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A Dynamic Individual-Based Model for High-Resolution Ant Interactions

  • Nathan B. WikleEmail author
  • Ephraim M. Hanks
  • David P. Hughes
Article
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Abstract

Ant feeding interactions (i.e., trophallaxis events) are thought to regulate the flow of nutrients and disease within a colony. Consequently, there is great interest in learning which environmental and behavioral factors drive ant trophallaxis. In this paper, we analyze ant trophallaxis behavior in a colony of 73 carpenter ants, observed at 1-s intervals over a period of 4 h. The data represent repeated observations from a dynamic contact network; however, traditional statistical analyses of network models are ill-suited for data observed at such high temporal resolution. We present a model for high-resolution longitudinal network data, where the network is assumed to be a time inhomogeneous, continuous-time Markov chain, with transition rates modeled as a function of time-varying individual and pairwise biological covariates. In particular, the high temporal resolution of the data leads to a tractable likelihood function, and likelihood-based inference procedures are utilized to explain which biological factors drive contact. Our results reveal how differences in ant social castes and individual behaviors, such as ant speed and activity levels, influence patterns of ant trophallaxis in the colony. Supplementary materials accompanying this paper appear online.

Keywords

Animal contact network Ant trophallaxis Camponotus pennsylvanicus Longitudinal network data Markov process 

Notes

Acknowledgements

Funding was provided by NSF EEID 1414296 and NIH GM 116927-01. We are grateful to Andreas Modlmeier and the many undergraduates in the Hughes Lab who tracked the ants. We thank Roland Langrock, Christen H. Fleming, and one anonymous reviewer for their helpful suggestions.

Supplementary material

13253_2019_363_MOESM1_ESM.pdf (266 kb)
Supplementary material 1 (pdf 265 KB)

References

  1. Bansal, S., Grenfell, B. T. and Meyers, L. A. (2007), When individual behaviour matters: Homogeneous and network models in epidemiology. Journal of the Royal Society Interface, 4(16), 879–891. ISSN 17425689.  https://doi.org/10.1098/rsif.2007.1100.
  2. Bartley, M., Hanks, E. and Hughes, D. (2018), A Bayesian penalized hidden Markov model for ant interactions. ArXiv e-prints, June.Google Scholar
  3. Billingsley, P. (1995), Probability and Measure. John Wiley and Sons, New York, NY.zbMATHGoogle Scholar
  4. Eames, K., Bansal, S., Frost, S. and Riley, S. (2014), Six challenges in measuring contact networks for use in modelling. Epidemics, 10, 72–77. ISSN 18780067.  https://doi.org/10.1016/j.epidem.2014.08.006.
  5. Farine, D. R., Strandburg-Peshkin, A., Berger-Wolf, T., Ziebart, B., Brugere, I., Li, J. and Crofoot, M. C. (2016), Both nearest neighbours and long-term affiliates predict individual locations during collective movement in wild baboons. Scientific Reports, 6.Google Scholar
  6. Ferguson, T. S. (1996), A Course in Large Sample Theory. Routledge, New York.CrossRefzbMATHGoogle Scholar
  7. Fewell, J. (2003), Social insect networks. Science, 301(5641), 1867–1870.CrossRefGoogle Scholar
  8. Gernat, T., Rao, V. D., Middendorf, M., Dankowicz, H., Goldenfeld, N. and Robinson, G. E. (2018), Automated monitoring of behavior reveals bursty interaction patterns and rapid spreading dynamics in honeybee social networks. Proceedings of the National Academy of Sciences, 115(7), 1433–1438.CrossRefGoogle Scholar
  9. Gordon, D. M. (2014), The ecology of collective behavior. PLoS Biology, 12(3), 1–4. ISSN 15457885.  https://doi.org/10.1371/journal.pbio.1001805.
  10. Greenwald, E., Segre, E. and Feinerman, O. (2015), Ant trophallactic networks: Simultaneous measurement of interaction patterns and food dissemination. Scientific Reports, 5(July):1–11. ISSN 20452322.  https://doi.org/10.1038/srep12496.
  11. Greenwald, E., Baltiansky, L. and Feinerman, O. (2018), Individual crop loads provide local control for collective food intake in ant colonies. eLife, 7, 1–22.  https://doi.org/10.7554/eLife.31730.
  12. Groendyke, C., Welch, D. and Hunter, D. R. (2011), Bayesian inference for contact networks given epidemic data. Scandinavian Journal of Statistics, 38(3), 600–616. ISSN 03036898.  https://doi.org/10.1111/j.1467-9469.2010.00721.x.
  13. Hamilton, C., Lejeune, B. T. and Rosengaus, R. B. (2011), Trophallaxis and prophylaxis: Social immunity in the carpenter ant Camponotus pennsylvanicus. Biology Letters, 7(1), 89–92. ISSN 1744957X.  https://doi.org/10.1098/rsbl.2010.0466.
  14. Holland, P. W. and Leinhardt, S. (1977), A dynamic model for social networks. The Journal of Mathematical Sociology, 5(1), 5–20.  https://doi.org/10.1080/0022250X.1977.9989862.MathSciNetCrossRefzbMATHGoogle Scholar
  15. Kays, R., Crofoot, M. C., Jetz, W. and Wikelski, M. (2015), Terrestrial animal tracking as an eye on life and planet. Science, 348(6240). ISSN 0036-8075.Google Scholar
  16. Krause, J., Krause, S., Arlinghaus, R., Psorakis, I., Roberts, S. and Rutz, C. (2013), Reality mining of animal social systems. Trends in Ecology and Evolution, 28(9), 541–551. ISSN 0169-5347.Google Scholar
  17. Krivitsky, P. N. and Handcock, M. S. (2014), A separable model for dynamic networks. Journal of the Royal Statistical Society. Series B: Statistical Methodology, 76(1), 29–46. ISSN 13697412.  https://doi.org/10.1111/rssb.12014.
  18. Leboeuf, A. C., Waridel, P., Brent, C. S., Gonçalves, A. N., Menin, L., Ortiz, D., Riba-Grognuz, O., Koto, A., Soares, Z. G., Privman, E., Miska, E. A., Benton, R. and Keller, L. (2016), Oral transfer of chemical cues, growth proteins and hormones in social insects. eLife, 5.Google Scholar
  19. Matias, C. and Miele, V. (2017), Statistical clustering of temporal networks through a dynamic stochastic block model. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(4), 1119–1141.MathSciNetCrossRefzbMATHGoogle Scholar
  20. Mersch, D. P., Crespi, A. and Keller, L. (2013), Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science, 340(6136), 1090–1093.  https://doi.org/10.1126/science.1234316.CrossRefGoogle Scholar
  21. Miele, V. and Matias, C. (2017), Revealing the hidden structure of dynamic ecological networks. Royal Society open science, 4(6).Google Scholar
  22. Oster, G. F. and Wilson, E. O. (1978), Caste and ecology in the social insects. Princeton University Press, Princeton, N.J.Google Scholar
  23. Pinter-Wollman, N., Wollman, R., Guetz, A., Holmes, S. and Gordon, D. M. (2011), The effect of individual variation on the structure and function of interaction networks in harvester ants. Journal of The Royal Society Interface, 8(64), 1562–1573. ISSN 1742-5689.  https://doi.org/10.1098/rsif.2011.0059.
  24. Pinter-Wollman, N., Bala, A., Merrell, A., Queirolo, J., Stumpe, M. C., Holmes, S. and Gordon, D. M. (2013), Harvester ants use interactions to regulate forager activation and availability. Animal Behaviour, 86(1), 197–207. ISSN 00033472.  https://doi.org/10.1016/j.anbehav.2013.05.012.
  25. Quevillon, L. E., Hanks, E. M., Bansal, S. and Hughes, D. P. (2015), Social, spatial, and temporal organization in a complex insect society. Scientific Reports, 5, 1–11. ISSN 20452322.  https://doi.org/10.1038/srep13393.
  26. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2017. URL https://www.R-project.org/.
  27. Russell, J. C., Hanks, E. M., Modlmeier, A. P. and Hughes, D. P. (2017), Modeling collective animal movement through interactions in behavioral states. Journal of Agricultural, Biological, and Environmental Statistics, 22(3), 313–334. ISSN 15372693.  https://doi.org/10.1007/s13253-017-0296-3.
  28. Salathe, M., Kazandjieva, M., Lee, J. W., Levis, P., Feldman, M. W. and Jones, J. H. (2010), A high-resolution human contact network for infectious disease transmission. Proceedings of the National Academy of Sciences, 107(51), 22020–22025. ISSN 0027-8424.  https://doi.org/10.1073/pnas.1009094108.
  29. Sendova-Franks, A. B., Hayward, R. K., Wulf, B., Klimek, T., James, R., Planqué, R., Britton, N. F. and Franks, N. R. (2010), Emergency networking: famine relief in ant colonies. Animal Behaviour, 79(2), 473–485. ISSN 00033472.  https://doi.org/10.1016/j.anbehav.2009.11.035.
  30. Snijders, T. A. B. (1996), Stochastic actor-oriented models for network change. The Journal of Mathematical Sociology, 21(1-2), 149–172.CrossRefzbMATHGoogle Scholar
  31. Snijders, T. A. B. (2001), The statistical evaluation of social network dynamics. Sociological Methodology, 31, 361–395.CrossRefGoogle Scholar
  32. Snijders, T. A. B. (2005), Models for Longitudinal Network Data. Cambridge University Press, Cambridge.CrossRefGoogle Scholar
  33. Steglich, C., Snijders, T. A. B. and Pearson, M. (2010), Dynamic networks and behavior: Separating selection from influence. Sociological Methodology, 40(1), 329–393.  https://doi.org/10.1111/j.1467-9531.2010.01225.x.CrossRefGoogle Scholar
  34. Torney, C. J., Lamont, M., Debell, L., Angohiatok, R. J., Leclerc, L.-M. and Berdahl, A. M. (2018), Inferring the rules of social interaction in migrating caribou. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1746). ISSN 0962-8436.Google Scholar
  35. Wasserman, S. (1980), Analyzing social networks as stochastic processes. Journal of the American Statistical Association, 75(370), 280–294. ISSN 01621459.Google Scholar
  36. Wheeler, W. M. (1918), A study of some ant larvæ , with a consideration of the origin and meaning of the social habit among insects. American Philosophical Society, 57(4), 293–343.Google Scholar

Copyright information

© International Biometric Society 2019

Authors and Affiliations

  1. 1.Department of StatisticsThe Pennsylvania State UniversityUniversity ParkUSA
  2. 2.Department of EntomologyThe Pennsylvania State UniversityUniversity ParkUSA

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