GENERALIZED LINEAR MIXED MODELING OF COOPERATIVE BEHAVIORAL OUTCOMES UNDER CONTROLLED EXPERIMENTAL CONDITIONS
DOI:
https://doi.org/10.69980/mjn5xs75Keywords:
cooperative behavior, bumblebees, generalized linear mixed models, repeated measures, social interactionAbstract
In this study, the cooperative behaviour of buff-tailed bumblebees was examined in a controlled experimental setting with the aim of obtaining quantitative data on their behaviour and establishing a generalized linear mixed model of cooperative behaviour outcomes. The data set included block-pushing and tunnel experiments with repeated observations within bees, bee pairs, and colonies. Behavioral outcomes comprise waiting time, response time, cooperative pushing, partner-dependent actions, turning behaviour and orientation towards a social partner. For positively skewed continuous outcomes, gamma generalized linear mixed models were used and for binary models, binomial models were used with treatment, experimental phase, experiment, and release order as fixed effects and hierarchical grouping factors as random effects. The analyses indicated that first-released partnered bees waited significantly longer than controls and that partnered bees had a higher probability of delaying pushing until their partner pushed and a higher probability of orienting to their partner during the tunnel trials. An important Phase × Treatment interaction also showed that partnered bees were slower in the delay tests when compared to the controls. Random-effect estimates revealed significant intra-individual, intra-pair, and intra-colony variation and the hierarchical structure of the behavioral data. The chosen distributional assumptions and general GLMM model were supported by model diagnostics. The results indicate that social and experimental context has a large effect on cooperative responses and also show the utility of mixed-effects modeling for repeated, clustered and non-normally distributed behavioral data.
References
1. Austin MW, Tripodi AD, Strange JP, Dunlap AS. Bumble bees exhibit body size clines across an urban gradient despite low genetic differentiation. Scientific Reports. 2022;12(1):4166.
2. Franklin EL, Smith KE, Raine NE. How foraging preference and activity level of bumble bees contribute to colony flexibility under resource demand. Animal Behaviour. 2022;194:43–55.
3. Avargues-Weber A, Lachlan R, Chittka L. Bumblebee social learning can lead to suboptimal foraging choices. Animal behaviour. 2018;135:209–14.
4. Cholé H, Carcaud J, Mazeau H, Famié S, Arnold G, Sandoz JC. Social contact acts as appetitive reinforcement and supports associative learning in honeybees. Current Biology. 2019;29(8):1407–13.
5. Gallo V, Chittka L. Cognitive aspects of comb-building in the honeybee? Frontiers in psychology. 2018;9:900.
6. Hill L, Gérard M, Hildebrandt F, Baird E. Bumblebee cognitive abilities are robust to changes in colony size. Behav Ecol Sociobiol. 2023 Feb;77(2):25. doi:10.1007/s00265-023-03299-6
7. Chittka L, Rossi N. Social cognition in insects. Trends in Cognitive Sciences. 2022;26(7):578–92.
8. Becher MA, Twiston‐Davies G, Penny TD, Goulson D, Rotheray EL, Osborne JL. Bumble ‐ BEEHAVE : A systems model for exploring multifactorial causes of bumblebee decline at individual, colony, population and community level. Beggs J, editor. Journal of Applied Ecology. 2018 Nov;55(6):2790–801. doi:10.1111/1365-2664.13165
9. Brunet J, Jiang Q, Zhao Y, Thairu MW, Clayton MK. Bee species perform distinct foraging behaviors that are best described by different movement models. Scientific Reports. 2023;13(1):71.
10. Harrison XA, Donaldson L, Correa-Cano ME, Evans J, Fisher DN, Goodwin CE, et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ. 2018;6:e4794.
11. Gomes DG. Should I use fixed effects or random effects when I have fewer than five levels of a grouping factor in a mixed-effects model? PeerJ. 2022;10:e12794.
12. Gelman A, Hill J, Vehtari A. Regression and other stories. Analytical methods for social research [Internet]. 2020 [cited 2026 Sep 22]. Available from: https://www.cambridge.org/as/universitypress/subjects/statistics-probability/statistical-theory-and-methods/regression-and-other-stories
13. Ben-Shachar M, Lüdecke D, Makowski D. effectsize: Estimation of Effect Size Indices and Standardized Parameters. JOSS. 2020 Dec 23;5(56):2815. doi:10.21105/joss.02815
14. Ghisbain G, Chittka L, Michez D. Bumblebees. Current Biology. 2025;35(6):R206–11.
15. Loukola O, Antinoja A, Mäkelä K, Arppi J, Peng F, Solvi C. Data from: Evidence for socially influenced and potentially actively coordinated cooperation by bumblebees [Internet]. 2024 Mar 29. doi:10.5061/dryad.gtht76htr
16. Lüdecke D, Ben-Shachar MS, Patil I, Waggoner P, Makowski D. performance: An R package for assessment, comparison and testing of statistical models. Journal of open source software [Internet]. 2021 [cited 2026 Sep 22];6(60). Available from: https://pure.mpg.de/rest/items/item_3316363/component/file_3316364/content
17. Melis AP, Raihani NJ. The cognitive challenges of cooperation in human and nonhuman animals. Nature Reviews Psychology. 2023;2(9):523–36.
18. Li L, Su S, Perry CJ, Elphick MR, Chittka L, Søvik E. Large-scale transcriptome changes in the process of long-term visual memory formation in the bumblebee, Bombus terrestris. Scientific reports. 2018;8(1):534.
19. Kembro JM, Lihoreau M, Garriga J, Raposo EP, Bartumeus F. Bumblebees learn foraging routes through exploitation–exploration cycles. Journal of the Royal Society Interface. 2019;16(156):20190103.
20. Martin CD, Fountain MT, Brown MJ. Bumblebee olfactory learning affected by task allocation but not by a trypanosome parasite. Scientific reports. 2018;8(1):5809.
21. Oberpriller J, De Souza Leite M, Pichler M. Fixed or random? On the reliability of mixed‐effects models for a small number of levels in grouping variables. Ecology and Evolution. 2022 Jul;12(7):e9062. doi:10.1002/ece3.9062
22. Schielzeth H, Dingemanse NJ, Nakagawa S, Westneat DF, Allegue H, Teplitsky C, et al. Robustness of linear mixed‐effects models to violations of distributional assumptions. Sutherland C, editor. Methods Ecol Evol. 2020 Sep;11(9):1141–52. doi:10.1111/2041-210X.13434
23. Linander N, Dacke M, Baird E, Hempel De Ibarra N. The role of spatial texture in visual control of bumblebee learning flights. J Comp Physiol A. 2018 Aug;204(8):737–45. doi:10.1007/s00359-018-1274-0
24. Muth F, Francis JS, Leonard AS. Modality-specific impairment of learning by a neonicotinoid pesticide. Biology letters. 2019;15(7):20190359.
25. Kaakinen K, Ramula S, Loukola OJ, Helander M. Effects of glyphosate and glyphosate‐based herbicide on learning and memory of the buff‐tailed bumblebee ( Bombus terrestris ). Entomologia Exp Applicata. 2024 Apr;172(4):324–33. doi:10.1111/eea.13418
26. Lüdecke D, Patil I, Ben-Shachar M, Wiernik B, Waggoner P, Makowski D. see: An R Package for Visualizing Statistical Models. JOSS. 2021 Aug 6;6(64):3393. doi:10.21105/joss.03393
