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:

Predation risk constrains herbivores’ adaptive capacity to warming

Nature Ecology & Evolution volume 4, pages 1069–1074 (2020)Cite this article

Subjects

Abstract

Global warming compels larger endothermic animals to adapt either physiologically or behaviourally to avoid thermal stress, especially in tropical ecosystems. Their adaptive responses may however be compromised by other constraints, such as predation risk or starvation. Using an exceptional camera-trap dataset spanning 32 protected areas across southern Africa, we find that intermediate-sized herbivores (100–550 kg) switch activity to hotter times of the day when exposed to predation by lions. These herbivores face a tight window for foraging activity being exposed to nocturnal predation and to heat during the day, suggesting a trade-off between predation risk and thermoregulation mediated by body size. These findings stress the importance of incorporating trophic interactions into climate change predictions.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: Herbivore constraints in African savannas.
Fig. 2: Lion presence changes herbivore activity patterns.
Fig. 3: Diel activity patterns in absence (blue) or presence (red) of lion (Panthera leo).

Similar content being viewed by others

Data availability

The data are located on the Dryad Digital Repository: https://doi.org/10.5061/dryad.6m905qfvx.

Code availability

R code of all analyses is available via the Dryad Digital Repository: https://doi.org/10.5061/dryad.6m905qfvx.

References

  1. Ripple, W. J. et al. Saving the world’s terrestrial megafauna. Bioscience 66, 807–812 (2016).

    Article  Google Scholar 

  2. Daskin, J. H. & Pringle, R. M. Warfare and wildlife declines in Africa’s protected areas. Nature 553, 328–332 (2018).

    Article  CAS  Google Scholar 

  3. Craigie, I. D. et al. Large mammal population declines in Africa’s protected areas. Biol. Conserv. 143, 2221–2228 (2010).

    Article  Google Scholar 

  4. Pacifici, M., Visconti, P. & Rondinini, C. A framework for the identification of hotspots of climate change risk for mammals. Glob. Change Biol. 24, 1626–1636 (2018).

    Article  Google Scholar 

  5. Thuiller, W. et al. Vulnerability of African mammals to anthropogenic climate change under conservative land transformation assumptions. Glob. Change Biol. 12, 424–440 (2006).

    Article  Google Scholar 

  6. Moritz, C. & Agudo, R. The future of species under climate change: resilience or decline? Science 341, 504–508 (2013).

    Article  CAS  Google Scholar 

  7. Harris, G., Thirgood, S. J., Hopcraft, J. G. C., Cromsigt, J. P. G. M. & Berger, J. Global decline in aggregated migrations of large terrestrial mammals. Endang. Species Res. 7, 55–76 (2009).

    Article  Google Scholar 

  8. Pekor, A. et al. Fencing Africa’s protected areas: costs, benefits, and management issues. Biol. Conserv. 229, 67–75 (2019).

    Article  Google Scholar 

  9. Crooks, K. R. et al. Quantification of habitat fragmentation reveals extinction risk in terrestrial mammals. Proc. Natl Acad. Sci. USA 114, 7635–7640 (2017).

    Article  CAS  Google Scholar 

  10. Engelbrecht, F. et al. Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett. 10, 085004 (2015).

    Article  Google Scholar 

  11. Hetem, R. S., Fuller, A., Maloney, S. K. & Mitchell, D. Responses of large mammals to climate change. Temperature 1, 115–127 (2014).

    Article  Google Scholar 

  12. Fuller, A., Mitchell, D., Maloney, S. K. & Hetem, R. S. Towards a mechanistic understanding of the responses of large terrestrial mammals to heat and aridity associated with climate change. Clim. Change Responses 3, 10 (2016).

    Article  Google Scholar 

  13. Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).

    Article  CAS  Google Scholar 

  14. Hetem, R. S. et al. Activity re-assignment and microclimate selection of free-living Arabian oryx: responses that could minimise the effects of climate change on homeostasis? Zoology 115, 411–416 (2012).

    Article  Google Scholar 

  15. Huey, R. B. & Tewksbury, J. J. Can behavior douse the fire of climate warming? Proc. Natl Acad. Sci. USA 106, 3647–3648 (2009).

    Article  CAS  Google Scholar 

  16. McCain, C. M. & King, S. R. B. Body size and activity times mediate mammalian responses to climate change. Glob. Change Biol. 20, 1760–1769 (2014).

    Article  Google Scholar 

  17. Kohl, M. T. et al. Diel predator activity drives a dynamic landscape of fear. Ecol. Monogr. 88, 638–652 (2018).

    Article  Google Scholar 

  18. Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).

    Article  Google Scholar 

  19. Milling, C. R., Rachlow, J. L., Johnson, T. R., Forbey, J. S. & Shipley, L. A. Seasonal variation in behavioral thermoregulation and predator avoidance in a small mammal. Behav. Ecol. 28, 1236–1247 (2017).

    Article  Google Scholar 

  20. Tambling, C. J. et al. Temporal shifts in activity of prey following large predator reintroductions. Behav. Ecol. Sociobiol. 69, 1153–1161 (2015).

    Article  Google Scholar 

  21. Veldhuis, M. P. et al. Large herbivore assemblages in a changing climate: incorporating water dependence and thermoregulation. Ecol. Lett. 22, 1536–1546 (2019).

    Article  CAS  Google Scholar 

  22. Terrien, J., Perret, M. & Aujard, F. Behavioral thermoregulation in mammals: a review. Front. Biosci. 16, 1428–1444 (2011).

    Article  Google Scholar 

  23. Sinclair, A. R. E., Mduma, S. & Brashares, J. S. Patterns of predation in a diverse predator-prey system. Nature 425, 288–290 (2003).

    Article  CAS  Google Scholar 

  24. Owen-Smith, N. Megaherbivores: The Influence of Very Large Body Size on Ecology (Cambridge Univ. Press, 1988).

  25. McCafferty, D. J. et al. Estimating metabolic heat loss in birds and mammals by combining infrared thermography with biophysical modelling. Comp. Biochem. Physiol. A 158, 337–345 (2011).

    Article  CAS  Google Scholar 

  26. Clauss, M., Steuer, P., Müller, D. W. H., Codron, D. & Hummel, J. Herbivory and body size: allometries of diet quality and gastrointestinal physiology, and implications for herbivore ecology and dinosaur gigantism. PLoS ONE 8, 1–16 (2013).

    Article  Google Scholar 

  27. Kronfeld-Schor, N., Visser, M. E., Salis, L. & van Gils, J. A. Chronobiology of interspecific interactions in a changing world. Phil. Trans. R. Soc. B 372, 20160248 (2017).

  28. Rowcliffe, J. M., Kays, R., Kranstauber, B., Carbone, C. & Jansen, P. A. Quantifying levels of animal activity using camera trap data. Methods Ecol. Evol. 5, 1170–1179 (2014).

    Article  Google Scholar 

  29. Porter, W. P. & Kearney, M. Size, shape, and the thermal niche of endotherms. Proc. Natl Acad. Sci. USA 106, 19666–19672 (2009).

    Article  CAS  Google Scholar 

  30. Hayward, M. W. & Kerley, G. I. H. Prey preferences of the lion (Panthera leo). J. Zool. 267, 309–322 (2005).

    Article  Google Scholar 

  31. Tambling, C. J. et al. Spatial and temporal changes in group dynamics and range use enable anti-predator responses in African buffalo. Ecology 93, 1297–1304 (2012).

    Article  Google Scholar 

  32. Hayward, M. W. & Kerley, G. I. H. Prey preferences and dietary overlap amongst Africa’s large predators. S. Afr. J. Wildl. Res. 38, 93–108 (2008).

    Article  Google Scholar 

  33. Kinahan, A. A., Pimm, S. L. & van Aarde, R. J. Ambient temperature as a determinant of landscape use in the savanna elephant, Loxodonta africana. J. Therm. Biol. 32, 47–58 (2007).

    Article  Google Scholar 

  34. le Roux, E., Kerley, G. I. H. & Cromsigt, J. P. G. M. Megaherbivores modify trophic cascades triggered by fear of predation in an African savanna ecosystem. Curr. Biol. 28, 2493–2499 (2018).

    Article  Google Scholar 

  35. Atkins, J. L. et al. Cascading impacts of large-carnivore extirpation in an African ecosystem. Science 364, 173–177 (2019).

    CAS  PubMed  Google Scholar 

  36. Bonnot, N. C. et al. Sitka black-tailed deer (Odocoileus hemionus sitkensis) adjust habitat selection and activity rhythm to the absence of predators. Can. J. Zool. 94, 385–394 (2016).

    Article  Google Scholar 

  37. Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science (80-) 360, 1232 LP–1235 (2018).

  38. Miller, J. R. B., Pitman, R. T., Mann, G. K. H., Fuller, A. K. & Balme, G. A. Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition. J. Anim. Ecol. 87, 1709–1726 (2018).

    Article  Google Scholar 

  39. Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).

  40. ArcGIS Desktop: Release 10.5 (ESRI, 2015).

  41. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).

    Article  Google Scholar 

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

  43. Bivand, R. & Lewin-Koh, N. maptools: Tools for handling spatial objects. R package version 0.9-5 (2019).

  44. Kingdon, J. et al. Mammals of Africa (Bloomsbury, 2013).

  45. Rowcliffe, M. activity: Animal activity statistics. R package version 1.2 (2019).

  46. Ridout, M. S. & Linkie, M. Estimating overlap of daily activity patterns from camera trap data. J. Agric. Biol. Environ. Stat. 14, 322–337 (2009).

    Article  Google Scholar 

  47. Hofmeester, T. R. et al. Framing pictures: a conceptual framework to identify and correct for biases in detection probability of camera traps enabling multi-species comparison. Ecol. Evol. 9, 2320–2336 (2019).

    Article  Google Scholar 

  48. Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Ezemvelo KZN Wildlife management and research staff for their help and logistical support while undertaking this study. The camera-trap surveys were funded by Panthera (with support from Peace Parks Foundation and Cartier) and run with the help of staff and volunteers from Wildlife ACT and Siyafunda Conservation. Furthermore, M.P.V. has been financially supported by the AfricanBioServices project which received funding from the European Union’s Horizon 2020 research and innovation programme under grant no. 641918. We thank J. L. Atkins, R. S. Hetem, H. Olff, N. Owen-Smith and R. M. Pringle for their comments on earlier versions of the manuscript.

Author information

Authors and Affiliations

  1. Institute of Environmental Sciences, Leiden University, Leiden, the Netherlands

    Michiel P. Veldhuis

  2. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

    Michiel P. Veldhuis

  3. Groningen Institute for Evolutionary Life Sciences, University of Groningen, Groningen, the Netherlands

    Michiel P. Veldhuis

  4. Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences, Umeå, Sweden

    Tim R. Hofmeester & Joris P.G.M. Cromsigt

  5. Panthera, New York, NY, USA

    Guy Balme & Ross T. Pitman

  6. Institute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Rondebosch, South Africa

    Guy Balme & Ross T. Pitman

  7. Ezemvelo KZN Wildlife, Cascades, South Africa

    Dave J. Druce

  8. School of Life Sciences, University of KwaZulu-Natal, Scottsville, South Africa

    Dave J. Druce

  9. Department of Zoology, Nelson Mandela Metropolitan University, Port Elizabeth, South Africa

    Joris P.G.M. Cromsigt

  10. Environmental Sciences Group, Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, the Netherlands

    Joris P.G.M. Cromsigt

Authors
  1. Michiel P. Veldhuis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tim R. Hofmeester
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guy Balme
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Dave J. Druce
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ross T. Pitman
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Joris P.G.M. Cromsigt
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.P.V. and J.P.M.G.C. conceived the study and developed the concept. G.B., R.T.P. and D.J.D. contributed data. T.R.H. and M.P.V. analysed the data. M.P.V. and J.P.M.G.C. wrote the first draft of the manuscript and all authors contributed revisions.

Corresponding author

Correspondence to Michiel P. Veldhuis.

Ethics declarations

Competing interests

The authors declare no competing interests.

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 Overview of 32 protected areas in South Africa with camera-trap surveys and 17 South African Weather Service stations.

Background colour represent mean annual temperature (WorldClim.org). Lions were either present (circles) or absent (triangle).

Extended Data Fig. 2 Daily temperature distributions during the 73 camera-trap surveys in 32 protected areas in South Africa.

A, hourly temperature records for an example survey (Zingela 2017). Line represents the hourly mean, dots are individual observations. B, temperature averaged by hour for the 73 surveys and C, relative temperature for each survey (standardized between 0 and 1).

Extended Data Fig. 3 Diel activity patterns of larger carnivores.

Wild dog (A; n = 751 detections), leopard (n = 6,833 detections), cheetah (n = 487 detections), spotted hyena (n = 5,331 detections), lion (n = 2,657 detections) and all carnivores combined (F; n = 16,059 detections). Species are ordered by increasing body mass, which is presented in kilograms below the scientific name. Dark grey represents 95% confidence interval around the estimated activity pattern, grey background represents period of high carnivore activity.

Extended Data Fig. 4 Diel activity patterns of the 29 herbivores in protected areas with lions (red) or without lions (blue).

Species are ordered by increasing body mass, which is presented in kilograms below the scientific name. Grey background represents period of high carnivore activity (Extended Data Fig. 3). Coloured area around the estimates represents the 95% confidence interval.

Extended Data Fig. 5 Lion (Panthera leo) dietary preferences based on Jacobs’ index (mean±SE) of 48 lion populations across Africa at differing prey densities.

Data from ref. 30. Only species recorded in lion diet more than once were included in the dataset. Vertical lines represent the preferred prey range used in this study (100-550kg).

Extended Data Fig. 6 Herbivore detections at each camera-trap location are not related to the number of lion detections.

(Linear Mixed Model: F1,22.4=0.09, P=0.77, n=39 surveys). Only herbivore species in the preferred prey range of lion (100-550kg) were included in this analysis.

Extended Data Fig. 7 Camera-trap locations for Hluhluwe-iMfolozi Park.

An example of how cameras were distributed across protected areas. The most Southern part of the area is managed as a wilderness area where tourist and research access is limited and was thus excluded in this study.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Veldhuis, M.P., Hofmeester, T.R., Balme, G. et al. Predation risk constrains herbivores’ adaptive capacity to warming. Nat Ecol Evol 4, 1069–1074 (2020). https://doi.org/10.1038/s41559-020-1218-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41559-020-1218-2

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