Abstract
In this essay, I consider the use of mathematical statistics in the study of biological systems in the field, using as case studies the work of Ruth Geyer Shaw and her colleagues at the University of Minnesota. To address practical issues, like how to enhance prairie restoration, and how to prepare for (and perhaps prevent) the effect of rapid climate change, she and her colleagues combine mathematical modeling and intensive data collection in the field. Using ANOVA and the more versatile approach of Aster Models, they show the effects of inbreeding (which follows from the fragmentation of prairie) to lower the fitness of plant populations. They study the genetic variance of plants in stable populations, which is important when that population must deal with changing conditions. And they study the consequences of rapid climate change: even good genetic variance and initial fitness may not save a given population from extinction. This leads to a new alliance of science with local, state, federal and global politics.
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References
Calude, Christian S., & Longo, Giuseppe. (2017). The deluge of spurious correlations in big data. Foundations of Science, 22(3), 595–612.
Cannon, A. R., et al. (2019). Stat2: Modeling with regression and ANOVA (Second Edition). New York: W. H. Freeman.
Doroege, Paula. (2019). Not by data alone: The promises and pitfalls of data analysis in understanding consciousness. European Review, 2019(27/3), 1–16.
Fisher, R. A. (1930). The genetic theory of natural selection. Oxford: Clarendon Press.
Fox Keller, E. (2002). Making sense of life. Cambridge: Harvard University Press.
Geyer, C. J. (2007). Wagenius, Stuart, and Shaw, Ruth G. Aster models for life history analysis. Biometrika, 114, 415–426.
Geyer, C. J. (2009). Likelihood interence in exponential families and directions of recession. Electronic Journal of Statistics, 3, 259–289.
Illari, Phyllis, & Russo, Federico. (2014). Causality: Philosophical theory meets scientific practice. Oxford: Oxford University Press.
Kitterson, Pamela M., Wagenius, Stuart, Nielsen, Reina, Qazi, Sanjive, Howe, Michael, Kiefer, Greter, & Shaw, Ruth G. (2015). How functional traits, herbivory, and genetic diversity interact in Echinacea: Implications for fragmented populations. Ecology, 96(7), 1877–1886.
Love, A. C. (2017). Building integrated explanatory models of complex biological phenomena: From Mill’s methods to a causal mosaic. In M. Massimim, J.-W. Romeijn, & G. Schurz (Eds.), EPSA15 Selected papers, from the 5th conference of the European philosophy of science association, Düsseldorf 2015 (pp. 221–232). Dordrecht: Springer.
Shaw, Ruth G., & Etterson, Julie R. (2012). Rapid climate change and the rate of adaptation: Insissght from experimental quantitative genetics. New Phytologist, 195, 752–765.
Shaw, Ruth G., Geyer, C. J., Wagenius, Stuart, Hangelbroek, H. H., & Etterson, Julie R. (2008). Unifying Life-history analysis for inference of fitness and population growth. The American Naturalist, 172, E35–E47.
Sheth, Seema Nayon, Kulbaba, Mason W., Pain, Rachel E., & Shaw, Ruth G. (2018). Expression of additive genetic variance for fitness in a population of partridge peas in two fields sites. Evolution International Journal of Organic Evolution, 72(11), 2537–2545.
Woodward, James. (2005). Making things happen. Oxford: Oxford University Press.
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Grosholz, E.R. On the plains and prairies of Minnesota: The role of mathematical statistics in biological explanation. Synthese 199, 5377–5393 (2021). https://doi.org/10.1007/s11229-021-03029-3
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DOI: https://doi.org/10.1007/s11229-021-03029-3