Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Spatial memory predicts home range size and predation risk in pheasants

Nature Ecology & Evolution volume 7, pages 461–471 (2023)Cite this article

Subjects

Abstract

Most animals confine their activities to a discrete home range, long assumed to reflect the fitness benefits of obtaining spatial knowledge about the landscape. However, few empirical studies have linked spatial memory to home range development or determined how selection operates on spatial memory via the latter’s role in mediating space use. We assayed the cognitive ability of juvenile pheasants (Phasianus colchicus) reared under identical conditions before releasing them into the wild. Then, we used high-throughput tracking to record their movements as they developed their home ranges, and determined the location, timing and cause of mortality events. Individuals with greater spatial reference memory developed larger home ranges. Mortality risk from predators was highest at the periphery of an individual’s home range in areas where they had less experience and opportunity to obtain spatial information. Predation risk was lower in individuals with greater spatial memory and larger core home ranges, suggesting selection may operate on spatial memory by increasing the ability to learn about predation risk across the landscape. Our results reveal that spatial memory, determined from abstract cognitive assays, shapes home range development and variation, and suggests predation risk selects for spatial memory via experience-dependent spatial variation in mortality.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Cognitive predictors of home range size in pheasants.
Fig. 2: Spatial patterns of pheasant mortality and consequences for selection on spatial memory and home range.

Similar content being viewed by others

Data availability

Data required to rerun the statistical analyses of this study are available online (https://data.mendeley.com/datasets/m89226xg6p)87. Animal AKDE models and GPS coordinates are available from the corresponding author upon request.

Code availability

R code used to run the simulation analyses of this study are available online (https://data.mendeley.com/datasets/m89226xg6p)87.

References

  1. Börger, L., Dalziel, B. D. & Fryxell, J. M. Are there general mechanisms of animal home range behaviour? A review and prospects for future research. Ecol. Lett. 11, 637–650 (2008).

    Article  PubMed  Google Scholar 

  2. Burt, W. H. Territoriality and home range concepts as applied to mammals. J. Mammal. 24, 346 (1943).

    Article  Google Scholar 

  3. Darwin, C. On the Origin of Species by Means of Natural Selection (D. Appleton Co., 1859).

  4. Merkle, J., Fortin, D. & Morales, J. M. A memory‐based foraging tactic reveals an adaptive mechanism for restricted space use. Ecol. Lett. 17, 924–931 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Bordes, F., Morand, S., Kelt, D. A. & Van Vuren, D. H. Home range and parasite diversity in mammals. Am. Nat. 173, 467–474 (2009).

    Article  PubMed  Google Scholar 

  6. Morales, J. M. et al. Building the bridge between animal movement and population dynamics. Philos. Trans. R. Soc. B: Biol. Sci. 365, 2289–2301 (2010).

    Article  Google Scholar 

  7. Lewis, M. A. & Murray, J. D. Modelling territoriality and wolf-deer interactions. Nature 366, 738–740 (1993).

    Article  Google Scholar 

  8. Kelt, D. A. & Van Vuren, D. H. The ecology and macroecology of mammalian home range area. Am. Nat. 157, 637–645 (2001).

    Article  CAS  PubMed  Google Scholar 

  9. Wang, M. & Grimm, V. Home range dynamics and population regulation: an individual-based model of the common shrew Sorex araneus. Ecol. Modell. 205, 397–409 (2007).

    Article  Google Scholar 

  10. Moorcroft, P. R., Lewis, M. A. & Crabtree, R. L. Mechanistic home range models capture spatial patterns and dynamics of coyote territories in Yellowstone. Proc. R. Soc. B: Biol. Sci. 273, 1651–1659 (2006).

    Article  Google Scholar 

  11. Powell, R. A. in Research Techniques in Animal Ecology Vol. 65 (eds. Boitani, L. & Fuller, T. K.) 599 (Columbia Univ. Press, 2000).

  12. Spencer, W. D. Home ranges and the value of spatial information. J. Mammal. 93, 929–947 (2012).

    Article  Google Scholar 

  13. Bracis, C., Gurarie, E., Van Moorter, B. & Goodwin, R. A. Memory effects on movement behavior in animal foraging. PLoS ONE 10, e0136057 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Fagan, W. F. et al. Spatial memory and animal movement. Ecol. Lett. 16, 1316–1329 (2013).

    Article  PubMed  Google Scholar 

  15. Powell, R. A. & Mitchell, M. S. What is a home range? J. Mammal. 93, 948–958 (2012).

    Article  Google Scholar 

  16. Stamps, J. Motor learning and the value of familiar space. Am. Nat. 146, 41–58 (1995).

    Article  Google Scholar 

  17. Gautestad, A. O. & Mysterud, I. Spatial memory, habitat auto-facilitation and the emergence of fractal home range patterns. Ecol. Modell. 221, 2741–2750 (2010).

    Article  Google Scholar 

  18. Gautestad, A. O. & Mysterud, I. Intrinsic scaling complexity in animal dispersion and abundance. Am. Nat. 165, 44–55 (2005).

    Article  PubMed  Google Scholar 

  19. Merkle, J. A., Potts, J. R. & Fortin, D. Energy benefits and emergent space use patterns of an empirically parameterized model of memory‐based patch selection. Oikos 126, 185–196 (2017).

  20. Schlägel, U. E. & Lewis, M. A. Detecting effects of spatial memory and dynamic information on animal movement decisions. Methods Ecol. Evolution 5, 1236–1246 (2014).

    Article  Google Scholar 

  21. Van Moorter, B. et al. Memory keeps you at home: a mechanistic model for home range emergence. Oikos 118, 641–652 (2009).

    Article  Google Scholar 

  22. Riotte-Lambert, L., Benhamou, S. & Chamaillé-Jammes, S. How memory-based movement leads to nonterritorial spatial segregation. Am. Naturalist 185, E103–E116 (2015).

    Article  Google Scholar 

  23. Marchand, P. et al. Combining familiarity and landscape features helps break down the barriers between movements and home ranges in a non‐territorial large herbivore. J. Anim. Ecol. 86, 371–383 (2017).

    Article  PubMed  Google Scholar 

  24. Gautestad, A. O., Loe, L. E. & Mysterud, A. Inferring spatial memory and spatiotemporal scaling from GPS data: comparing red deer Cervus elaphus movements with simulation models. J. Anim. Ecol. 82, 572–586 (2013).

    Article  PubMed  Google Scholar 

  25. Ranc, N., Cagnacci, F. & Moorcroft, P. R. Memory drives the formation of animal home ranges: evidence from a reintroduction. Ecol. Lett. 25, 716–728 (2022).

    Article  PubMed  Google Scholar 

  26. Ranc, N., Moorcroft, P. R., Ossi, F. & Cagnacci, F. Experimental evidence of memory-based foraging decisions in a large wild mammal. Proc. Natl Acad. Sci. USA 118, e2014856118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Potts, J. R. & Lewis, M. A. A mathematical approach to territorial pattern formation. Am. Math. Monthly 121, 754–770 (2014).

    Article  Google Scholar 

  28. Shettleworth, S. J. Cognition, Evolution, and Behavior (Oxford Univ. Press, 2009).

  29. van Asselen, M. et al. Brain areas involved in spatial working memory. Neuropsychologia 44, 1185–1194 (2006).

    Article  PubMed  Google Scholar 

  30. Paul, C., Magda, G. & Abel, S. Spatial memory: theoretical basis and comparative review on experimental methods in rodents. Behav. Brain Res. 203, 151–164 (2009).

    Article  PubMed  Google Scholar 

  31. Boratyński, Z. Energetic constraints on mammalian home-range size. Funct. Ecol. 34, 468–474 (2020).

    Article  Google Scholar 

  32. Tamburello, N., Côté, I. M. & Dulvy, N. K. Energy and the scaling of animal space use. Am. Naturalist 186, 196–211 (2015).

    Article  Google Scholar 

  33. McNab, B. K. Bioenergetics and the determination of home range size. Am. Naturalist 97, 133–140 (1963).

    Article  Google Scholar 

  34. McNab, B. K. Food habits, energetics, and the population biology of mammals. Am. Naturalist 116, 106–124 (1980).

    Article  Google Scholar 

  35. Fokidis, H. B., Risch, T. S. & Glenn, T. C. Reproductive and resource benefits to large female body size in a mammal with female-biased sexual size dimorphism. Anim. Behav. 73, 479–488 (2007).

    Article  Google Scholar 

  36. Saïd, S. et al. What shapes intra-specific variation in home range size? A case study of female roe deer. Oikos 118, 1299–1306 (2009).

    Article  Google Scholar 

  37. Schradin, C. et al. Female home range size is regulated by resource distribution and intraspecific competition: a long-term field study. Anim. Behav. 79, 195–203 (2010).

    Article  Google Scholar 

  38. Dröge, E., Creel, S., Becker, M. S. & M’soka, J. Risky times and risky places interact to affect prey behaviour. Nat. Ecol. Evolution 1, 1123–1128 (2017).

    Article  Google Scholar 

  39. Croston, R., Branch, C., Kozlovsky, D., Dukas, R. & Pravosudov, V. Heritability and the evolution of cognitive traits. Behav. Ecol. 26, 1447–1459 (2015).

    Article  Google Scholar 

  40. Ashton, B. J., Ridley, A. R., Edwards, E. K. & Thornton, A. Cognitive performance is linked to group size and affects fitness in Australian magpies. Nature 554, 364–367 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Madden, J. R., Langley, E. J. G., Whiteside, M. A., Beardsworth, C. E. & Van Horik, J. O. The quick are the dead: pheasants that are slow to reverse a learned association survive for longer in the wild. Philos. Trans. R. Soc. B. Biol. Sci. https://doi.org/10.1098/rstb.2017.0297 (2018).

  42. Sonnenberg, B. R., Branch, C. L., Pitera, A. M., Bridge, E. & Pravosudov, V. V. Natural selection and spatial cognition in wild food-caching mountain chickadees. Curr. Biol. 29, 670–676 (2019).

    Article  CAS  PubMed  Google Scholar 

  43. Shaw, R. C., MacKinlay, R. D., Clayton, N. S. & Burns, K. C. Memory performance influences male reproductive success in a wild bird. Curr. Biol. 29, 1498–1502.e3 (2019).

    Article  CAS  PubMed  Google Scholar 

  44. Gehr, B. et al. Stay home, stay safe—site familiarity reduces predation risk in a large herbivore in two contrasting study sites. J. Anim. Ecol. 89, 1329–1339 (2020).

    Article  PubMed  Google Scholar 

  45. Palmer, M. S., Fieberg, J., Swanson, A., Kosmala, M. & Packer, C. A ‘dynamic’ landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol. Lett. 20, 1364–1373 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Willems, E. P. & Hill, R. A. Predator-specific landscapes of fear and resource distribution: effects on spatial range use. Ecology 90, 546–555 (2009).

    Article  PubMed  Google Scholar 

  47. Gaynor, K. M., Brown, J. S., Middleton, A. D., Power, M. E. & Brashares, J. S. Landscapes of fear: spatial patterns of risk perception and response. Trends Ecol. Evolution 34, 355–368 (2019).

    Article  Google Scholar 

  48. Bose, S. et al. Implications of fidelity and philopatry for the population structure of female black-tailed deer. Behav. Ecol. 28, 983–990 (2017).

    Article  Google Scholar 

  49. Forrester, T. D., Casady, D. S. & Wittmer, H. U. Home sweet home: fitness consequences of site familiarity in female black-tailed deer. Behav. Ecol. Sociobiol. 69, 603–612 (2015).

    Article  Google Scholar 

  50. Magrath, R. D., Haff, T. M., Fallow, P. M. & Radford, A. N. Eavesdropping on heterospecific alarm calls: from mechanisms to consequences. Biol. Rev. 90, 560–586 (2015).

    Article  PubMed  Google Scholar 

  51. Skelhorn, J. & Rowe, C. Cognition and the evolution of camouflage. Proc. R. Soc. B: Biol. Sci. 283, 20152890 (2016).

    Article  Google Scholar 

  52. Dickinson, A. Associative learning and animal cognition. Philos. Trans. R. Soc. B: Biol. Sci. 367, 2733–2742 (2012).

    Article  Google Scholar 

  53. Baddeley, A. D. & Lieberman, K. in Exploring Working Memory 206–223 (Routledge, 2017).

  54. Olton, D. S. & Samuelson, R. J. Remembrance of places passed: spatial memory in rats. J. Exp. Psychol. Anim. Behav. Process. 2, 97–116 (1976).

    Article  Google Scholar 

  55. Lashley, K. S. Brain Mechanisms and Intelligence: A Quantitative Study of Injuries to the Brain (Univ. Chicago Press, 1929).

  56. O’keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Oxford Univ. Press, 1978).

  57. Beardsworth, C. E. et al. Is habitat selection in the wild shaped by individual-level cognitive biases in orientation strategy? Ecol. Lett. 24, 751–760 (2021).

    Article  PubMed  Google Scholar 

  58. Rowe, C. & Healy, S. D. Measuring variation in cognition. Behav. Ecol. 25, 1287–1292 (2014).

    Article  Google Scholar 

  59. Warner, R. E. Use of cover by pheasant broods in east-central Illinois. J. Wildl. Manag. 43, 334 (1979).

    Article  Google Scholar 

  60. Toledo, S. et al. Cognitive map-based navigation in wild bats revealed by a new high-throughput tracking system. Science 369, 188–193 (2020).

    Article  CAS  PubMed  Google Scholar 

  61. Weiser, A. W. et al. Characterizing the accuracy of a self-synchronized reverse-GPS wildlife localization system. In Proc. 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2016 1–12 (IEEE, 2016).

  62. Nathan, R. et al. Big-data approaches lead to an increased understanding of the ecology of animal movement. Science 375, eabg1780 (2022).

    Article  CAS  PubMed  Google Scholar 

  63. Beardsworth, C. E. et al. Validating ATLAS: a regional-scale high-throughput tracking system. Methods Ecol. Evolution 13, 1990–2004 (2022).

    Article  Google Scholar 

  64. Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: an r package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evolution 7, 1124–1132 (2016).

    Article  Google Scholar 

  65. Clutton‐Brock, T. H. & Harvey, P. H. Primates, brains and ecology. J. Zool. 190, 309–323 (1980).

    Article  Google Scholar 

  66. Avgar, T. et al. Space-use behaviour of woodland caribou based on a cognitive movement model. J. Anim. Ecol. 84, 1059–1070 (2015).

    Article  PubMed  Google Scholar 

  67. Laundré, J. W., Hernández, L. & Ripple, W. J. The landscape of fear: ecological implications of being afraid. Open Ecol. J. 3, 1–7 (2010).

    Article  Google Scholar 

  68. Stephens, D. W. & Krebs, J. R. Foraging Theory (Princeton Univ. Press, 2019).

  69. Beauchamp, G. Animal Vigilance: Monitoring Predators and Competitors. Animal Vigilance: Monitoring Predators and Competitors (Elsevier, 2015).

  70. Langley, E. J. G. et al. Heritability and correlations among learning and inhibitory control traits. Behav. Ecol. 31, 798–806 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Chen, J., Zou, Y., Sun, Y.-H. & Ten Cate, C. Problem-solving males become more attractive to female budgerigars. Science 363, 166–167 (2019).

    Article  CAS  PubMed  Google Scholar 

  72. Vale, R., Evans, D. A. & Branco, T. Rapid spatial learning controls instinctive defensive behavior in mice. Curr. Biol. 27, 1342–1349 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Burt de Perera, T. & Guilford, T. Rapid learning of shelter position in an intertidal fish, the shanny Lipophrys pholis L. J. Fish. Biol. 72, 1386–1392 (2008).

    Article  Google Scholar 

  74. Font, E. Rapid learning of a spatial memory task in a lacertid lizard (Podarcis liolepis). Behav. Procs. 169, 103963 (2019).

    Article  Google Scholar 

  75. Senar, J. & Pascual, J. Keel and tarsus length may provide a good predictor of avian body size. Ard.-Wageningen 85, 269–274 (1997).

    Google Scholar 

  76. Lavielle, M. Detection of multiple changes in a sequence of dependent variables. Stoch. Process. Appl. 83, 79–102 (1999).

    Article  Google Scholar 

  77. Calenge, C. The package ‘adehabitat’ for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).

    Article  Google Scholar 

  78. Millspaugh, J. J. A Manual for Wildlife Radio Tagging Robert E. Kenward. The Auk 118 (Academic Press, 2001).

  79. Gupte, P. R. et al. A guide to pre-processing high-throughput animal tracking data. J. Anim. Ecol. 91, 287–307 (2022).

    Article  PubMed  Google Scholar 

  80. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

  81. Grahn, M., Göransson, G. & Von Schantz, T. Territory acquisition and mating success in pheasants, Phasianus colchicus: an experiment. Anim. Behav. 46, 721–730 (1993).

    Article  Google Scholar 

  82. Ridley, M. W. & Hill, D. A. Social organization in the pheasant (Phasianus colchicus): harem formation, mate selection and the role of mate guarding. J. Zool. 211, 619–630 (1987).

    Article  Google Scholar 

  83. Gompper, M. E. & Gittleman, J. L. Home range scaling: intraspecific and comparative trends. Oecologia 87, 343–348 (1991).

    Article  PubMed  Google Scholar 

  84. Fisher, R. A. in Breakthroughs in Statistics (eds Kotz, S. & Johnson, N. L.) 66–70 (Springer, 1992).

  85. Barton, K. MuMIn: Multi-Model Inference (cran.r-project.org, 2022).

  86. Nakagawa, S. A farewell to Bonferroni: the problems of low statistical power and publication bias. Behav. Ecol. 15, 1044–1045 (2004).

    Article  Google Scholar 

  87. Heathcote, R. Data for ‘Spatial memory predicts home range size and predation risk in pheasants’ nature ecology and evolution. Mendeley Data https://doi.org/10.17632/m89226xg6p.1 (2022).

Download references

Acknowledgements

We thank Rothamsted Research North Wyke for accommodating the rearing and release of the pheasants, and various landowners in Devon for hosting our tracking equipment. We also thank the Minerva Foundation and the Minerva Center for movement ecology for their persistence in supporting and developing ATLAS. Additionally, we thank K. Griffin and A. Morris for their help with data collection and animal husbandry, and F. Moultrie for helpful discussions and comments on the manuscript. We also thank N. Griffiths of Sporting Shots Ltd for supplying the pheasants for the carcass tracking experiment. This work was funded by an European Research Council Consolidator award no. 616474 to J.R.M.

Author information

Authors and Affiliations

  1. School of Biological Sciences, University of Bristol, Bristol, UK

    Robert J. P. Heathcote

  2. Centre for Research in Animal Behaviour, College of Life and Environmental Sciences, University of Exeter, Exeter, UK

    Robert J. P. Heathcote, Mark A. Whiteside, Christine E. Beardsworth, Jayden O. Van Horik, Philippa R. Laker & Joah R. Madden

  3. School of Biological and Marine Sciences, University of Plymouth, Plymouth, UK

    Mark A. Whiteside

  4. NIOZ Royal Netherlands Institute for Sea Research, Department of Coastal Systems, Den Burg, the Netherlands

    Christine E. Beardsworth

  5. School of Biological and Environmental Sciences, Liverpool John Moores University, Liverpool, UK

    Christine E. Beardsworth

  6. University of Exeter Clinical Trials Unit, College of Medicine and Health, University of Exeter Medical School, Exeter, UK

    Jayden O. Van Horik

  7. Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel

    Sivan Toledo

  8. Movement Ecology Laboratory, Department of Ecology, Evolution and Behavior, Alexander Silberman Institute of Life Sciences, Faculty of Science, The Hebrew University of Jerusalem, Jerusalem, Israel

    Yotam Orchan & Ran Nathan

Authors
  1. Robert J. P. Heathcote
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mark A. Whiteside
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christine E. Beardsworth
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jayden O. Van Horik
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Philippa R. Laker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sivan Toledo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yotam Orchan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ran Nathan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Joah R. Madden
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.J.P.H. and J.R.M. conceived the idea for the manuscript. C.E.B., P.R.L., M.A.W., J.O.v.H. and J.R.M. collected the cognition data. M.A.W., C.E.B. and J.R.M. collected the movement data. R.J.P.H. and M.A.W. carried out the carcass tracking study. R.J.P.H. conducted the analyses and led the writing. R.N., Y.O. and S.T. developed the reverse‐GPS system and provided support throughout data collection. All authors contributed critically to the drafts.

Corresponding author

Correspondence to Robert J. P. Heathcote.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Ecology & Evolution thanks Francesca Cagnacci and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Home range overlaps with simulated and real high-risk regions.

Density plot showing the proportion of the surviving bird’s home range that overlaps with the high-risk region of the landscape. The vertical dashed blue line indicates the mean proportion of home range overlaps with the real high-risk region.

Extended Data Fig. 2

Histogram comparison of distribution of home range isopleths where deaths occur for the birds that were killed (right plot) compared to all non-predated neighbour who’s 100% minimum convex polygon overlapped with the death location (left plot).

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Heathcote, R.J.P., Whiteside, M.A., Beardsworth, C.E. et al. Spatial memory predicts home range size and predation risk in pheasants. Nat Ecol Evol 7, 461–471 (2023). https://doi.org/10.1038/s41559-022-01950-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-022-01950-5

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing