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Global wind patterns and the vulnerability of wind-dispersed species to climate change

Nature Climate Change volume 10, pages 868–875 (2020)Cite this article

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Abstract

The resilience of biodiversity in the face of climate change depends on gene flow and range shifts. For diverse wind-dispersed and wind-pollinated organisms, regional wind patterns could either facilitate or hinder these movements, depending on alignment of winds with spatial climate patterns. We map global variation in terrestrial wind regimes, and model how ‘windscape’ connectivity will shape inbound and outbound dispersal between sites and their predicted future climate analogs. This model predicts that wind-accessible, climatically analogous sites will be scarcer in locations such as the tropics and on the leeward sides of mountain ranges, implying that the wind-dispersed biota in these landscapes may be more vulnerable to future climate change. A case study of Pinus contorta illustrates species-specific patterns of predicted genetic rescue and range expansion facilitated by wind. This framework has implications across fields ranging from historical biogeography and landscape genetics to ecological forecasting and conservation planning.

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Fig. 1: Global wind patterns as characterized by three drivers of dispersal: prevailing wind direction, average wind speed and anisotropy.
Fig. 2: Prevailing wind alignment with temperature gradients.
Fig. 3: Example wind and climate change landscapes for one focal site.
Fig. 4: Modelled global patterns of downwind accessibility to outbound climate analogues.
Fig. 5: Case study of wind connectivity and climate resilience for lodgepole pine in western North America.

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Data availability

All the input data used in the study are publicly available. Source data that represent the results associated with each figure accompanies this paper.

Code availability

All R code used in the analysis has been deposited in the Zenodo data repository (https://doi.org/10.5281/zenodo.3860687)64, as has the source code for the version of the windscape R package developed and used for this study (https://doi.org/10.5281/zenodo.3857730)65.

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Acknowledgements

M.K. was funded by a US National Science Foundation Graduate Research Fellowship. We thank B. Collins for providing access to wind data from CMIP5 simulations.

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Authors and Affiliations

  1. Department of Integrative Biology, University of California, Berkeley, CA, USA

    Matthew M. Kling & David D. Ackerly

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Contributions

M.K. conceived the study, conducted the analyses and drafted the manuscript. D.A. contributed discussion and feedback on the study design and edited the manuscript.

Corresponding author

Correspondence to Matthew M. Kling.

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The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Gil Bohrer, Frank Schurr and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Global patterns of alignment between prevailing wind direction and temperature gradients.

a, Prevailing local wind direction, that is the bearing at which wind-dispersed organisms are expected to move on average. b, Direction of temperature gradient descent, that is the local direction in which organisms will need to move to offset warming climate. c, The difference between these two directions, with 0° indicating migratory tailwinds (prevailing winds blow directly down the temperature gradient) and 180° indicating migratory headwinds (prevailing winds blow directly up the temperature gradient).

Source data

Extended Data Fig. 2 Global patterns of landscape overlap between windsheds and climate analogs.

Maps show the amount of climatically analogous area versus the proportion of that area that is wind-accessible within 250 km of each focal site, in the outbound (a), and inbound (b), directions. (Panel a presents the same data as Fig. 4c of the main text, and is repeated here for comparison.) Color represents the bivariate relationship between these variables c, with green and blue indicating wind facilitation and yellow and red indicating wind hindrance. Additional scatterplots (d–f) compare the amount of similar climate, the amount of wind-accessible area, and the amount of wind-climate overlap in the forward versus reverse directions. Extreme outliers are rescaled in panel f for visual purposes only.

Extended Data Fig. 3 Global patterns of wind facilitation of climate change tracking.

Maps show wind facilitation for the landscape within 250km of each terrestrial grid cell, in the inbound (a), and outbound (b), directions, and with respect to major geographic gradients (c–e). In the scatterplots, latitude represents absolute latitude.

Extended Data Fig. 4 Global patterns of wind-climate overlap.

Maps show overlap for the landscape within 250km of each terrestrial grid cell, in the inbound (a), and outbound (b), directions, and with respect to major geographic gradients (c–e). In the scatterplots, latitude represents absolute latitude.

Extended Data Fig. 5 Global patterns of wind facilitation ‘syndromes’.

Sites can be assigned continuous rankings or discrete categories representing four alternative syndromes: wind facilitation, directional hindrance, speed hindrance, or climate limitation. a, Sites are ranked by climate availability, wind facilitation, and directional alignment (collapsed z-axis differentiating red from yellow) to assign relative membership in each of the four syndromes. b, Examples of each syndrome, with colors representing climate similarity, wind accessibility, and their areas of overlap across the 250 km radius landscapes surrounding each central origin cell. c,e, Syndrome prevalence by latitude in the inbound and outbound directions, respectively; syndromes are categorized to place 25% of global land area in each category, along the dotted lines depicted in panel a. d,f, Global map of syndromes in the inbound and outbound directions, respectively, with colors representing a continuous gradient among the four categories as depicted in panel a.

Source data

Extended Data Fig. 6 Global patterns of wind accessibility.

Maps show the mean wind accessibility of landscapes within 250km of each terrestrial grid cell, in the inbound (a), and outbound (b), directions, and with respect to major geographic gradients (c–e). In the scatterplots, latitude represents absolute latitude.

Extended Data Fig. 7 Global patterns of climate analog availability.

Maps show analog availability within 250km of each terrestrial grid cell, in the inbound (a), and outbound (b), directions, and with respect to major geographic gradients (c–e). In the scatterplots, latitude represents absolute latitude.

Supplementary information

Supplementary Information

Supplementary Discussion and Figs. 1–8.

Supplementary Data

Source data for Fig. 4 and Extended Data Figs. 2–4, 6 and 7 representing global patterns of windscape and climate change results.

Source data

Source Data Fig. 1

Spatial data for wind regimes.

Source Data Fig. 5

Spatial data for Pinus contorta results.

Source Data Extended Data Fig. 1

Spatial data for wind gradient alignment results.

Source Data Extended Data Fig. 5

Spatial data for wind facilitation syndrome results.

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Kling, M.M., Ackerly, D.D. Global wind patterns and the vulnerability of wind-dispersed species to climate change. Nat. Clim. Chang. 10, 868–875 (2020). https://doi.org/10.1038/s41558-020-0848-3

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