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

  • 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).

    Google Scholar 

  • 2.

    Hampe, A. Plants on the move: the role of seed dispersal and initial population establishment for climate-driven range expansions. Acta Oecol. 37, 666–673 (2011).

    Google Scholar 

  • 3.

    Kremer, A. et al. Long‐distance gene flow and adaptation of forest trees to rapid climate change. Ecol. Lett. 15, 378–392 (2012).

    Google Scholar 

  • 4.

    Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).

    Google Scholar 

  • 5.

    Felicísimo, Á. M., Muñoz, J. & González-Solis, J. Ocean surface winds drive dynamics of transoceanic aerial movements. PLoS ONE 3, e2928 (2008).

    Google Scholar 

  • 6.

    Gillespie, R. G. et al. Long-distance dispersal: a framework for hypothesis testing. Trends Ecol. Evol. 27, 47–56 (2012).

    Google Scholar 

  • 7.

    Muñoz, J., Felicísimo, Á. M., Cabezas, F., Burgaz, A. R. & Martínez, I. Wind as a long-distance dispersal vehicle in the Southern Hemisphere. Science 304, 1144–1147 (2004).

    Google Scholar 

  • 8.

    Smith, D. J. et al. Intercontinental dispersal of bacteria and archaea by transpacific winds. Appl. Environ. Microbiol. 79, 1134–1139 (2013).

    CAS  Google Scholar 

  • 9.

    Austerlitz, F., Dutech, C., Smouse, P. E., Davis, F. & Sork, V. L. Estimating anisotropic pollen dispersal: a case study in Quercus lobata. Heredity 99, 193–204 (2007).

    CAS  Google Scholar 

  • 10.

    Bullock, J. M. & Clarke, R. T. Long distance seed dispersal by wind: measuring and modelling the tail of the curve. Oecologia 124, 506–521 (2000).

    CAS  Google Scholar 

  • 11.

    Gassmann, M. I. & Pérez, C. F. Trajectories associated to regional and extra-regional pollen transport in the southeast of Buenos Aires province, Mar del Plata (Argentina). Int. J. Biometeorol. 50, 280–291 (2006).

    Google Scholar 

  • 12.

    Skarpaas, O. & Shea, K. Dispersal patterns, dispersal mechanisms, and invasion wave speeds for invasive thistles. Am. Naturalist 170, 421–430 (2007).

    Google Scholar 

  • 13.

    Wang, Z. F. et al. Pollen and seed flow under different predominant winds in wind-pollinated and wind-dispersed species Engelhardia roxburghiana. Tree Genet. Genomes 12, 19 (2016).

    CAS  Google Scholar 

  • 14.

    Soubeyrand, S., Enjalbert, J., Sanchez, A. & Sache, I. Anisotropy, in density and in distance, of the dispersal of yellow rust of wheat: experiments in large field plots and estimation. Phytopathology 97, 1315–1324 (2007).

    CAS  Google Scholar 

  • 15.

    Born, C., le Roux, P. C., Spohr, C., McGeoch, M. A. & van Vuuren, B. J. Plant dispersal in the sub‐Antarctic inferred from anisotropic genetic structure. Mol. Ecol. 21, 184–194 (2012).

    Google Scholar 

  • 16.

    Geremew, A., Woldemariam, M. G., Kefalew, A., Stiers, I. & Triest, L. Isotropic and anisotropic processes influence fine-scale spatial genetic structure of a keystone tropical plant. AoB Plants 10, plx076 (2018).

    Google Scholar 

  • 17.

    Brown, J. K. & Hovmøller, M. S. Aerial dispersal of pathogens on the global and continental scales and its impact on plant disease. Science 297, 537–541 (2002).

    CAS  Google Scholar 

  • 18.

    Vanschoenwinkel, B., Gielen, S., Seaman, M. & Brendonck, L. Any way the wind blows—frequent wind dispersal drives species sorting in ephemeral aquatic communities. Oikos 117, 125–134 (2008).

    Google Scholar 

  • 19.

    Ahmed, S., Compton, S. G., Butlin, R. K. & Gilmartin, P. M. Wind-borne insects mediate directional pollen transfer between desert fig trees 160 kilometers apart. Proc. Natl Acad. Sci. USA 106, 20342–20347 (2009).

    CAS  Google Scholar 

  • 20.

    Larson-Johnson, K. Field observations of Carpinus (Betulaceae) demonstrate high dispersal asymmetry and inform migration simulations with implications for times of rapid climate change. Int. J. Plant Sci. 177, 389–399 (2016).

    Google Scholar 

  • 21.

    Nathan, R. et al. Spread of North American wind‐dispersed trees in future environments. Ecol. Lett. 14, 211–219 (2011).

    Google Scholar 

  • 22.

    Sorte, C. J. Predicting persistence in a changing climate: flow direction and limitations to redistribution. Oikos 122, 161–170 (2013).

    Google Scholar 

  • 23.

    Loarie, S. R. et al. The velocity of climate change. Nature 462, 1052–1055 (2009).

    CAS  Google Scholar 

  • 24.

    Molinos, J. G., Burrows, M. T. & Poloczanska, E. S. Ocean currents modify the coupling between climate change and biogeographical shifts. Sci. Rep. 7, 1332 (2017).

    Google Scholar 

  • 25.

    Higgins, S. I. et al. Forecasting plant migration rates: managing uncertainty for risk assessment. J. Ecol. 91, 341–347 (2003).

    Google Scholar 

  • 26.

    Bullock, J. M. et al. Modelling spread of British wind‐dispersed plants under future wind speeds in a changing climate. J. Ecol. 100, 104–115 (2012).

    Google Scholar 

  • 27.

    Kuparinen, A., Katul, G., Nathan, R. & Schurr, F. M. Increases in air temperature can promote wind-driven dispersal and spread of plants. Proc. R. Soc. B 276, 3081–3087 (2009).

    Google Scholar 

  • 28.

    Davis, H. G., Taylor, C. M., Lambrinos, J. G. & Strong, D. R. Pollen limitation causes an Allee effect in a wind-pollinated invasive grass (Spartina alterniflora). Proc. Natl Acad. Sci. USA 101, 13804–13807 (2004).

    CAS  Google Scholar 

  • 29.

    Dullinger, S., Dirnböck, T. & Grabherr, G. Patterns of shrub invasion into high mountain grasslands of the Northern Calcareous Alps, Austria. Arct. Antarct. Alp. Res. 35, 434–441 (2003).

    Google Scholar 

  • 30.

    Payette, S. The range limit of boreal tree species in Québec-Labrador: an ecological and palaeoecological interpretation. Rev. Palaeobot. Palynol. 79, 7–30 (1993).

    Google Scholar 

  • 31.

    Sandel, B., Monnet, A. C., Govaerts, R. & Vorontsova, M. Late Quaternary climate stability and the origins and future of global grass endemism. Ann. Bot. 119, 279–288 (2016).

    Google Scholar 

  • 32.

    Svenning, J. C. & Skov, F. Could the tree diversity pattern in Europe be generated by postglacial dispersal limitation? Ecol. Lett. 10, 453–460 (2007).

    Google Scholar 

  • 33.

    Schurr, F. M. et al. Colonization and persistence ability explain the extent to which plant species fill their potential range. Glob. Ecol. Biogeogr. 16, 449–459 (2007).

    Google Scholar 

  • 34.

    Saha, S. et al. The NCEP Climate Forecast System Reanalysis. Bull. Am. Meteorol. Soc. 91, 1015–1058 (2010).

    Google Scholar 

  • 35.

    Hamann, A., Roberts, D. R., Barber, Q. E., Carroll, C. & Nielsen, S. E. Velocity of climate change algorithms for guiding conservation and management. Glob. Change Biol. 21, 997–1004 (2015).

    Google Scholar 

  • 36.

    Kling, M. M., Auer, S. L., Comer, P. J., Ackerly, D. D. & Hamilton, H. Multiple axes of ecological vulnerability to climate change. Glob. Change Biol. 26, 2798–2813 (2020).

    Google Scholar 

  • 37.

    Keeley, A. T. et al. New concepts, models, and assessments of climate-wise connectivity. Environ. Res. Lett. 13, 073002 (2018).

    Google Scholar 

  • 38.

    Savage, D., Barbetti, M. J., MacLeod, W. J., Salam, M. U. & Renton, M. Timing of propagule release significantly alters the deposition area of resulting aerial dispersal. Diversity Distrib. 16, 288–299 (2010).

    Google Scholar 

  • 39.

    Nathan, R. et al. Long‐distance biological transport processes through the air: can nature’s complexity be unfolded in silico? Divers. Distrib. 11, 131–137 (2005).

    Google Scholar 

  • 40.

    Zeller, K. A., McGarigal, K. & Whiteley, A. R. Estimating landscape resistance to movement: a review. Landsc. Ecol. 27, 777–797 (2012).

    Google Scholar 

  • 41.

    Treml, E. A., Halpin, P. N., Urban, D. L. & Pratson, L. F. Modeling population connectivity by ocean currents, a graph-theoretic approach for marine conservation. Landsc. Ecol. 23, 19–36 (2008).

    Google Scholar 

  • 42.

    Fernández‐López, J. & Schliep, K. rWind: download, edit and include wind data in ecological and evolutionary analysis. Ecography 42, 804–810 (2019).

    Google Scholar 

  • 43.

    Thompson, S. & Katul, G. Plant propagation fronts and wind dispersal: an analytical model to upscale from seconds to decades using superstatistics. Am. Naturalist 171, 468–479 (2008).

    Google Scholar 

  • 44.

    Savage, D., Barbetti, M. J., MacLeod, W. J., Salam, M. U. & Renton, M. Can mechanistically parameterised, anisotropic dispersal kernels provide a reliable estimate of wind-assisted dispersal? Ecol. Model. 222, 1673–1682 (2011).

    Google Scholar 

  • 45.

    Regal, P. J. Pollination by wind and animals: ecology of geographic patterns. Annu. Rev. Ecol. Syst. 13, 497–524 (1982).

    Google Scholar 

  • 46.

    Carroll, C., Lawler, J. J., Roberts, D. R. & Hamann, A. Biotic and climatic velocity identify contrasting areas of vulnerability to climate change. PLoS ONE 10, e0140486 (2015).

    Google Scholar 

  • 47.

    Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).

    Google Scholar 

  • 48.

    Ackerly, D. D. et al. The geography of climate change: implications for conservation biogeography. Divers. Distrib. 16, 476–487 (2010).

    Google Scholar 

  • 49.

    Owens, J. N. The Reproductive Biology of Lodgepole Pine Extension Note 07 (Forest Genetics Council of British Columbia, 2006).

  • 50.

    Bontrager, M. & Angert, A. L. Gene flow improves fitness at a range edge under climate change. Evol. Lett. 3, 55–68 (2019).

    Google Scholar 

  • 51.

    Sexton, J. P., Strauss, S. Y. & Rice, K. J. Gene flow increases fitness at the warm edge of a species’ range. Proc. Natl Acad. Sci. USA 108, 11704–11709 (2011).

    CAS  Google Scholar 

  • 52.

    Rehfeldt, G. E., Ying, C. C., Spittlehouse, D. L. & Hamilton, D. A. Jr Genetic responses to climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecol. Monogr. 69, 375–407 (1999).

    Google Scholar 

  • 53.

    Wang, T., O’Neill, G. A. & Aitken, S. N. Integrating environmental and genetic effects to predict responses of tree populations to climate. Ecol. Appl. 20, 153–163 (2010).

    CAS  Google Scholar 

  • 54.

    Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Google Scholar 

  • 55.

    Dobrowski, S. Z. et al. The climate velocity of the contiguous United States during the 20th century. Glob. Change Biol. 19, 241–251 (2013).

    Google Scholar 

  • 56.

    van Etten, J. R Package gdistance: distances and routes on geographical grids. J. Stat. Softw. 76, 1–21 (2017).

    Google Scholar 

  • 57.

    IPCC Special Report on Global Warming of 1.5°C (eds Masson-Delmotte, V. et al.) (WMO, 2018).

  • 58.

    Schleussner, C. F. et al. Differential climate impacts for policy-relevant limits to global warming: the case of 1.5 °C and 2 °C. Earth Syst. Dyn. 7, 327–351 (2016).

    Google Scholar 

  • 59.

    Little, E. L. Jr Atlas of United States Trees. Volume 1, Conifers and Important Hardwoods Miscellaneous Publication 1146 (US Department of Agriculture, 1971).

  • 60.

    Wang, T., Hamann, A., Yanchuk, A., O’Neill, G. A. & Aitken, S. N. Use of response functions in selecting lodgepole pine populations for future climates. Glob. Change Biol. 12, 2404–2416 (2006).

    Google Scholar 

  • 61.

    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).

    Google Scholar 

  • 62.

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).

    Google Scholar 

  • 63.

    R Core Team (2017). R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017); https://www.R-project.org/

  • 64.

    Kling, M. M. & Ackerly, D. D. Scripts and Data used in ‘Global Wind Patterns and the Vulnerability of Wind-Dispersed Species to Climate Change (Zenodo Repository, 2020); https://doi.org/10.5281/zenodo.3860687

  • 65.

    Kling, M. M. Windscape R Package v.1.0.0 (Zenodo Repository, 2020); https://doi.org/10.5281/zenodo.3857730


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