, quantitative genetics (both pedigree and marker based) of social behaviour and the evolutionary consequences of social behaviour. This project is part of a large project on the role of social processes in evolutionary ecology funded by an ERC grant awarded to Prof Ben Sheldon.
Radersma R, Garroway CJ, Santure AW, De Cauwer I, Farine DR, Slate S, Sheldon BC, 2017. Social and spatial effects on genetic variation between foraging flocks in a wild bird population. Molecular Ecology. 26: 5807-5819.
Kasper C, Vierbuchen M, Ernst U, Fischer S, Radersma R, Raulo A, Cunha-Saraiva F, Wu M, Mobley K, Taborsky B, 2017. Genetics and developmental biology of cooperation. Molecular Ecology. 26: 4364-4377.
Milligan ND, Radersma R, Cole EF, Sheldon BC, 2017. To graze or gorge: consistency and flexibility of individual foraging tactics in tits. Journal of Animal Ecology. 86: 826-836.
Crates RA, Firth JA, Farine DR, Garroway CJ, Kidd LR, Aplin LM, Radersma R, Milligan ND, Voelkl B, Culina A, Verhelst BL, Hinde CA, Sheldon BC, 2016. Individual variation in winter supplementary food consumption and its consequences for reproduction in wild birds. Journal of Avian Biology. 47: 678-689.
Aplin LM, Firth JA, Farine DR, Voelkl B, Crates RA, Culina A, Garroway CJ, Hinde CA, Kidd LR, Psorakis I, Milligan ND, Radersma R, Verhelst BL, Sheldon BC, 2015. Consistent individual differences in the social phenotypes of wild great tits, Parus major. Animal Behaviour. 108: 117-127.
Farine DR, Firth JA, Aplin LM, Crates RA, Culina A, Garroway CJ, Hinde CA, Kidd LR, Milligan ND, Psorakis I, Radersma R, Verhelst B, Voelkl B, Sheldon BC, 2015. The role of social and ecological processes in structuring animal populations: a case study from automated tracking of wild birds. Royal Society Open Science. 2: 150057.
Radersma R, Sheldon BC, 2015. A new permutation technique to explore and control for spatial autocorrelation. Methods in Ecology and Evolution. 6: 1026-1033.
Psorakis I, Voelkl B, Garroway CJ, Radersma R, Aplin LM, Crates RA, Culina A, Farine DR, Firth JA, Hinde CA, Kidd LR, Milligan ND, Roberts SJ, Verhelst B, Sheldon BC, 2015. Inferring social structure from temporal data. Behavioral Ecology and Sociobiology. 69: 857-866.
Sepil I, Radersma R, Santure AW, De Cauwer I, Slate J, Sheldon BC, 2015. No evidence for MHC class I based disassortative mating in a wild population of great tits. Journal of Evolutionary Biology. 28: 643-654.
Garroway CJ, Radersma R, Hinde CA, 2014. Perspectives on social network analyses of bird populations. In: Animal social networks (eds: Krause J, James R, Franks D and Croft DP). Oxford University Press: 171-183.
Garroway CJ*, Radersma R*, Sepil I, Santure AW, De Cauwer I, Slate J, Sheldon BC, 2013. Fine-scale genetic structure in a wild bird population: the role of limited dispersal and environmentally-based selection as causal factors. Evolution. 67: 3488-3500.