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Warming causes contrasting spider behavioural responses by changing their prey size spectra

Nature Climate Change volume 14, pages 190–197 (2024)Cite this article

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Abstract

Predators may adapt to global warming via behavioural plasticity. However, empirical evidence showing such adaptations in terrestrial ecosystems is scarce. Here we report behavioural shifts that alter the web mesh size of two dominant predatory spider species in response to experimental warming in an alpine meadow field. Experimental large open-top chambers increased the mean annual air temperature by 0.6 °C, resulting in a decrease in the web mesh size of the large spider (−43.6%), and an increase in the web mesh size of the small spider (+79.8%). Structural equation models indicated that the changes in mesh size and web area were primarily the result of warming-induced changes in prey size spectra, which in turn were impacted by warming-induced changes in soil moisture and plant community. These results indicate that predators can adjust their behavioural responses to warming-induced changes in the physical setting and prey community.

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Fig. 1: The effect of warming on spider abundance and body size.
Fig. 2: The response of web-building behaviours to warming in large and small spider species.
Fig. 3: Prey species ordered by body size for both large and small spider species.
Fig. 4: Experimental warming decreased the body length of observed prey for large spiders and increased foraging success for small spiders.
Fig. 5: Experimental warming increases the abundance of prey shared by large and small spiders.
Fig. 6: Warming-induced bottom-up effects on the plant community cascade affect the abundance and behaviour of spiders.

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Data availability

Source data on physical environment, abundance and body size of spiders and their prey resulting from the warming experiment have been deposited in Zenodo (https://doi.org/10.5281/zenodo.10379963). Source data on spider density and web height resulting from an independent field survey have been deposited in Zenodo (https://doi.org/10.5281/zenodo.10380144).

Code availability

R scripts for statistical analyses have been deposited in Zenodo (https://doi.org/10.5281/zenodo.103799421).

References

  1. Meineke, E. K., Holmquist, A. J., Wimp, G. M. & Frank, S. D. Changes in spider community composition are associated with urban temperature, not herbivore abundance. J. Urban Ecol. 3, 1–8 (2017).

    Article  Google Scholar 

  2. Riddell, E. A., Odom, J. P., Damm, J. D. & Sears, M. W. Plasticity reveals hidden resistance to extinction under climate change in the global hotspot of salamander diversity. Sci. Adv. 4, eaar5471 (2018).

  3. Verdeny-Vilalta, O. & Moya-Laraño, J. Seeking water while avoiding predators: moisture gradients can affect predator–prey interactions. Anim. Behav. 90, 101–108 (2014).

    Article  Google Scholar 

  4. Post, E., Peterson, R. O., Stenseth, N. C. & McLaren, B. E. Ecosystem consequences of wolf behavioural response to climate. Nature 401, 9905–9907 (1999).

    Article  Google Scholar 

  5. Antiqueira, P. A. P., Petchey, O. L. & Romero, G. Q. Warming and top predator loss drive ecosystem multifunctionality. Ecol. Lett. 21, 72–82 (2017).

    Article  PubMed  Google Scholar 

  6. Ross, S. R. P.-J. et al. Predators mitigate the destabilising effects of heatwaves on multitrophic stream communities. Glob. Change Biol. 28, 403–416 (2022).

    Article  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  8. IPCC. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  9. Araújo, M. B. & Rahbek, C. How does climate change affect biodiversity? Science 313, 1396–1397 (2006).

    Article  PubMed  Google Scholar 

  10. Lawler, J. J. et al. Projected climate-induced faunal change in the western hemisphere. Ecology 90, 588–597 (2009).

    Article  PubMed  Google Scholar 

  11. Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gruner, D. S. et al. Effects of experimental warming on biodiversity depend on ecosystem type and local species composition. Oikos 126, 8–17 (2017).

    Article  ADS  Google Scholar 

  13. Roy, J. et al. Ecotrons: Powerful and versatile ecosystem analysers for ecology, agronomy and environmental science. Glob. Change Biol. 27, 1387–1407 (2021).

    Article  ADS  CAS  Google Scholar 

  14. Hollister, R. D. et al. A review of open top chamber (OTC) performance across the ITEX Network. Arct. Sci. 9, 331–344 (2023).

    Article  Google Scholar 

  15. Guo, K., Hao, S.-G., Sun, O. J. & Kang, L. Differential responses to warming and increased precipitation among three contrasting grasshopper species. Glob. Change Biol. 15, 2539–2548 (2009).

    Article  ADS  Google Scholar 

  16. Han, Z. et al. Effects of simulated climate warming on the population dynamics of Sitobion avenae (Fabricius) and its parasitoids in wheat fields. Pest Manag. Sci. 75, 3252–3259 (2019).

    Article  ADS  CAS  PubMed  Google Scholar 

  17. Tilman, D. & Kareiva, P. Spatial Ecology (Princeton Univ. Press, 1997).

  18. Pruitt, J. N., Demes, K. W. & Dittrich‐Reed, D. R. Temperature mediates shifts in individual aggressiveness, activity level, and social behavior in a spider. Ethology 117, 318–325 (2011).

    Article  Google Scholar 

  19. Byrne, M. P. & O'Gorman, P. A. Understanding decreases in land relative humidity with global warming: conceptual model and GCM simulations. J. Climate 29, 9045–9061 (2016).

    Article  ADS  Google Scholar 

  20. Vollrath, F. & Edmonds, D. Modulation of the mechanical properties of spider silk by coating with water. Nature 340, 305–307 (1989).

    Article  ADS  Google Scholar 

  21. Horne, C. R., Hirst, A. G. & Atkinson, D. Seasonal body size reductions with warming covary with major body size gradients in arthropod species. Proc. R. Soc. B 284, 20170238 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lindo, Z. Warming favours small-bodied organisms through enhanced reproduction and compositional shifts in belowground systems. Soil Biol. Biochem. 91, 271–278 (2015).

    Article  CAS  Google Scholar 

  23. Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).

    Article  ADS  CAS  PubMed  Google Scholar 

  24. Hu, X., Zhou, W., Li, X., Niklas, K. J. & Sun, S. Changes in community composition induced by experimental warming in an alpine meadow: beyond plant functional type. Front. Ecol. Evol. 9, 569422 (2021).

    Article  Google Scholar 

  25. Feeny, P. in Recent Advances in Phytochemistry (eds Wallace, J. W. & Mansell, R. L.) 1–40 (Plenum Press, 1976).

  26. Vasconcellos-Neto, J., Messas, Y. F., Souza, H. D. S., Villanueva-Bonilla, G. A. & Romero, G. Q. in Behaviour and Ecology of Spiders: Contributions from the Neotropical Region (eds. Viera, C. & Gonzaga, O. M.) 165–214 (Springer, 2017).

  27. Bale, S. J. et al. Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Glob. Change Biol. 8, 1–16 (2002).

    Article  ADS  Google Scholar 

  28. Villalpando, S. N., Williams, R. S. & Norby, R. J. Elevated air temperature alters an old‐field insect community in a multifactor climate change experiment. Glob. Change Biol. 15, 930–942 (2009).

    Article  ADS  Google Scholar 

  29. Barton, B. T. & Ives, A. R. Direct and indirect effects of warming on aphids, their predators, and ant mutualists. Ecology 95, 1479–1484 (2014).

    Article  PubMed  Google Scholar 

  30. Williamson, C., Grad, G., Lange, H. D., Gilroy, S. & Karapelou, D. Temperature‐dependent ultraviolet responses in zooplankton: implications of climate change. Limnol. Oceanogr. 47, 1844–1848 (2002).

    Article  ADS  Google Scholar 

  31. Daufresne, M., Lengfellner, K. & Sommer, U. Global warming benefits the small in aquatic ecosystems. Proc. Natl Acad. Sci. USA 106, 12788–12793 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  32. Robinson, S. I., McLaughlin, Ó. B., Marteinsdóttir, B. & O’Gorman, E. J. Soil temperature effects on the structure and diversity of plant and invertebrate communities in a natural warming experiment. J. Anim. Ecol. 87, 634–646 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Atkinson, D. Temperature and organism size: a biological law for ectotherms? Adv. Ecol. Res. 25, 1–58 (1994).

    Article  Google Scholar 

  34. Smith, J. J., Hasiotis, S. T., Kraus, M. J. & Woody, D. T. Transient dwarfism of soil fauna during the Paleocene–Eocene thermal maximum. Proc. Natl Acad. Sci. USA 106, 17655–17660 (2009).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zeuthen, E. Oxygen uptake as related to body size in organisms. Q. Rev. Biol. 28, 1–12 (1953).

    Article  CAS  PubMed  Google Scholar 

  36. Portner, H. O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97 (2007).

    Article  ADS  PubMed  Google Scholar 

  37. Liu, H. et al. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proc. Natl Acad. Sci. USA 115, 4051–4056 (2018).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ganjurjav, H. et al. Differential response of alpine steppe and alpine meadow to climate warming in the central Qinghai–Tibetan Plateau. Agric. Meteorol. 223, 233–240 (2015).

    Article  Google Scholar 

  39. Tilman, D. & Elhaddi, A. Drought and biodiversity in grasslands. Oecologia 89, 257–264 (1992).

    Article  ADS  CAS  PubMed  Google Scholar 

  40. Massey, P. F., Ennos, A. R. & Hartley, E. S. Silica in grasses as a defence against insect herbivores: contrasting effects on folivores and a phloem feeder. J. Anim. Ecol. 75, 595–603 (2006).

    Article  PubMed  Google Scholar 

  41. Tscharntke, T. & Greiler, H. J. Insect communities, grasses, and grasslands. Annu. Rev. Entomol. 40, 535–558 (1995).

    Article  CAS  Google Scholar 

  42. Wise, D. H. & Barata, J. L. Prey of two synoptic spiders with different web structures. J. Arachnol. 11, 271–281 (1983).

    Google Scholar 

  43. Opell, B. D. Material investment and prey capture potential of reduced spider webs. Behav. Ecol. Sociobiol. 26, 375–381 (1990).

    Article  Google Scholar 

  44. Sherman, P. M. The orb-web: an energetic and behavioural estimator of a spider’s dynamic foraging and reproductive strategies. Anim. Behav. 48, 19–34 (1994).

    Article  Google Scholar 

  45. Herberstein, M. E. et al. The effect of feeding history on prey capture behaviour in the orb-web spider Argiope keyserlingi Karsch (Araneae: Araneidae). Ethology 104, 565–571 (1998).

    Article  Google Scholar 

  46. Blamires, S. J., Thompson, M. B. & Hochuli, D. F. Habitat selection and web plasticity by the orb spider Argiope keyserlingi (Argiopidae): do they compromise foraging success for predator avoidance? Austral Ecol. 32, 551–563 (2007).

    Article  Google Scholar 

  47. Rall, B. C., Vucic‐Pestic, O., Ehnes, R. B., Emmerson, M. & Brose, U. Temperature, predator–prey interaction strength and population stability. Glob. Change Biol. 16, 2145–2157 (2010).

    Article  ADS  Google Scholar 

  48. Eberhard, W. G. Function and phylogeny of spider webs. Annu. Rev. Ecol. Syst. 21, 341–372 (1990).

    Article  Google Scholar 

  49. Tariel, J., Pleenet, S. & Luquet, E. Transgenerational plasticity in the context of predator-prey interactions. Front. Ecol. Evol. 8, 548660 (2020).

    Article  Google Scholar 

  50. Kawecki, J. T. Evolutionary ecology of learning: insights from fruit flies. Popul. Ecol. 52, 15–25 (2010).

    Article  Google Scholar 

  51. Quarles, W. Global warming means more pests. IPM Pract. 29, 1–8 (2007).

    Google Scholar 

  52. Schwarz, B. et al. Warming alters energetic structure and function but not resilience of soil food webs. Nat. Clim. Change 7, 895–900 (2017).

    Article  ADS  Google Scholar 

  53. Barnes, A. D. et al. Energy flux: The link between multitrophic biodiversity and ecosystem functioning. Trends Ecol. Evol. 53, 186–197 (2018).

  54. Xi, X., Griffin, J. N. & Sun, S. Grasshoppers amensalistically suppress caterpillar performance and enhance plant biomass in an alpine meadow. Oikos 122, 1049–1057 (2013).

    Article  ADS  Google Scholar 

  55. Wu, X., Duffy, J. E., Reich, P. B. & Sun, S. A brown-world cascade in the dung decomposer food web of an alpine meadow: effects of predator interactions and warming. Ecol. Monogr. 81, 313–328 (2011).

    Article  Google Scholar 

  56. Xi, X. et al. Experimental reduction of plant abundance changes interaction frequency of a tri‐trophic micro‐food web: contrasting responses of generalists and specialists. J. Ecol. 108, 415–423 (2019).

    Article  Google Scholar 

  57. Hu, J. L. Spiders in Qinghai-Tibet Plateau of China (Henan Science and Technology Publishing House, 2001).

  58. Wilson, A. G. & Tamura, G. T. Stack Effect and Building Design (National Research Council Canada, 1968).

  59. Taylor, L. R. Insect migration, flight periodicity and the boundary layer. J. Anim. Ecol. 43, 225–238 (1974).

    Article  Google Scholar 

  60. Taylor, L. R., Brown, E. S. & Llttlewood, S. C. The effect of size on the height of flight of migrant moths. Bull. Entomol. Res. 69, 605–609 (1979).

    Article  Google Scholar 

  61. IPCC: Summary for Policymakers. In Climate Change 2022 (Cambridge Univ. Press, 2022).

  62. Hu, X., Zhou, W. & Sun, S. Responses of plant reproductive phenology to winter-biased warming in an alpine meadow. Front. Plant Sci. 11, 534703 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Pauli, H. The GLORIA Field Manual—Standard Multi-summit Approach, Supplementary Methods, and Extra Approaches (Austrian Academy of Sciences and University of Natural Resources and Life Sciences, 2015).

  64. Sweet, D., Lorente, M., Valenzuel, A., Lorente, J. A. & Alvarez, J. C. Increasing DNA extraction yield from saliva stains with a modified Chelex method. Forensic Sci. Int. 83, 167–177 (1996).

    Article  CAS  PubMed  Google Scholar 

  65. Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplifcation of mitochondrial cytochrome C oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).

    CAS  PubMed  Google Scholar 

  66. Turnbull, A. J. Ecology of the true spiders (Araneomorphae). Annu. Rev. Entomol. 18, 305–348 (1973).

    Article  Google Scholar 

  67. Turnbull, A. L. Spider predators of the spruce budworm Choristoneura fumiferana (Clem.), at Lillooet, British Columbia (Univ. of British of Columbia, 1953).

  68. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.R-project.org/

  69. Dormann, C. F., Gruber, B. & Fruend, J. Introducing the bipartite package: analysing ecological networks. R News 8/2, 8–11 (2008).

    Google Scholar 

  70. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

    Article  Google Scholar 

  71. Hothorn, T., Hornik, K., van de Wiel, M. A. & Zeileis, A. A Lego system for conditional inference. Am. Stat. 60, 257–263 (2012).

    Article  MathSciNet  Google Scholar 

  72. 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 

  73. Oksanen, J. et al. Vegan: community ecology package. The comprehensive R Archive Network https://cran.r-project.org/web/packages/vegan/vegan.pdf (2022).

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Acknowledgements

We thank X. Xi, W. Zhou, L. Zhang and N. Liu for field and laboratory assistance, K. Niu, J. Lai, S. Zhang and M. Xu for data analysis, and the Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station and the High Performance Computing Center of Nanjing University for platform support. This work was supported by National Science Foundation of China (32071605 and 31530007). N.E. acknowledges support from iDiv funded by the German Research Foundation (DFG– FZT 118, 202548816) and funding by DFG (Ei 862/29-1 and Ei 862/31-1). P.B.R. acknowledges support for the Institute for Global Change Biology by the Biosciences Initiative at University of Michigan and the NSF Biological Integration Institute programme (NSF-DBI 2021898).

Author information

Authors and Affiliations

  1. Department of Ecology, School of Life Sciences, Nanjing University, Nanjing, China

    Xiaoli Hu, Xinwei Wu & Shucun Sun

  2. Sichuan Zoige Alpine Wetland Ecosystem National Observation and Research Station and Institute of Qinghai-Tibetan Plateau, Southwest Minzu University, Chengdu, China

    Qingping Zhou

  3. School of Integrative Plant Science, Cornell University, Ithaca, NY, USA

    Karl J. Niklas

  4. School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA

    Lin Jiang

  5. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

    Nico Eisenhauer

  6. Institute of Biology, Leipzig University, Leipzig, Germany

    Nico Eisenhauer

  7. Institute for Global Change Biology and School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA

    Peter B. Reich

  8. Department of Forest Resources, University of Minnesota, St Paul, MN, USA

    Peter B. Reich

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  1. Xiaoli Hu
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Contributions

S.S. and X.W. conceived and designed the methodology. X.H. and Q.Z. collected the data. X.H. and S.S. analysed the data. K.J.N., P.B.R., L.J. and N.E. led the writing of the manuscript. All authors contributed critically to the drafts and gave final approval for publication.

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Correspondence to Xinwei Wu or Shucun Sun.

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Extended data

Extended Data Fig. 1 The warming effect on plant aboveground biomass and relative biomass of graminoids.

Aboveground biomass (a), relative biomass of Graminoids (b) in both non-warmed (blue) and warmed (red) chambers from 2017 to 2020. The data are shown as mean ± s.e.m.; n = 3. The linear mixed model with treatment as fix effect and chamber identity as random effect was used for statistical analysis. The asterisk represents significant differences (*** P < 0.001). The statistical parameters (t and P value) were shown in Supplementary Table 4. The data from 2017 to 2018 were published in Frontiers in Ecology and Evolution (Hu et al. 24).

Extended Data Fig. 2 The variation in soil moisture during the experimental period.

The soil moisture (−5 cm) in the August (the driest month each year) of 2017 to 2019 (data deficiency in 2020 because of the logger malfunctioning).

Extended Data Fig. 3 The variation in precipitation intensity during the experimental period.

The precipitation in May to August during experimental period from 2017 to 2020.

Extended Data Fig. 4 Plant seed production unchanged by the chamber setting.

Number of sound seeds per fruit (a) and seed set ratio (b) of four dicot species (that is, Anemone rivularis, Saussurea nigrescens, Silene aprica, and Delphinium caeruleum) in the field (yellow), non-warmed (blue) and warmed chambers (red). The data are shown as mean ± s.e.m. Sample size (n) is the same for both panels A and B, as provided under the panel A for each treatment and each species. The treatment effects (natural, non-warmed and warmed) was determined by generalized linear mixed model with treatment as fixed effect as well as ‘species’ and ‘chamber identity’ as random effects. The treatment (that is chamber setting) did not significantly affected the number of sound seed (GLMM with negative binomial error distribution: χ² = 0.89, d.f = 2, P = 0.64) and seed set ratio (GLMM with binomial error distribution: χ² = 4.44, d.f = 2, P = 0.11).

Extended Data Fig. 5 The variation in air temperature during the experimental period.

The temperature variation (daily mean) during the experimental period (2017–2020).

Extended Data Fig. 6 Hypothesized structural equation model.

Full hypothesized structural equation models used in the AIC model selections.

Supplementary information

Supplementary Information

Supplementary Tables 1–6 and Figs. 1 and 2.

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Hu, X., Wu, X., Zhou, Q. et al. Warming causes contrasting spider behavioural responses by changing their prey size spectra. Nat. Clim. Chang. 14, 190–197 (2024). https://doi.org/10.1038/s41558-023-01918-8

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