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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).

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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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