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2 - Developments in statistical methods applied over four decades of research in the Taï Chimpanzee Project

Published online by Cambridge University Press:  25 November 2019

Christophe Boesch
Affiliation:
Max-Planck-Institut für Evolutionäre Anthropologie, Germany
Roman Wittig
Affiliation:
Max-Planck-Institut für Evolutionäre Anthropologie, Germany
Catherine Crockford
Affiliation:
Max-Planck-Institut für Evolutionäre Anthropologie, Germany
Linda Vigilant
Affiliation:
Max-Planck-Institut für Evolutionäre Anthropologie, Germany
Tobias Deschner
Affiliation:
Max-Planck-Institut für Evolutionäre Anthropologie, Germany
Fabian Leendertz
Affiliation:
Robert Koch-Institut, Germany
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Summary

The statistical methods used in projects conducted within the Taï Chimpanzee Project have changed considerably in the ongoing duration (about four decades) of this project. In particular, while initially classic tests focusing on statistical significance dominated the analyses, we now largely see the application of linear models. Here, I review this change and discuss the implications it has, and I also compare and contrast the classic statistical tests with contemporary analytical approaches. I argue that modelling not only allows for a better control of confounders and sources of non-independence, but also means to address more informative questions and, hence, reveals more informative answers. Finally, I discuss to what extent carefully designed models bridge the gap between the gold standard (randomized experiments) and observational studies.

Type
Chapter
Information
The Chimpanzees of the Taï Forest
40 Years of Research
, pp. 28 - 43
Publisher: Cambridge University Press
Print publication year: 2019

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References

Aarts, E., Dolan, C. V., Verhage, M. & van der Sluis, S. (2015). Multilevel analysis quantifies variation in the experimental effect while optimizing power and preventing false positives. BMC Neuroscience, 16, 94. https://doi.org/10.1186/s12868-015–0228–5Google Scholar
Adams, D. C. & Anthony, C. D. (1996). Using randomisation techniques to analyse behavioural data. Animal Behaviour, 51, 733738.Google Scholar
Aiken, L. S. & West, S. G. (1991). Multiple Regression: Testing and Interpreting Interactions. Newbury Park: Sage.Google Scholar
Baayen, R. H. (2008). Analyzing Linguistic Data. Cambridge: Cambridge University Press.Google Scholar
Barr, D. J., Levy, R., Scheepers, C. & Tily, H. J. (2013). Random effects structure for confirmatory hypothesis testing: Keep it maximal. Journal of Memory and Language, 68, 255278.Google Scholar
Bates, B., Mächler, M., Bolker, B. & Walker, S. (2015b). Fitting linear mixed-effects models using lme4. Journal of Statistical Software, 67, 148.Google Scholar
Bates, D., Kliegl, R., Vasishth, S. & Baayen, H. (2015a). Parsimonious mixed models. http://arxiv.org/abs/1506.04967v1.Google Scholar
Boesch, C. (1994). Cooperative hunting in wild chimpanzees. Animal Behaviour, 48, 653667.CrossRefGoogle Scholar
Boesch, C. & Boesch, H. (1981). Sex differences in the use of natural hammers by wild chimpanzees: A preliminary report. Journal of Human Evolution, 10, 585593.CrossRefGoogle Scholar
Boesch, C. & Boesch, H. (1984a). Mental map in wild chimpanzees: An analysis of hammer transports for nut cracking. Primates, 25, 160170.Google Scholar
Boesch, C. & Boesch, H. (1984b). Possible causes of sex differences in the use of natural hammers by wild chimpanzees. Journal of Human Evolution, 13, 41440.Google Scholar
Boesch, C. & Boesch, H. (1989). Hunting behavior of wild chimpanzees in the Taï National Park. American Journal of Physical Anthropology, 78, 547573.CrossRefGoogle ScholarPubMed
Bolker, B. M., Brooks, M. E., Clark, C. J., Geange, S. W., Poulsen, J. R., Stevens, M. H. H., et al. (2009). Generalized linear mixed models: A practical guide for ecology and evolution. Trends in Ecology and Evolution, 24, 127135.Google Scholar
Cohen, J. & Cohen, P. (1983). Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 2nd edn. Englewood Cliffs: Lawrence Erlbaum Associates Inc.Google Scholar
Gelman, A. & Hill, J. (2007). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge: Cambridge University Press.Google Scholar
Gomes, C. M. & Boesch, C. (2009). Wild chimpanzees exchange meat for sex on a long-term basis. PLoS ONE, 4, e5116.CrossRefGoogle ScholarPubMed
Gomes, C. M. & Boesch, C. (2011). Reciprocity and trades in wild West African chimpanzees. Behavioral Ecology and Sociobiology, 65, 21832196.Google Scholar
Hurlbert, S. H. (1984). Pseudoreplication and the design of ecological field experiments. Ecological Monographs, 54, 187211.CrossRefGoogle Scholar
Jaeger, T. F. (2008). Categorical data analysis: Away from ANOVAs (transformation or not) and towards logit mixed models. Journal of Memory and Language, 59, 434446.Google Scholar
Kroodsma, D. E. (1989). Suggested experimental designs for song playbacks. Animal Behaviour, 37, 600609.Google Scholar
Luncz, L. V., Mundry, R. & Boesch, C. (2012). Evidence for cultural differences between neighboring chimpanzee communities. Current Biology, 22, 922926.Google Scholar
Machlis, L., Dodd, P. W. D & Fentress, J. C. (1985). The pooling fallacy: Problems arising when individuals contribute more than one observation to the data set. Zeitschrift für Tierpsychologie, 68, 201214.Google Scholar
Matuschek, H., Kliegl, R., Vasishth, S., Baayen, H. & Bates, D. (2017). Balancing type I error and power in linear mixed models. Journal of Memory and Language, 94, 305315.Google Scholar
McCullagh, P. & Nelder, J. A. (1989). Generalized Linear Models. London: Chapman and Hall.CrossRefGoogle Scholar
McGill, B. (2012). Statistical machismo? Retrieved from https://dynamicecology.wordpress.com/2012/09/11/statistical-machismo/Google Scholar
Meddis, R. (1984). Statistics Using Ranks. Oxford: Blackwell.Google Scholar
Milinski, M. (1997). How to avoid seven deadly sins in the study of behavior. Advances in the Study of Behavior, 26, 159180.Google Scholar
Möbius, Y. B. (2008). Social and ecological influences on skill acquisition in wild chimpanzees, Pan troglodytes verus. Doctoral dissertation, Universität Leipzig, Leipzig.Google Scholar
Muller, M. N. & Lipson, S. F. (2003). Diurnal patterns of urinary steroid excretion in wild chimpanzees. American Journal of Primatology, 60, 161166.CrossRefGoogle ScholarPubMed
Mundry, R. & Oelze, V. M. (2016). Who is who matters – The effects of pseudoreplication in stable isotope analysis. American Journal of Primatology, 78, 10171030.Google Scholar
Nakagawa, S. & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: A practical guide for biologists. Biological Reviews, 82, 591605.Google Scholar
Nieuwenhuis, S., Forstmann, B. U. & Wagenmakers, E-J. (2011). Erroneous analyses of interactions in neuroscience: A problem of significance. Nature Neuroscience, 14, 11051107.Google Scholar
Quinn, G. P. & Keough, M. J. (2002). Experimental Designs and Data Analysis for Biologists. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
R Core Team. (2018). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing.Google Scholar
Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688701.Google Scholar
Rubin, D. B. (2008). Comment: The design and analysis of gold standard randomized experiments. Journal of the American Statistical Association, 103, 13501353.Google Scholar
Samuni, L., Preis, A., Deschner, T., Crockford, C. & Wittig, R. M. (2018). Reward of labor coordination and hunting success in wild chimpanzees. Communications Biology, 1, 138.Google Scholar
Samuni, L., Preis, A., Deschner, T., Wittig, R. M. & Crockford, C. (2019). Cortisol and oxytocin show independent activity during chimpanzee intergroup conflict. Psychoneuroendocrinology. https://doi.org/10.1016/j.psyneuen.2019.02.007CrossRefGoogle Scholar
Schielzeth, H. & Forstmeier, W. (2009). Conclusions beyond support: Overconfident estimates in mixed models. Behavioral Ecology, 20, 416420.Google Scholar
Siegel, S. & Castellan, N. J. (1988). Nonparametric Statistics for the Behavioral Sciences, 2nd edn. New York: McGraw-Hill.Google Scholar
Sirianni, G., Mundry, R. & Boesch, C. (2015). When to choose which tool: Multidimensional and conditional selection of nut-cracking hammers in wild chimpanzees. Animal Behaviour, 100, 152165.CrossRefGoogle Scholar
Sokal, R. R. & Rohlf, F. J. (1996). Introduction to Biostatistics. New York: Freeman and Company.Google Scholar
Sonnweber, R., Araya-Ajoy, Y. G., Behringer, V., Deschner, T., Tkaczynski, P., Fedurek, P., et al. (2018). Circadian rhythms of urinary cortisol levels vary between individuals in wild male chimpanzees: A reaction norm approach. Frontiers in Ecology and Evolution, 6, 85. https://doi.org/10.3389/fevo.2018.00085CrossRefGoogle Scholar
Stoehr, A. M. (1999). Are significance thresholds appropriate for the study of animal behaviour? Animal Behaviour, 57, F22F25.Google Scholar
Surbeck, M., Deschner, T., Weltring, A. & Hohmann, G. (2012). Social correlates of variation in urinary cortisol in wild male bonobos (Pan paniscus). Hormones and Behavior, 62, 2735.Google Scholar
Waller, B. M., Warmelink, L., Liebal, K., Micheletta, J. & Slocombe, K. E. (2013). Pseudoreplication: A widespread problem in primate communication research. Animal Behaviour, 86, 483488.Google Scholar
Wittig, R. M., Crockford, C., Weltring, A., Deschner, T. & Zuberbühler, K. (2015). Single aggressive interactions increase urinary glucocorticoid levels in wild male chimpanzees. PLoS ONE, 102, e0118695.Google Scholar
Zar, J. H. (1999). Biostatistical Analysis, 4th edn. Upper Saddle River: Prentice Hall.Google Scholar

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