Abstract
Understanding how species respond to climate change is critical for forecasting the future dynamics and distribution of pests, diseases and biological diversity1,2,3. Although ecologists have long acknowledged species’ direct physiological and demographic responses to climate, more recent work suggests that these direct responses can be overwhelmed by indirect effects mediated via other interacting community members2,3,4,5,6,7. Theory suggests that some of the most dramatic impacts of community change will probably arise through the assembly of novel species combinations after asynchronous migrations with climate8,9,10. Empirical tests of this prediction are rare, as existing work focuses on the effects of changing interactions between competitors that co-occur today7,11,12,13,14,15. To explore how species’ responses to climate warming depend on how their competitors migrate to track climate, we transplanted alpine plant species and intact plant communities along a climate gradient in the Swiss Alps. Here we show that when alpine plants were transplanted to warmer climates to simulate a migration failure, their performance was strongly reduced by novel competitors that could migrate upwards from lower elevation; these effects generally exceeded the impact of warming on competition with current competitors. In contrast, when we grew the focal plants under their current climate to simulate climate tracking, a shift in the competitive environment to novel high-elevation competitors had little to no effect. This asymmetry in the importance of changing competitor identity at the leading versus trailing range edges is best explained by the degree of functional similarity between current and novel competitors. We conclude that accounting for novel competitive interactions may be essential to predict species’ responses to climate change accurately.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2012)
Gilman, S. E., Urban, M. C., Tewksbury, J., Gilchrist, G. W. & Holt, R. D. A framework for community interactions under climate change. Trends Ecol. Evol. 25, 325–331 (2010)
Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008)
Davis, A. J., Jenkinson, L. S., Lawton, J. H., Shorrocks, B. & Wood, S. Making mistakes when predicting shifts in species range in response to global warming. Nature 391, 783–786 (1998)
González-Megías, A. & Menéndez, R. Climate change effects on above- and below-ground interactions in a dryland ecosystem. Phil. Trans. R. Soc. B 367, 3115–3124 (2012)
Liancourt, P. et al. Plant response to climate change varies with topography, interactions with neighbors, and ecotype. Ecology 94, 444–453 (2013)
Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007)
Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013)
Urban, M. C., Tewksbury, J. J. & Sheldon, K. S. On a collision course: competition and dispersal differences create no-analogue communities and cause extinctions during climate change. Proc. R. Soc. B 279, 2072–2080 (2012)
Williams, J. W. et al. Model systems for a no-analog future: species associations and climates during the last deglaciation. Ann. NY Acad. Sci. 1297, 29–43 (2013)
Adler, P. B., Dalgleish, H. J. & Ellner, S. P. Forecasting plant community impacts of climate variability and change: when do competitive interactions matter? J. Ecol. 100, 478–487 (2012)
Adler, P. B., Leiker, J. & Levine, J. M. Direct and indirect effects of climate change on a prairie plant community. PLoS One 4, e6887 (2009)
Farrer, E. C., Ashton, I. W., Knape, J. & Suding, K. N. Separating direct and indirect effects of global change: a population dynamic modeling approach using readily available field data. Glob. Change Biol. 20, 1238–1250 (2014)
Levine, J. M., McEachern, A. K. & Cowan, C. Do competitors modulate rare plant response to precipitation change? Ecology 91, 130–140 (2010)
Milazzo, M., Mirto, S., Domenici, P. & Gristina, M. Climate change exacerbates interspecific interactions in sympatric coastal fishes. J. Anim. Ecol. 82, 468–477 (2013)
HilleRisLambers, J., Harsch, M. A., Ettinger, A. K., Ford, K. R. & Theobald, E. J. How will biotic interactions influence climate change-induced range shifts? Ann. NY Acad. Sci. 1297, 112–125 (2013)
Wisz, M. S. et al. The role of biotic interactions in shaping distributions and realised assemblages of species: implications for species distribution modelling. Biol. Rev. Camb. Philos. Soc. 88, 15–30 (2013)
CH2011. Swiss Climate Change Scenarios CH2011 (C2SM, MeteoSwiss, ETH Zurich, NCCR Climate and OcCC, 2011)
Elmendorf, S. C. et al. Experiment, monitoring, and gradient methods used to infer climate change effects on plant communities yield consistent patterns. Proc. Natl Acad. Sci. USA 112, 448–452 (2015)
Svenning, J.-C. et al. The influence of interspecific interactions on species range expansion rates. Ecography 37, 1198–1209 (2014)
Chen, I.-C., Hill, J. K., Ohlemüller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011)
Gottfried, M. et al. Continent-wide response of mountain vegetation to climate change. Nature Clim. Change 2, 111–115 (2012)
Adler, P. B., Fajardo, A., Kleinhesselink, A. R. & Kraft, N. J. B. Trait-based tests of coexistence mechanisms. Ecol. Lett. 16, 1294–1306 (2013)
Freckleton, R. P. & Watkinson, A. R. Predicting competition coefficients for plant mixtures: reciprocity, transitivity and correlations with life-history traits. Ecol. Lett. 4, 348–357 (2001)
Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems (Springer, 2003)
R Development Core Team. R: a language and environment for statistical computing v.3.1.3 (R foundation for statistical computing, 2015)
Pérez-Harguindeguy, N. et al. New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013)
Zimmermann, N. E. & Kienast, F. Predictive mapping of alpine grasslands in switzerland: Species versus community approach. J. Veg. Sci. 10, 469–482 (1999)
Hintze, C. et al. D3: the Dispersal and Diaspore database – baseline data and statistics on seed dispersal. Perspect. Plant Ecol. Evol. Syst. 15, 180–192 (2013)
Tamme, R. et al. Predicting species’ maximum dispersal distances from simple plant traits. Ecology 95, 505–513 (2014)
Acknowledgements
We thank M.-J. Mächler, D. Righetti, C. Schmid, P. Stettler, R. Guidon, A. Vitra, S. Minneboo, J. Leuenberger and other members of the Plant Ecology group for assistance with field work, and the community of Haldenstein for providing field sites. S. Güsewell provided statistical advice. We thank P. Adler, J. HilleRisLambers and the Plant Ecology group for reading and commenting on the manuscript. ETH Zurich funding to the Plant Ecology group supported the project.
Author information
Authors and Affiliations
Contributions
All authors designed the study, assisted with fieldwork and wrote the paper. J.M.A. analysed the data and wrote the first draft.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Extended data figures and tables
Extended Data Figure 2 Effect of novel competitors on alpine plant biomass in 2013.
Focal species were exposed to different competition scenarios, depending on whether they and/or their surrounding community would either migrate, or fail to migrate, following climate warming (see Fig. 1). Shown are means (s.e.m.) of the raw data, and likelihood ratio tests (d.f. = 1, n = 182 (a) and 221 (b), n = 10 experimental units (blocks) per site) of the novel competitor effect at each experimental site (in the main model, across all species and sites: novel competitor × site interaction χ2 = 8.42, d.f. = 1, P = 0.004; novel competitor × site × species interaction χ2 = 3.17, d.f. = 3, P = 0.367).
Extended Data Figure 3 Biomass in 2014 of four alpine plant species growing on soils without competition.
Plants grew under a warmer climate (a, at 1,400 m) or under their current climate (b, at 2,000 m), either on soil from that site, or on soil from a site 600 m higher up the mountain slope. Shown are means (s.e.m.) of the raw data (total n = 314).
Extended Data Figure 4 Effect of soil biota on plant biomass.
Plants grew on soils inoculated with soil biota from 1,400 or 2,000 m. Plants grew better with soil biota originating from lower elevation, but this effect was shared across species from 2,000 m (in yellow, focal species from the field experiment) and 1,400 m (orange). Thus how fast the 1,400 m soil biota migrate or rise to dominance at higher elevation in the future may not strongly determine the relative performance of 1,400 and 2,000 m plants. Shown are means (s.e.m.) of standardized plant biomass. For statistics and n see Extended Data Table 3.
Extended Data Figure 5 Above-ground community biomass.
Standing biomass was estimated in late summer 2013 (a) and 2014 (b) in the plant communities from sites at 1,400, 2,000 and 2,600 m (mean ± s.e.m., n = 10 per community and site), growing in sites at either 1,400 m, 2,000 m or 2,600 m.
Rights and permissions
About this article
Cite this article
Alexander, J., Diez, J. & Levine, J. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015). https://doi.org/10.1038/nature14952
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nature14952
This article is cited by
-
Transnational conservation to anticipate future plant shifts in Europe
Nature Ecology & Evolution (2024)
-
Microclimate and forest density drive plant population dynamics under climate change
Nature Climate Change (2023)
-
Ecophysiological adaptations shape distributions of closely related trees along a climatic moisture gradient
Nature Communications (2023)
-
Thermophilisation of communities differs between land plant lineages, land use types and elevation
Scientific Reports (2023)
-
Exploring how disturbance and light availability shape the elevation ranges of multiple mountain tree and shrub species in the tropics
Landscape Ecology (2023)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.