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:

Macroclimate data overestimate range shifts of plants in response to climate change

Nature Climate Change volume 13, pages 484–490 (2023)Cite this article

Subjects

Abstract

Current conservation policy has been shaped by the expectation that, for many species, places with suitable climate will lie outside their current range, thus leading to predictions of numerous extinctions. Here we show that the magnitude of range shifts is often overestimated as climate data used do not reflect the microclimatic conditions that many organisms experience. We model the historic (1977–1995) distributions of 244 heathland and grassland plant taxa using both macro- and microclimate data and project these distributions to present day (2003–2021). Whereas macroclimate models predicted major range shifts (median 14 km shift), microclimate models predicted localized shifts, generally of less than 1 km, into favourable microclimates that more closely match observed patterns of establishment and extirpation. Thus, improving protection of refugial populations within species’ existing geographic range may, for species living in environments exposed to sunlight, be more effective than assisted translocations and overhaul of protected area networks.

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

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

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

Fig. 1: Microclimate and range shifts.
Fig. 2: Range shifts predicted using macro- and microclimate data.
Fig. 3: Suitability predicted using macro- and microclimate.
Fig. 4: Shifts in climatically suitable conditions predicted using macro- and microclimate data.

Similar content being viewed by others

Data availability

The global gridded 0.5° climate dataset is available from https://crudata.uea.ac.uk/cru/data/hrg/. The UK gridded climate dataset is available from https://www.metoffice.gov.uk/research/climate/maps-and-data/data/haduk-grid/datasets. Datasets required to generate the 100-m-resolution gridded climate dataset for the Lizard Peninsula are included with R package microclima16 available from https://github.com/ilyamaclean/microclima. Plant distribution datasets required to run the models are published online38. Forest-cover datasets are publicly available from UKCEH53 and https://land.copernicus.eu/pan-european/corine-land-cover.

Code availability

All data and code used for the analysis are available on request from the corresponding author and are published online38. The microclima16 R package is available from https://github.com/ilyamaclean/microclima.

References

  1. Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).

    Article  Google Scholar 

  2. Dawson, T. P., Jackson, S. T., House, J. I., Prentice, I. C. & Mace, G. M. Beyond predictions: biodiversity conservation in a changing climate. Science 332, 53–58 (2011).

    Article  CAS  Google Scholar 

  3. Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).

    Article  CAS  Google Scholar 

  4. 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).

    Article  CAS  Google Scholar 

  5. Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).

    Article  Google Scholar 

  6. Lenoir, J. & Svenning, J. C. Climate‐related range shifts—a global multidimensional synthesis and new research directions. Ecography 38, 15–28 (2015).

    Article  Google Scholar 

  7. Thomas, C. D. Translocation of species, climate change, and the end of trying to recreate past ecological communities. Trends Ecol. Evol. 26, 216–221 (2011).

    Article  Google Scholar 

  8. Heller, N. E. & Zavaleta, E. S. Biodiversity management in the face of climate change: a review of 22 years of recommendations. Biol. Conserv. 142, 14–32 (2009).

    Article  Google Scholar 

  9. Bramer, I. et al. Advances in monitoring and modelling climate at ecologically relevant scales. Adv. Ecol. Res. 58, 101–161 (2018).

    Article  Google Scholar 

  10. Maclean, I. M. D., Bennie, J. J., Scott, A. J. & Wilson, R. J. A high-resolution model of soil and surface water conditions. Ecol. Modell. 237, 109–119 (2012).

    Article  Google Scholar 

  11. Maclean, I. M. D., Suggitt, A. J., Wilson, R. J., Duffy, J. P. & Bennie, J. J. Fine‐scale climate change: modelling spatial variation in biologically meaningful rates of warming. Glob. Change Biol. 23, 256–268 (2017).

    Article  Google Scholar 

  12. Potter, K. A., Arthur Woods, H. & Pincebourde, S. Microclimatic challenges in global change biology. Glob. Change Biol. 19, 2932–2939 (2013).

    Article  Google Scholar 

  13. Trivedi, M. R., Berry, P. M., Morecroft, M. D. & Dawson, T. P. Spatial scale affects bioclimate model projections of climate change impacts on mountain plants. Glob. Change Biol. 14, 1089–1103 (2008).

    Article  Google Scholar 

  14. Randin, C. F. et al. Climate change and plant distribution: local models predict high‐elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).

    Article  Google Scholar 

  15. Dobrowski, S. Z. A climatic basis for microrefugia: the influence of terrain on climate. Glob. Change Biol. 17, 1022–1035 (2011).

    Article  Google Scholar 

  16. Maclean, I. M. D., Mosedale, J. R. & Bennie, J. J. Microclima: an R package for modelling meso‐and microclimate. Methods Ecol. Evol. 10, 280–290 (2019).

    Article  Google Scholar 

  17. Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).

    Article  Google Scholar 

  18. Hollis, D., McCarthy, M., Kendon, M., Legg, T. & Simpson, I. HadUK‐Grid—a new UK dataset of gridded climate observations. Geosci. Data J. 6, 151–159 (2019).

    Article  Google Scholar 

  19. GBIF.org. GBIF Occurrence Data Downloads historic (1977–1995): https://doi.org/10.15468/dl.38928w and recent: (2003–2021) https://doi.org/10.15468/dl.5cdeuy Accessed from R via rgbif (https://github.com/ropensci/rgbif) on 2022-07-31.

  20. Preston, C. D., Pearman, D. & Dines, T. D. New Atlas of the British & Irish Flora (Oxford Univ. Press, 2002).

  21. Bennallick, I. et al. Red Data Book for Cornwall and the Isles of Scilly 2nd edn (Croceago Press, 2009).

  22. Haesen, S. et al. ForestTemp–sub‐canopy microclimate temperatures of European forests. Glob. Change Biol. 27, 6307–6319 (2021).

    Article  CAS  Google Scholar 

  23. Blonder, B. et al. Extreme and highly heterogeneous microclimates in selectively logged tropical forests. Front. For. Glob. Change 1, 5 (2018).

    Article  Google Scholar 

  24. Marsh, C. D. et al. Measuring and modelling microclimatic air temperature in a historically degraded tropical forest. Int. J. Biometeorol. 66, 1283–1295 (2022).

    Article  Google Scholar 

  25. Lenoir, J., Hattab, T. & Pierre, G. Climatic microrefugia under anthropogenic climate change: implications for species redistribution. Ecography 40, 253–266 (2017).

    Article  Google Scholar 

  26. Hickling, R. et al. The distributions of a wide range of taxonomic groups are expanding polewards. Glob. Change Biol. 12, 450–455 (2006).

    Article  Google Scholar 

  27. Bertrand, R. et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature 479, 517–520 (2011).

    Article  CAS  Google Scholar 

  28. Bertrand, R. et al. Ecological constraints increase the climatic debt in forests. Nat. Commun. 7, 12643 (2016).

    Article  CAS  Google Scholar 

  29. Lembrechts, J. J. & Lenoir, J. Microclimatic conditions anywhere at any time! Glob. Change Biol. 26, 337–339 (2020).

    Article  Google Scholar 

  30. Gillingham, P., Huntley, B., Kunin, W. & Thomas, C. The effect of spatial resolution on projected responses to climate warming. Divers. Distrib. 18, 990–1000 (2012).

    Article  Google Scholar 

  31. Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).

    Article  Google Scholar 

  32. Araújo, M. B., Alagador, D., Cabeza, M., Nogués‐Bravo, D. & Thuiller, W. Climate change threatens European conservation areas. Ecol. Lett. 14, 484–492 (2011).

    Article  Google Scholar 

  33. Greenwood, O., Mossman, H. L., Suggitt, A. J., Curtis, R. J. & Maclean, I. M. D. Using in situ management to conserve biodiversity under climate change. J. Appl. Ecol. 53, 885–894 (2016).

    Article  Google Scholar 

  34. R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).

  35. Kearney, M. R., Gillingham, P. K., Bramer, I., Duffy, J. P. & Maclean, I. M. D. A method for computing hourly, historical, terrain-corrected microclimate anywhere on Earth. Methods Ecol. Evol. 11, 38–43 (2020).

    Article  Google Scholar 

  36. Kalnay, E. et al. The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc. 77, 437–472 (1996).

    Article  Google Scholar 

  37. Kearney, M. R. & Porter, W. P. NicheMapR—an R package for biophysical modelling: the microclimate model. Ecography 40, 664–674 (2017).

    Article  Google Scholar 

  38. Maclean, I. M. D. & Early R. R code and data to accompany macroclimate data over-estimate range shifts of plants in response to climate change. Zenodo https://doi.org/10.5281/zenodo.7221995 (2022).

  39. Nychka, D., Furrer, R., Paige, J. & Sain, S. fields: Tools for Spatial Data https://doi.org/10.5065/D6W957CT (UCAR, 2015).

  40. 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 

  41. Xu, T. & Hutchinson, M. ANUCLIM version 6.1 User Guide (Fenner School of Environment and Society, The Australian Natl Univ., 2011).

  42. Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).

    Article  Google Scholar 

  43. Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C. & Guisan, A. Selecting predictors to maximize the transferability of species distribution models: lessons from cross‐continental plant invasions. Glob. Ecol. Biogeogr. 26, 275–287 (2017).

    Article  Google Scholar 

  44. Hughes, A. C. et al. Sampling biases shape our view of the natural world. Ecography 44, 1259–1269 (2021).

    Article  Google Scholar 

  45. Baker, D. A., Maclean, I. M. D., Goodall, M. & Gaston, K. J. Correlations between spatial sampling biases and environmental niches affect species distribution models. Glob. Ecol. Biogeogr. 31, 1038–1050 (2022).

    Article  Google Scholar 

  46. Brown, J. L. SDM toolbox: a python‐based GIS toolkit for landscape genetic, biogeographic and species distribution model analyses. Methods Ecol. Evol. 5, 694–700 (2014).

    Article  Google Scholar 

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

    Article  Google Scholar 

  48. Hastie, T. J. & Tibshirani, R. J. Generalized Additive Models (CRC Press, 1990).

  49. Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Article  Google Scholar 

  50. F Dormann, C. et al. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30, 609–628 (2007).

    Article  Google Scholar 

  51. Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).

    Article  Google Scholar 

  52. Büttner, G. CORINE Land Cover and Land Cover Change Products. In Land Use and Land Cover Mapping in Europe (eds Manakos, I. & Braun, M. 55–74 (Springer, 2014).

  53. Land Cover Map 2020 (UKCEH Environmental Information Data Centre, 2020).

  54. Di Cola, V. et al. ecospat: an R package to support spatial analyses and modeling of species niches and distributions. Ecography 40, 774–787 (2017).

    Article  Google Scholar 

  55. Pebesma, E. & Bivand, R. Classes and methods for spatial data in R. R News 5, 9–13 (2005).

    Google Scholar 

Download references

Acknowledgements

We thank K. Gaston for helpful comments. I.M.D.M. was supported by the Natural Environment Research Council (NE/L00268X/1).

Author information

Authors and Affiliations

  1. Environment and Sustainability Institute, University of Exeter Penryn Campus, Cornwall, UK

    Ilya M. D. Maclean

  2. Department of Ecology and Conservation, University of Exeter Penryn Campus, Cornwall, UK

    Ilya M. D. Maclean & Regan Early

Authors
  1. Ilya M. D. Maclean
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Regan Early
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

I.M.D.M. and E.R. conceived and designed the study. I.M.D.M. performed the numerical analysis and wrote the paper with contributions from E.R. Both authors discussed and interpreted results.

Corresponding author

Correspondence to Ilya M. D. Maclean.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Climate Change thanks Jonas Lembrechts, Jonathan Lenoir, Martin Macek and Helen Slater for their contribution to the peer review of this work.

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 Range shifts predicted using macro- and microclimate data.

Here results are shown without forest grid cells masked. Top: how far taxa would need to move in order to track climatic changes. Here the mean distances between each grid cell predicted to be occupied historically (1977–1995) and the nearest location with climatically suitable conditions under recent condition (2003–2021) is predicted using (a) 0.5° and (b) 5 km grid resolution macroclimate data and (c) 100 m grid resolution microclimate data for each of 244 plant taxa. Bottom: here the magnitude and direction of the shift in the suitability-weighted centroid of each taxa’s distribution is shown, modelled at (d) 0.5°, (e) 5 km resolution and (f) 100 m resolution.

Extended Data Fig. 2 Range shifts predicted using macro- and microclimate data (forest cells not masked).

Mean distances between each grid cell predicted to be occupied recently (2003–2021) to the nearest location with climatically suitable conditions historically (1977–1995) predicted using (a) 0.5° and (b) 5 km grid resolution macroclimate data and (c) 100 m grid resolution microclimate data for each of 244 plant taxa.

Extended Data Fig. 3 Range shifts predicted using macro- and microclimate data (additional models).

a–d, Mean distances between each grid cell predicted to be occupied historically (1977–1995) and the nearest location with climatically suitable conditions under recent condition (2003–2021). e–h, The magnitude and direction of the shift in the suitability-weighted centroid of each taxa’s distribution. a,e, Results for Erica tetralix modelled at 100 m resolution at each of 73 sites. b,f, The results for all taxa modelled at 0.5° grid resolution, but using temperature estimates for 5 cm above ground. c,g, Results for models fitted across Europe and projected to the Lizard Peninsula (temperature estimates 5 cm above ground). d,h, Results for the Lizard Peninsula using temperature estimates at the height of a standard weather station.

Extended Data Fig. 4 Suitability predicted using macro- and microclimate (forest grid cells not masked).

In (a) and (b) the relationship between the current (2003–2021) and historic (1977–1995) predicted probability of occurrence on the Lizard Peninsula is shown for all taxa, demonstrating that when microclimate (green) data are used, the relationship is shallower than when macroclimate (purple) data are used. In consequence, when modelled using microclimate data, fewer extirpations are predicted. The green and purple dashed lines are the line-of-best fit for the modelled relationship for microclimate and macroclimate respectively. In (a) macroclimate occurrence is derived from 0.5° grid resolution models and in (b) from 5 km grid resolution models. In (c) the relationship between the current (2003–2021) and historic (1977–1995) predicted probability of occurrence of Erica tetralix is shown in every grid cell with a predicted probability of occurrence >0.1 across Europe (purple) and for each 100 m grid cell with a historic record in each of 73 40 × 40 km selected focal areas (green).

Supplementary information

Reporting Summary

Supplementary Data 1

Supplementary Tables 1–8.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Maclean, I.M.D., Early, R. Macroclimate data overestimate range shifts of plants in response to climate change. Nat. Clim. Chang. 13, 484–490 (2023). https://doi.org/10.1038/s41558-023-01650-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41558-023-01650-3

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