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Global predation pressure redistribution under future climate change

Abstract

How climate affects biotic interactions is a question of urgent concern1,2,3. Theory predicts that biotic interactions are stronger at lower latitudes4,5,6. However, the role of climate in governing these patterns is typically assumed, rather than explicitly tested. Here, we dissected the influence of climatic descriptors on predation pressure using data from a global experiment with model caterpillars. We then used projections of future climate change to predict shifts in predation pressure. Climate, particularly components of temperature, explained latitudinal and elevational patterns of predation better than latitude or elevation by themselves. Projected predation pressure was greater under higher temperatures and more stable climates. Increased climatic instability projected for the near future predicts a general decrease in predation pressure over time. By identifying the current climatic drivers of global patterns in a key biotic interaction, we show how shifts in these drivers could alter the functioning of terrestrial ecosystems and their associated services.

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Fig. 1: Direct and indirect effects of latitude, elevation and climate (current and future) on predation pressure.
Fig. 2: Global distribution of high and low predation pressure under the current climate (2015) and a climate scenario projected for 2070.
Fig. 3: Changes in predation pressure predicted by the GLMM approach.
Fig. 4: Global shift in the suitability of high and low predation pressure between the present-day climate and that of 2070.

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Data availability

The data that support the findings of this study are publicly available in the Dryad Digital Repository at https://doi.org/10.5061/dryad.j432q.

References

  1. Tylianakis, J. M. et al. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).

    Article  Google Scholar 

  2. Blois, J. L. et al. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).

    Article  CAS  Google Scholar 

  3. Urban, M. C. et al. Improving the forecast for biodiversity under climate change. Science 353, aad8466 (2016).

    Article  Google Scholar 

  4. Dobzhansky, T. Evolution in the tropics. Am. Sci. 38, 209–221 (1950).

    Google Scholar 

  5. Schemske, D. W. in Speciation and Patterns of Diversity (eds Butlin, R. K., Bridle, J. R. & Schluter, D.) 219–239 (Cambridge Univ. Press, Cambridge, 2009).

  6. Schemske, D. W. et al. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).

    Article  Google Scholar 

  7. IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).

  8. Allan, R. & Soden, B. Atmospheric warming and the amplification of precipitation extremes. Science 321, 1481–1484 (2008).

    Article  CAS  Google Scholar 

  9. Fischer, E. M. & Knutti, R. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nat. Clim. Change 5, 560–564 (2015).

    Article  Google Scholar 

  10. Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science 287, 1770–1774 (2000).

    Article  CAS  Google Scholar 

  11. Callaway, R. M. et al. Positive interactions among alpine plants increase with stress. Nature 417, 844–847 (2002).

    Article  CAS  Google Scholar 

  12. Romero, G. Q. et al. Ecosystem engineering effects on species diversity across ecosystems: a meta-analysis. Biol. Rev. 90, 877–890 (2015).

    Article  Google Scholar 

  13. LaManna, J. A. et al. Plant diversity increases with the strength of negative density dependence at the global scale. Science 356, 1389–1392 (2017).

    Article  CAS  Google Scholar 

  14. Roslin, T. et al. Higher predation risk for insect prey at low latitudes and elevations. Science 356, 742–744 (2017).

    Article  CAS  Google Scholar 

  15. Moles, A. T. & Ollerton, J. Is the notion that species interactions are stronger and more specialized in the tropics a zombie idea? Biotropica 48, 141–145 (2016).

    Article  Google Scholar 

  16. Romero, G. Q. et al. Food web structure shaped by habitat size and climate across a latitudinal gradient. Ecology 97, 2705–2715 (2016).

    Article  Google Scholar 

  17. Reynolds, P. L. et al. Latitude, temperature and habitat complexity predict predation pressure in eelgrass beds across the Northern Hemisphere. Ecology 99, 29–35 (2018).

    Article  Google Scholar 

  18. Jiang, M. et al. Biome-specific climatic space defined by temperature and precipitation predictability. Glob. Ecol. Biogeogr. 26, 1270–1282 (2017).

    Article  Google Scholar 

  19. Körner, C. The use of ‘altitude’ in ecological research. Trends Ecol. Evol. 22, 569–574 (2007).

    Article  Google Scholar 

  20. Hawkins, B. A. & Diniz-Filho, J. A. F. ‘Latitude’ and geographic patterns in species richness. Ecography 27, 268–272 (2004).

    Article  Google Scholar 

  21. Anstett, D. N. et al. Sources of controversy surrounding latitudinal patterns in herbivory and defense. Trends Ecol. Evol. 31, 789–802 (2016).

    Article  Google Scholar 

  22. Brown, J. H. et al. Towards a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).

    Article  Google Scholar 

  23. Rosenblatt, A. E. & Schmitz, O. J. Climate change, nutrition, and bottom-up and top-down food web processes. Trends Ecol. Evol. 31, 965–975 (2016).

    Article  Google Scholar 

  24. Gilbert, B. et al. A bioenergetic framework for the temperature dependence of trophic interactions. Ecol. Lett. 17, 902–914 (2014).

    Article  Google Scholar 

  25. Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).

    Article  Google Scholar 

  26. Wheeler, T. & von Braun, J. Climate change impacts on global food security. Science 341, 508–513 (2013).

    Article  CAS  Google Scholar 

  27. Chen, I.-C. et al. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).

    Article  CAS  Google Scholar 

  28. Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. Lond. B 280, 20121890 (2013).

    Article  Google Scholar 

  29. Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Article  Google Scholar 

  30. Marino, N. A. C., Romero, G. Q. & Farjalla, V. F. Geographical and experimental contexts modulate the effect of warming on top‐down control: a meta‐analysis. Ecol. Lett. 21, 455–466 (2018).

    Article  Google Scholar 

  31. Roslin, T. et al. Dryad Data from: Higher predation risk for insect prey at low latitudes and elevations. (Dryad Digital Repository, 2017); https://doi.org/10.5061/dryad.j432q

  32. Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).

    Article  Google Scholar 

  33. Title, P. O. & Bemmels, J. B. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41, 291–307 (2017).

    Article  Google Scholar 

  34. Morueta-Holme, N. et al. Habitat area and climate stability determine geographical variation in plant species range sizes. Ecol. Lett. 16, 1446–1454 (2013).

    Article  Google Scholar 

  35. Wilson, M. F. J. et al. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Mar. Geodesy 30, 3–35 (2007).

    Article  Google Scholar 

  36. Conrad, O. et al. System for automated geoscientific analyses (SAGA) v. 2.1.4. Geosci. Model Dev. 8, 1991–2007 (2015).

    Article  Google Scholar 

  37. Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).

    Article  Google Scholar 

  38. Grace, J. B. et al. On the specification of structural equation models for ecological systems. Ecol. Monogr. 80, 67–87 (2010).

    Article  Google Scholar 

  39. Peres-Neto, P. R. & Jackson, D. A. How well do multivariate data sets match? The advantages of a Procrustean superimposition approach over the Mantel test. Oecologia 129, 169–178 (2001).

    Article  Google Scholar 

  40. R Development Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

  41. Wiens, J. A. et al. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc. Natl Acad. Sci. USA 106, 19729–19736 (2009).

    Article  CAS  Google Scholar 

  42. Peterson, A. T. et al. Ecological Niches and Geographic Distributions (Princeton Univ. Press, Princeton, 2011).

  43. Soberón, J. & Nakamura, M. Niches and distributional areas: concepts, methods and assumptions. Proc. Natl Acad. Sci. USA 106, 19644–19650 (2009).

    Article  Google Scholar 

  44. Rahbek, C. et al. Predicting continental-scale patterns of bird species richness with spatially explicit models. Proc. R. Soc. B 274, 165–174 (2007).

    Article  Google Scholar 

  45. Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).

    Article  Google Scholar 

  46. Diniz-Filho, J. A. F. et al. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32, 897–906 (2009).

    Article  Google Scholar 

  47. Nix, H. in Snakes: Atlas of Elapid Snakes of Australia (ed. Longmore, R.) 4–10 (Bureau of Flora and Fauna, Canberra, 1986).

  48. Carpenter, G. et al. DOMAIN: a flexible modeling procedure for mapping potential distributions of animals and plants. Biodiv. Conserv. 2, 667–680 (1993).

    Article  Google Scholar 

  49. Farber, O. & Kadmon, R. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecol. Model. 160, 115–130 (2003).

    Article  CAS  Google Scholar 

  50. Phillips, S. J. & Dudik, M. Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).

    Article  Google Scholar 

  51. Tax, D. M. J. & Duin, R. P. W. Support vector data description. Mach. Learn. 54, 45–66 (2004).

    Article  Google Scholar 

  52. Liu, C. et al. On the selection of thresholds for predicting species occurrence with presence-only data. Ecol. Evol. 6, 337–348 (2016).

    Article  Google Scholar 

  53. Allouche, O. et al. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).

    Article  Google Scholar 

  54. Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).

    Article  Google Scholar 

  55. Sobral-Souza, T. et al. Biogeography of neotropical rainforests: past connections between Amazon and Atlantic forest detected by ecological niche modeling. Evol. Ecol. 29, 643–655 (2015).

    Article  Google Scholar 

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Acknowledgements

We are most grateful to all authors of Roslin et al.14; without their contributions, this study would not have been possible. G.Q.R. was supported by BPE-FAPESP (grant no. 2016/01209-9) and CNPq-Brazil research grants. T.S.-S. was supported by a CNPq fellowship (grant no. 151003/2018-1). We gratefully acknowledge funding from the Academy of Finland (grant nos 138346, 276909, 285803 to T.R.). This work was performed during GQR’s sabbatical stay at Queen Mary University of London. This paper is a contribution of the Brazilian Network on Global Climate Change Research funded by CNPq (grant no. 550022/2014-7) and FINEP (grant no. 01.13.0353.00).

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G.Q.R. conceived the idea, developed it with all co-authors, and drafted the manuscript with inputs from all co-authors. G.Q.R., T.G.-S. and N.A.C.M. performed the statistical analyses. T.S.-S. performed the niche modelling with inputs from T.G.-S. and G.Q.R. T.G.-S. and T.S.-S. drafted the figures. All authors contributed substantially to revisions and the final format of the manuscript.

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Correspondence to Gustavo Q. Romero.

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Supplementary Tables 1–3, Supplementary Figures 1–11

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Romero, G.Q., Gonçalves-Souza, T., Kratina, P. et al. Global predation pressure redistribution under future climate change. Nature Clim Change 8, 1087–1091 (2018). https://doi.org/10.1038/s41558-018-0347-y

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