Pat White
Research
I became interested in this field when I was researching the impact of land management on productivity of farmland multi-brooded songbirds with the Centre for Agri-environmental Research (University of Reading) and Game & Wildlife Conservation Trust). Wanting to extract population-level predictions from variation in nest success I started exploring use of individual-based re-nesting models, inspired by work of Larkin Powell and others. However, I found that it was difficult to know what assumptions to make about the probability that a bird would make another nesting attempt after a success or failure, which is important in species that can have several nesting attempts.
Initially we developed a novel approach to estimate biologically plausible re-nesting probability functions from unmarked birds (White et al. 2013) which could be used in individual-based re-nesting models. I initially developed the individual-based model in MS Excel and Visual Basic, and this model was used to investigate the impact of controlling invasive rats on the productivity of a critically endangered endemic passerine 0n Mauritius (Maggs et al. 2015).
In 2018 I attended a Summer School on Individual and Agent-based Modelling at the Technische Universität Dresden under Uta Berger and Steve Railsback. I learned the specialist individual-based modelling language NetLogo and with colleagues at the school I also co-authored a paper on pattern-oriented modelling in individual-based models (Gallagher et al. 2021). I used these techniques to migrate the re-nesting model to Netlogo where it has an easier user-interface and is more customizable to different species and contexts. It can be accessed open-source and with instructions here.
Using this model, a dataset of individually marked breeding passerines provided by Martin Weggler, and a pattern-oriented modelling approach, we were able to demonstrate that both using an individual-based re-nesting model, and the choice of re-nesting probability function in that model, will modify population-level predictions, which has consequences for studying the impact of environmental or anthropogenic change on bird populations (White et al. 2023).
Subsequently, I worked with Dr Malcom Burgess at RSPB / University of Exeter to apply this model to look at demographic drivers of spotted flycatcher Muscicapa stiata declines in England (Burgess et al. 2025) and we are looking to apply it to study threatened black-tailed godwit Limosa limosa populations. If you have a study species or system where you think that an individual-based re-nesting model might help you understand variation productivity and demography of your study species, please get in touch.
Outputs
Gallagher, C. A., Chudzinska, M., Larsen‐Gray, A., Pollock, C. J., Sells, S. N., White, P. J. C., & Berger, U. (2021). From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models. Biological Reviews https://doi.org/10.1111/brv.12729
Maggs, G., Nicoll, M., Zuël, N., White, P. J. C., Winfield, E., Poongavanan, S., …Norris, K. (2015). Rattus management is essential for population persistence in a critically endangered passerine: combining small-scale field experiments and population modelling. Biological Conservation https://doi.org/10.1016/j.biocon.2015.06.039
White, P. J. C., Stoate, C., Szczur, J., Aebischer, N.J. & Norris K. (2014). A method for deriving time-variable avian re-nesting probability functions for use in seasonal productivity models. In Proceedings of the BOU’s 2013 AnnualConference – From populations to policy impact: avian demography in a changing world http://bou.org.uk/avian-demography-abstracts-2012-12-21.pdf
White, P. J. C., Stoate, C., Aebischer, N.J., Szczur, J., Ferrer, L. & Norris K. (2023) Choice of model and re-nesting probability function influences behaviour of avian seasonal productivity models and their demographic predictions. Ibis https://doi.org/10.1111/ibi.13267
Names in bold are current or former staff or students working in the lab
Pat White | Method(Ec)ology 