Ecology

1.
Daws, M. I., Mullins, C. E., Burslem, D. F. R. P., Paton, S. R. & Dalling, J. W. Topographic position affects the water regime in a semideciduous tropical forest in Panamá. Plant Soil 238, 79–89. https://doi.org/10.1023/A:1014289930621 (2002).
CAS  Article  Google Scholar 
2.
Sundqvist, M. K., Sanders, N. J. & Wardle, D. A. Community and ecosystem responses to elevational gradients: processes, mechanisms, and insights for global change. Annu. Rev. Ecol. Evol. Syst. 44, 261–280. https://doi.org/10.1146/annurev-ecolsys-110512-135750 (2013).
Article  Google Scholar 

3.
Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000. https://doi.org/10.1111/ele.12964 (2018).
Article  PubMed  PubMed Central  Google Scholar 

4.
Moeslund, J. E., Arge, L., Bøcher, P. K., Dalgaard, T. & Svenning, J.-C. Topography as a driver of local terrestrial vascular plant diversity patterns. Nord. J. Bot. 31, 129–144. https://doi.org/10.1111/j.1756-1051.2013.00082.x (2013).
Article  Google Scholar 

5.
Holland, P. G. & Steyn, D. G. Vegetational responses to latitudinal variations in slope angle and aspect. J. Biogeogr. 2, 179–183. https://doi.org/10.2307/3037989 (1975).
Article  Google Scholar 

6.
Yetemen, O., Istanbulluoglu, E. & Duvall, A. R. Solar radiation as a global driver of hillslope asymmetry: Insights from an ecogeomorphic landscape evolution model. Water Resour. Res. 51, 9843–9861. https://doi.org/10.1002/2015wr017103 (2015).
ADS  Article  Google Scholar 

7.
Bennie, J., Hill, M. O., Baxter, R. & Huntley, B. Influence of slope and aspect on long-term vegetation change in British chalk grasslands. J. Ecol. 94, 355–368. https://doi.org/10.1111/j.1365-2745.2006.01104.x (2006).
Article  Google Scholar 

8.
Cantlon, J. E. Vegetation and microclimates on north and south slopes of Cushetunk Mountain, New Jersey. Ecol. Monogr. 23, 241–270. https://doi.org/10.2307/1943593 (1953).
Article  Google Scholar 

9.
Warren, R. J. Mechanisms driving understory evergreen herb distributions across slope aspects: as derived from landscape position. Plant Ecol. 198, 297–308. https://doi.org/10.1007/s11258-008-9406-1 (2008).
Article  Google Scholar 

10.
Bennie, J., Huntley, B., Wiltshire, A., Hill, M. O. & Baxter, R. Slope, aspect and climate: Spatially explicit and implicit models of topographic microclimate in chalk grassland. Ecol. Model. 216, 47–59. https://doi.org/10.1016/j.ecolmodel.2008.04.010 (2008).
Article  Google Scholar 

11.
Burnett, B. N., Meyer, G. A. & McFadden, L. D. Aspect-related microclimatic influences on slope forms and processes, northeastern Arizona. J. Geophys. Res. Earth Surf. https://doi.org/10.1029/2007jf000789 (2008).
Article  Google Scholar 

12.
Geroy, I. J. et al. Aspect influences on soil water retention and storage. Hydrol. Process. 25, 3836–3842. https://doi.org/10.1002/hyp.8281 (2011).
ADS  Article  Google Scholar 

13.
Huang, Y.-M., Liu, D. & An, S.-S. Effects of slope aspect on soil nitrogen and microbial properties in the Chinese Loess region. CATENA 125, 135–145. https://doi.org/10.1016/j.catena.2014.09.010 (2015).
CAS  Article  Google Scholar 

14.
Lozano-García, B., Parras-Alcántara, L. & Brevik, E. C. Impact of topographic aspect and vegetation (native and reforested areas) on soil organic carbon and nitrogen budgets in Mediterranean natural areas. Sci. Total Environ. 544, 963–970. https://doi.org/10.1016/j.scitotenv.2015.12.022 (2016).
ADS  CAS  Article  PubMed  Google Scholar 

15.
Rasmussen, C. & Tabor, N. J. Applying a quantitative pedogenic energy model across a range of environmental gradients. Soil Sci. Soc. Am. J. 71, 1719–1729. https://doi.org/10.2136/sssaj2007.0051 (2007).
ADS  CAS  Article  Google Scholar 

16.
Broxton, P. D., Troch, P. A. & Lyon, S. W. On the role of aspect to quantify water transit times in small mountainous catchments. Water Resour. Res. https://doi.org/10.1029/2008wr007438 (2009).
Article  Google Scholar 

17.
Casanova, M., Messing, I. & Joel, A. Influence of aspect and slope gradient on hydraulic conductivity measured by tension infiltrometer. Hydrol. Process. 14, 155–164. https://doi.org/10.1002/(sici)1099-1085(200001)14:1%3c155::aid-hyp917%3e3.0.co;2-j (2000).
ADS  Article  Google Scholar 

18.
Gutiérrez-Jurado, H. A., Vivoni, E. R., Istanbulluoglu, E. & Bras, R. L. Ecohydrological response to a geomorphically significant flood event in a semiarid catchment with contrasting ecosystems. Geophys. Res. Lett. https://doi.org/10.1029/2007gl030994 (2007).
Article  Google Scholar 

19.
Wang, L., Wei, S., Horton, R. & Shao, M. A. Effects of vegetation and slope aspect on water budget in the hill and gully region of the Loess Plateau of China. CATENA 87, 90–100. https://doi.org/10.1016/j.catena.2011.05.010 (2011).
Article  Google Scholar 

20.
Pook, E. & Moore, C. The influence of aspect on the composition and structure of dry sclerophyll forest on Black Mountain, Canberra. ACT. Aust. J. Bot. 14, 223–242. https://doi.org/10.1071/BT9660223 (1966).
Article  Google Scholar 

21.
Armesto, J. J. & Martίnez, J. A. Relations between vegetation structure and slope aspect in the Mediterranean region of Chile. J. Ecol. 66, 881–889. https://doi.org/10.2307/2259301 (1978).
Article  Google Scholar 

22.
Badano, E. I., Cavieres, L. A., Molina-Montenegro, M. A. & Quiroz, C. L. Slope aspect influences plant association patterns in the Mediterranean matorral of central Chile. J. Arid Environ. 62, 93–108. https://doi.org/10.1016/j.jaridenv.2004.10.012 (2005).
ADS  Article  Google Scholar 

23.
Zapata-Rios, X., Brooks, P. D., Troch, P. A., McIntosh, J. & Guo, Q. Influence of terrain aspect on water partitioning, vegetation structure and vegetation greening in high-elevation catchments in northern New Mexico. Ecohydrology 9, 782–795. https://doi.org/10.1002/eco.1674 (2016).
Article  Google Scholar 

24.
Poulos, H. M. & Camp, A. E. Topographic influences on vegetation mosaics and tree diversity in the Chihuahuan Desert Borderlands. Ecology 91, 1140–1151. https://doi.org/10.1890/08-1808.1 (2010).
Article  PubMed  Google Scholar 

25.
Kutiel, P. & Lavee, H. Efffect of slope aspect on soil and vegetation properties along an aridity transect. Isr. J. Plant Sci. 47, 169. https://doi.org/10.1080/07929978.1999.10676770 (1999).
Article  Google Scholar 

26.
Sternberg, M. & Shoshany, M. Influence of slope aspect on Mediterranean woody formations: comparison of a semiarid and an arid site in Israel. Ecol. Res. 16, 335–345. https://doi.org/10.1046/j.1440-1703.2001.00393.x (2001).
Article  Google Scholar 

27.
Méndez-Toribio, M., Meave, J. A., Zermeño-Hernández, I. & Ibarra-Manríquez, G. Effects of slope aspect and topographic position on environmental variables, disturbance regime and tree community attributes in a seasonal tropical dry forest. J. Veg. Sci. 27, 1094–1103. https://doi.org/10.1111/jvs.12455 (2016).
Article  Google Scholar 

28.
Gallardo-Cruz, J. A., Pérez-García, E. A. & Meave, J. A. β-Diversity and vegetation structure as influenced by slope aspect and altitude in a seasonally dry tropical landscape. Landsc. Ecol. 24, 473–482. https://doi.org/10.1007/s10980-009-9332-1 (2009).
Article  Google Scholar 

29.
Paudel, S. & Vetaas, O. R. Effects of topography and land use on woody plant species composition and beta diversity in an arid Trans-Himalayan landscape, Nepal. J. Mt. Sci. 11, 1112–1122. https://doi.org/10.1007/s11629-013-2858-3 (2014).
Article  Google Scholar 

30.
Zhang, R. The Dry Valley of the Hengduan Mountains Regions (Science Press, Beijing, 1992).
Google Scholar 

31.
Vegetation, E. B. O. S. Sichuan Vegetation (People’s Publishing House of Sichuan, Beijing, 1980).
Google Scholar 

32.
Guan, W. et al. Vegetation classification and the main types of vegetation of the dry valley of Minjiang River. J. Mt. Res. 22, 679–686 (2004).
Google Scholar 

33.
Ma, K.-M. et al. Multiple-scale soil moisture distribution and its implications for ecosystem restoration in an arid river valley, China. Land Degrad. Dev. 15, 75–85. https://doi.org/10.1002/ldr.584 (2004).
CAS  Article  Google Scholar 

34.
Lu, T., Ma, K. M., Zhang, W. H. & Fu, B. J. Differential responses of shrubs and herbs present at the Upper Minjiang River basin (Tibetan Plateau) to several soil variables. J. Arid Environ. 67, 373–390. https://doi.org/10.1016/j.jaridenv.2006.03.011 (2006).
ADS  Article  Google Scholar 

35.
Xu, X.-L., Ma, K.-M., Fu, B.-J., Song, C.-J. & Liu, W. Relationships between vegetation and soil and topography in a dry warm river valley, SW China. CATENA 75, 138–145. https://doi.org/10.1016/j.catena.2008.04.016 (2008).
Article  Google Scholar 

36.
Solon, J., Degórski, M. & Roo-Zielińska, E. Vegetation response to a topographical-soil gradient. CATENA 71, 309–320. https://doi.org/10.1016/j.catena.2007.01.006 (2007).
Article  Google Scholar 

37.
Birkeland, P. W. Soils and Geomorphology (Oxford University Press, Oxford, 1984).
Google Scholar 

38.
Loik, M. E., Breshears, D. D., Lauenroth, W. K. & Belnap, J. A multi-scale perspective of water pulses in dryland ecosystems: climatology and ecohydrology of the western USA. Oecologia 141, 269–281. https://doi.org/10.1007/s00442-004-1570-y (2004).
ADS  Article  PubMed  Google Scholar 

39.
Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220. https://doi.org/10.1007/s00442-004-1520-8 (2004).
ADS  Article  PubMed  Google Scholar 

40.
Fernandez-Going, B. M., Harrison, S., Anacker, B. & Safford, H. Climate interacts with soil to produce beta diversity in Californian plant communities. Ecology 94, 2007–2018. https://doi.org/10.1890/12-2011.1 (2013).
CAS  Article  PubMed  Google Scholar 

41.
Araya, Y. N., Gowing, D. J. & Dise, N. Does soil nitrogen availability mediate the response of grassland composition to water regime?. J. Veg. Sci. 24, 506–517. https://doi.org/10.1111/j.1654-1103.2012.01481.x (2013).
Article  Google Scholar 

42.
Araya, Y. N. et al. A fundamental, eco-hydrological basis for niche segregation in plant communities. New Phytol. 189, 253–258. https://doi.org/10.1111/j.1469-8137.2010.03475.x (2011).
Article  PubMed  Google Scholar 

43.
Li, Y. J., Bao, W. K. & Wu, N. Spatial patterns of the soil seed bank and extant vegetation across the dry Minjiang River valley in southwest China. J. Arid Environ. 75, 1083–1089. https://doi.org/10.1016/j.jaridenv.2011.05.012 (2011).
ADS  Article  Google Scholar 

44.
Li, F., Bao, W., Liu, J. & Wu, N. Eco-anatomical characteristics of Sophora davidii leaves along an elevation gradient in upper Minjiang River dry valley. Chin. J. Appl. Ecol. 17, 5–10 (2006).
Google Scholar 

45.
Li, F., Bao, W. & Zhu, L. Species diversity and spatial distribution of legumes in the dry valley of Minjiang River, SW China. J. Mt. Sci. 1, 76–84 (2010).
Google Scholar 

46.
Song, C. J. et al. Distribution patterns of shrubby N-fixers and non-N fixers in an arid valley in Southwest China: implications for ecological restoration. Ecol. Res. 25, 553–564. https://doi.org/10.1007/s11284-009-0685-3 (2010).
Article  Google Scholar 

47.
Liu, G. et al. Aboveground biomass of main shrubs in dry valley of Minjiang River. Acta Ecol. Sin. 23, 1757–1764. https://doi.org/10.1023/A:1022289509702 (2003).
Article  Google Scholar 

48.
Ellenberg, H. et al. Zeigerwerte von pflanzen in Mitteleuropa (1992).

49.
Shreve, F. Soil temperature as influenced by altitude and slope exposure. Ecology 5, 128–136. https://doi.org/10.2307/1929010 (1924).
Article  Google Scholar 

50.
Gutiérrez-Jurado, H. A. et al. On the observed ecohydrologic dynamics of a semiarid basin with aspect-delimited ecosystems. Water Resour. Res. 49, 8263–8284. https://doi.org/10.1002/2013wr014364 (2013).
Article  Google Scholar 

51.
Liu, G. et al. Distribution regulation of aboveground biomass of three main shrub types in the dry valley of Minjiang River. J. Mt. Sci. 21, 24–32 (2003).
Google Scholar 

52.
Pugnaire, F. I. & Luque, M. T. Changes in plant interactions along a gradient of environmental stress. Oikos 93, 42–49. https://doi.org/10.1034/j.1600-0706.2001.930104.x (2001).
Article  Google Scholar 

53.
Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430. https://doi.org/10.1038/416427a (2002).
ADS  CAS  Article  Google Scholar 

54.
Sarr, D. A., Hibbs, D. E. & Huston, M. A. A hierarchical perspective of plant diversity. Q. Rev. Biol. 80, 187–212. https://doi.org/10.1086/433058 (2005).
Article  PubMed  Google Scholar 

55.
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853. https://doi.org/10.1038/35002501 (2000).
ADS  CAS  Article  PubMed  Google Scholar 

56.
Ricklefs, R. E. Community diversity: relative roles of local and regional processes. Science 235, 167–171. https://doi.org/10.1126/science.235.4785.167 (1987).
ADS  CAS  Article  PubMed  Google Scholar 

57.
Pang, X. Y., Bao, W. K. & Ning, W. U. Reasons of dry valley climate characteristic and its formation reason in upstream of Minjiang River. Resour. Environ. Yangtze Basin 17, 46–53 (2008).
Google Scholar 

58.
Minchin, P. R. An evaluation of the relative robustness of techniques for ecological ordination. Vegetatio 69, 89–107. https://doi.org/10.1007/BF00038690 (1987).
Article  Google Scholar 

59.
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18, 117–143. https://doi.org/10.1111/j.1442-9993.1993.tb00438.x (1993).
Article  Google Scholar 

60.
Oksanen, J. et al.vegan: Community Ecology Package. R package version 2.5-2. 2018 (2018).

61.
Wang, Y. J., Huang, C. D., Zhang, J., Yang, W. Q. & Wang, X. S. Species Diversity, biomass and their relationship of shrubberies in an arid valley of the Minjiang River. Arid Zone Res. 27, 567–572. https://doi.org/10.3724/SP.J.1077.2010.01263 (2010).
CAS  Article  Google Scholar 

62.
Gotelli, N. J. & Colwell, R. K. Estimating species richness. In: Biological Diversity: Frontiers in Measurement and Assessment, Vol. 12 (eds Magurran, A. & McGill, B.) 39–54 (Oxford University Press, Oxford, 2011).
Google Scholar 

63.
Colwell, R. K. & Coddington, J. A. Estimating terrestrial biodiversity through extrapolation. Philos. Trans. R. Soc. Lond. B Biol. Sci. 345, 101–118 (1994).
ADS  CAS  Article  Google Scholar 

64.
Smith, E. P. & van Belle, G. Nonparametric estimation of species richness. Biometrics 40, 119–129 (1984).
Article  Google Scholar 

65.
Maurer, B. A. & McGill, B. J. In Biological Diversity: Frontiers in Measurement and Assessment (eds Magurran, A. E. & McGill, B. J.) 55–65 (Oxford University Press, Oxford, 2011).

66.
Fisher, R. A., Corbet, A. S. & Williams, C. B. The relation between the number of species and the number of individuals in a random sample of an animal population. J. Anim. Ecol. 12, 42–58 (1943).
Article  Google Scholar 

67.
Sørensen, T. A. A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish commons. K. Dan. Vidensk. Selsk. Biol. Skr. 5, 1–34 (1948).
Google Scholar 

68.
Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence-absence data. J. Anim. Ecol. 72, 367–382. https://doi.org/10.1046/j.1365-2656.2003.00710.x (2003).
Article  Google Scholar 

69.
Anderson, M. J. Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62, 245–253. https://doi.org/10.1111/j.1541-0420.2005.00440.x (2006).
MathSciNet  Article  PubMed  MATH  Google Scholar 

70.
Anderson, M. J., Ellingsen, K. E. & McArdle, B. H. Multivariate dispersion as a measure of beta diversity. Ecol. Lett. 9, 683–693. https://doi.org/10.1111/j.1461-0248.2006.00926.x (2006).
Article  PubMed  Google Scholar 

71.
R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2018). More

1.
Reynolds, M. P. (eds) Climate Change and Crop Production (CABI Publishing, 2010).
2.
Morales-Castilla, I. et al. Diversity buffers winegrowing regions from climate change losses. Proc. Natl Acad. Sci. USA 117, 2864–2869 (2020).
CAS  Article  Google Scholar 

3.
Nicotra, A. B. et al. Plant phenotypic plasticity in a changing climate. Trends Plant Sci. 15, 684–692 (2010).
CAS  Article  Google Scholar 

4.
Bellon, M. R., Hodson, D. & Hellin, J. Assessing the vulnerability of traditional maize seed systems in Mexico to climate change. Proc. Natl Acad. Sci. USA 108, 13432–13437 (2011).
CAS  Article  Google Scholar 

5.
Driedonks, N., Rieu, I. & Vriezen, W. H. Breeding for plant heat tolerance at vegetative and reproductive stages. Plant Reprod. 29, 67–79 (2016).
CAS  Article  Google Scholar 

6.
Mercer, K. L. & Perales, H. Evolutionary response of landraces to climate change in centers of crop diversity. Evol. Appl. 3, 480–493 (2010).
Article  Google Scholar 

7.
FAOSTAT (FAO, 2020); http://www.fao.org/faostat/en/#home

8.
Gibson, R., Mwanga, R. O. M., Namanda, S., Jeremiah, S. C. & Barker, I. Review of Sweetpotato Seed Systems in East and Southern Africa Working Paper 2009-1 (International Potato Center, 2009).

9.
Laurie, R. N., Laurie, S. M., Du Plooy, C. P., Finnie, J. F. & Van Staden, J. Yield of drought-stressed sweet potato in relation to canopy cover, stem length and stomatal conductance. J. Agric. Sci. 7, 201–214 (2015).
Google Scholar 

10.
Yang et al. High-throughput deep sequencing reveals the important role that microRNAs play in the salt response in sweet potato (Ipomoea batatas L.). BMC Genomics 21, 164 (2020).
CAS  Article  Google Scholar 

11.
Warren, J. F. Typhoons and droughts: food shortages and famine in the Philippines since the seventeenth century. Int. Rev. Environ. Hist. 4, 27–44 (2018).
Article  Google Scholar 

12.
Challinor, A. J. et al. A meta-analysis of crop yield under climate change and adaptation. Nat. Clim. Change 4, 287–291 (2014).
Article  Google Scholar 

13.
Roullier, C. et al. Disentangling the origins of cultivated sweet potato (Ipomoea batatas (L.) Lam.). PLoS ONE 8, e62707 (2013).
CAS  Article  Google Scholar 

14.
Kassali, R. Economics of sweet potato production. Int. J. Veg. Sci. 17, 313–321 (2011).
Article  Google Scholar 

15.
Omotobora, B. O., Adebola, P. O., Modise, D. M., Laurie, S. M. & Gerrano, A. S. Greenhouse and field evaluation of selected sweetpotato (Ipomoea batatas (L.) Lam.) accessions for drought tolerance in South Africa. Am. J. Plant Sci. 5, 3328–3339 (2014).
Article  Google Scholar 

16.
Woolfe, J. A. Sweetpotato: An Untapped Food Resource (Cambridge Univ. Press, 1992).

17.
Jayne, T. S., Villareal, M., Pingali, P. & Hemrich, G. Interactions between the Agricultural Sector and the HIV/AIDS Pandemic: Implications for Agricultural Policy ESA Working Paper No. 04-46 (FAO, 2004).

18.
Lebot, V. in Root and Tuber Crops (ed. Bradshaw, J. E.) 97–125 (Springer, 2010).

19.
Zhao et al. Temperature increase reduces global yields of major crops in four independent estimates. Proc. Natl Acad. Sci. USA 114, 9326–9331 (2017).
CAS  Article  Google Scholar 

20.
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).
CAS  Article  Google Scholar 

21.
Nangombe et al. Record-breaking climate extremes in Africa under stabilized 1.5 °C and 2 °C global warming scenarios. Nat. Clim. Change 8, 375–380 (2018).
Article  Google Scholar 

22.
Seneviratne, S. I. et al. in Special Report on Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (eds Field, C. B. et al.) 109–230 (Cambridge Univ. Press, 2012).

23.
O’sullivan et al. Thermal limits of leaf metabolism across biomes. Glob. Change Biol. 23, 209–223 (2017).
Article  Google Scholar 

24.
Tack, J., Lingenfelser, J. & Jagadish, S. K. Disaggregating sorghum yield reductions under warming scenarios exposes narrow genetic diversity in US breeding programs. Proc. Natl Acad. Sci. USA 114, 9296–9301 (2017).
CAS  Article  Google Scholar 

25.
Araújo et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
Article  Google Scholar 

26.
Singh, K. D. & Mandal, R. C. Performance of coleus and sweet potato in relation to seasonal variations, time of planting. J. Root Crops 2, 17–22 (1976).
Google Scholar 

27.
Gajanayake, B. R., Reddy, K., Shankle, M. W., Arancibia, R. A. & Villordon, A. Quantifying storage root initiation, growth, and developmental responses of sweetpotato to early season temperature. Agr. J. 106, 1795–1804 (2014).
Article  Google Scholar 

28.
Boeck, H. J. D., Velde, H. V. D., Groote, T. D. & Nijs, I. Ideas and perspectives: heat stress: more than hot air. Biogeosciences 13, 5821–5825 (2016).
Article  Google Scholar 

29.
Wahid, A., Gelani, S., Ashraf, M. & Foolad, M. Heat tolerance in plants: an overview. Environ. Exp. Bot. 61, 199–223 (2007).
Article  Google Scholar 

30.
Bita, C. & Gerats, T. Plant tolerance to high temperature in a changing environment: scientific fundamentals and production of heat stress-tolerant crops. Front. Plant Sci. 4, 273 (2013).
Article  Google Scholar 

31.
Jones, H. G. Plants and Microclimate: A Quantitative Approach to Environmental Plant Physiology (Cambridge Univ. Press, 2013).

32.
Dong, N., Prentice, I. C., Harrison, S. P., Song, Q. H. & Zhang, Y. P. Biophysical homoeostasis of leaf temperature: a neglected process for vegetation and land-surface modelling. Glob. Ecol. Biogeogr. 26, 998–1007 (2017).
Article  Google Scholar 

33.
Reynolds, M. P. et al. Evaluating physiological traits to complement empirical selection for wheat in warm environments. Euphytica 100, 84–95 (1998).
Article  Google Scholar 

34.
Park, S. et al. Orange protein has a role in phytoene synthase stabilization in sweetpotato. Sci. Rep. 6, 33563 (2016).
CAS  Article  Google Scholar 

35.
Pironon, S. et al. Potential adaptive strategies for 29 sub-Saharan crops under future climate change. Nat. Clim. Change 9, 758–763 (2019).
Article  Google Scholar 

36.
Herrera, J. M. et al. Lessons from 20 years of studies of wheat genotypes in multiple environments and under contrasting production systems. Front. Plant Sci. 10, 1745 (2020).
Article  Google Scholar 

37.
Khoury, C. K. et al. Distributions, ex situ conservation priorities, and genetic resource potential of crop wild relatives of sweetpotato [Ipomoea batatas (L.) Lam., I. series Batatas]. Front. Plant Sci. 6, 251 (2015).
Article  Google Scholar 

38.
Alwang, J. et al. Pathways from research on improved staple crop germplasm to poverty reduction for smallholder farmers. Agric. Sys. 172, 16–27 (2019).
Article  Google Scholar 

39.
Pilling, D., Bélanger, J. & Hoffmann, I. Declining biodiversity for food and agriculture needs urgent global action. Nat. Food 1, 144–147 (2020).
Article  Google Scholar 

40.
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).
CAS  Article  Google Scholar 

41.
Beck, H. et al. Present and future Köppen-Geiger climate classification maps at 1-km resolution. Sci. Data 5, 180214 (2018).
Article  Google Scholar 

42.
Patterson, H. D. & Williams, E. R. A new class of resolvable incomplete block designs. Biometrika 63, 83–92 (1976).
Article  Google Scholar 

43.
Kumar, A. et al. Improving the efficiency of wheat breeding experiments using alpha lattice design over randomized complete block design. Cereal Res. Commun. 48, 95–101 (2020).
Article  Google Scholar 

44.
Khan, M. et al. Comparative efficiency of alpha lattice design and complete randomized block design in wheat, maize and potato field trials. J. Res. Dev. Manag. 11, 115–118 (2015).
Google Scholar 

45.
Faye, E., Rebaudo, F., Yánez-Cajo, D., Cauvy-Fraunié, S. & Dangles, O. A toolbox for studying thermal heterogeneity across spatial scales: from unmanned aerial vehicle imagery to landscape metrics. Methods Ecol. Evol. 7, 437–446 (2016).
Article  Google Scholar 

46.
Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 

47.
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Clim. 37, 4302–4315 (2017).
Article  Google Scholar 

48.
Knox, J., Hess, T., Daccache, A. & Wheeler, T. Climate change impacts on crop productivity in Africa and South Asia. Environ. Res. Lett. 7, 034032 (2012).
Article  Google Scholar 

49.
Adoption of the Paris Agreement FCC/CP/2015/L.9/Rev.1 (UNFCCC, 2015). More

1.
Klicka, J. & Zink, R. M. Pleistocene effects on North American songbird evolution. Proc. R. Soc. B Biol. Sci. 266, 695–700 (1999).
Article  Google Scholar 
2.
Johnson, N. K. & Cicero, C. New mitochondrial DNA data affirm the importance of pleistocene speciation in North American birds. Evolution 58, 1122–1130 (2004).
PubMed  Article  Google Scholar 

3.
Avise, J. C. & Walker, D. Pleistocene phylogeographic effects on avian populations and the speciation process. Proc. R. Soc. B Biol. Sci. 265, 457–463 (1998).
CAS  Article  Google Scholar 

4.
April, J., Hanner, R. H., Dion-Côté, A.-M. & Bernatchez, L. Glacial cycles as an allopatric speciation pump in north-eastern American freshwater fishes. Mol. Ecol. 22, 409–422 (2013).
CAS  PubMed  Article  Google Scholar 

5.
Ribera, I. & Vogler, A. P. Speciation of Iberian diving beetles in Pleistocene refugia (Coleoptera, Dytiscidae). Mol. Ecol. 13, 179–193 (2004).
CAS  PubMed  Article  Google Scholar 

6.
Hewitt, G. Some genetic consequences of ice ages, and their role in divergence and speciation. Biol. J. Linn. Soc. 58, 247–276 (1996).
Article  Google Scholar 

7.
Hewitt, G. M. Genetic consequences of climatic oscillations in the Quaternary. . Philos. Trans. R. Soc. Lond., B Biol. Sci. 359, 183–195 (2004).
CAS  Article  Google Scholar 

8.
Hewitt, G. Post-glacial re-colonization of European biota. Biol. J. Linn. Soc. 68, 87–112 (1999).
Article  Google Scholar 

9.
Skrzecz, I. & Bulka, M. Insect assemblages in Norway spruce (Picea abies (L.) Karst.) stumps in the Eastern Sudetes. Folia For. Pol. Ser. A. 52, 98–107 (2010).
Google Scholar 

10.
Röder, J. et al. Arthropod species richness in the Norway Spruce (Picea abies (L.) Karst.) canopy along an elevation gradient. For. Ecol. Manage. 259, 1513–1521 (2010).
Article  Google Scholar 

11.
Tollefsrud, M. M. et al. Genetic consequences of glacial survival and postglacial colonization in Norway spruce: combined analysis of mitochondrial DNA and fossil pollen. Mol. Ecol. 17, 4134–4150 (2008).
CAS  PubMed  Article  Google Scholar 

12.
Latałowa, M. & van der Knaap, W. O. Late quaternary expansion of Norway spruce Picea abies (L.) Karst. in Europe according to pollen data. Quat. Sci. Rev. 25, 2780–2805 (2006).
ADS  Article  Google Scholar 

13.
Gałka, M. & Tobolski, K. Macrofossil evidence of early Holocene presence of Picea abies (Norway spruce) in NE Poland. Ann. Bot. Fenn. 3847, 129–141 (2013).
Article  Google Scholar 

14.
Dering, M. & Lewandowski, A. Finding the meeting zone: Where have the northern and southern ranges of Norway spruce overlapped?. For. Ecol. Manage. 259, 229–235 (2009).
Article  Google Scholar 

15.
Giesecke, T. & Bennett, K. D. The Holocene spread of Picea abies (L.) Karst. in Fennoscandia and adjacent areas. J. Biogeogr. 31, 1523–1548 (2004).
Article  Google Scholar 

16.
Taberlet, P., Fumagalli, L., Wust-Saucy, A. G. & Cosson, J. F. Comparative phylogeography and postglacial colonization routes in Europe. Mol. Ecol. 7, 453–464 (1998).
CAS  PubMed  Article  Google Scholar 

17.
Zając, A. & Zając, M. Atlas rozmieszczenia roślin naczyniowych w Polsce. (ed. Zając, A. & Zając, M) (Pracownia Chorologii Komputerowej Instytutu Botaniki Uniwersytetu Jagiellońskiego, 2001).

18.
Sallé, A., Arthofer, W., Lieutier, F., Stauffer, C. & Kerdelhué, C. Phylogeography of a host-specific insect: Genetic structure of Ips typographus in Europe does not reflect past fragmentation of its host. Biol. J. Linn. Soc. 90, 239–246 (2007).
Article  Google Scholar 

19.
Stauffer, C., Lakatos, F. & Hewitt, G. M. Phylogeography and postglacial colonization routes of Ips typographus L. (Coleoptera, Scolytidae). Mol. Ecol. 8, 763–773 (1999).
Article  Google Scholar 

20.
Bertheau, C. et al. Divergent evolutionary histories of two sympatric spruce bark beetle species. Mol. Ecol. 22, 3318–3332 (2013).
CAS  PubMed  Article  Google Scholar 

21.
Mayer, F. et al. Comparative multilocus phylogeography of two Palaearctic spruce bark beetles: influence of contrasting ecological strategies on genetic variation. Mol. Ecol. 24, 1292–1310 (2015).
PubMed  Article  Google Scholar 

22.
Schebeck, M. et al. Pleistocene climate cycling and host plant association shaped the demographic history of the bark beetle Pityogenes chalcographus. Sci. Rep. 8, 14207 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

23.
Forsse, E. & Solbreck, C. Migration in the bark beetle Ips typographus L: duration, timing and height of flight. Zeitschrift für Angewandte Entomologie 100, 47–57 (1985).
Article  Google Scholar 

24.
Putz, J., Vorwagner, E. M. & Hoch, G. Flight performance of Monochamus sartor and Monochamus sutor, potential vectors of the pine wood nematode. For. J. 62, 195–201 (2016).
Google Scholar 

25.
Kawai, M. et al. Genetic Structure of Pine Sawyer Monochamus alternatus (Coleoptera: Cerambycidae) Populations in Northeast Asia: Consequences of the Spread of Pine Wilt Disease. Environ. Entomol. 35, 569–579 (2006).
Article  Google Scholar 

26.
Shoda-Kagaya, E. Genetic differentiation of the pine wilt disease vector Monochamus alternatus (Coleoptera: Cerambycidae) over a mountain range – revealed from microsatellite DNA markers. Bull. Entomol. Res. 97, 167–174 (2007).
CAS  PubMed  Article  Google Scholar 

27.
Haran, J., Rousselet, J., Tellez, D., Roques, A. & Roux, G. Phylogeography of Monochamus galloprovincialis, the European vector of the pinewood nematode. J. Pest Sci. 2004(91), 247–257 (2018).
Article  Google Scholar 

28.
Koutroumpa, F. A., Rougon, D., Bertheau, C., Lieutier, F. & Roux-Morabito, G. Evolutionary relationships within European Monochamus (Coleoptera: Cerambycidae) highlight the role of altitude in species delineation. Biol. J. Linn. Soc. 109, 354–376 (2013).
Article  Google Scholar 

29.
Plewa, R. et al. Morphology, genetics and Wolbachia endosymbionts support distinctiveness of Monochamus sartor sartor and M. s. urussovii (Coleoptera: Cerambycidae). Arthropod Syst. Phylogeny 76, 123–135 (2018).
Google Scholar 

30.
Ballard, J. W. O. & Whitlock, M. C. The incomplete natural history of mitochondria. Mol. Ecol. 13, 729–744 (2004).
PubMed  Article  Google Scholar 

31.
Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).
CAS  PubMed  Article  Google Scholar 

32.
Bazin, E. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572 (2006).
ADS  CAS  PubMed  Article  Google Scholar 

33.
Plavil’shhikov, N. Zhuki-drovoseki, chast’3. Podsemejstvo Lamiinae, ch. 1. (ed. Plavil’shhikov, N.) 510–514 (Fauna SSSR. Zhestkokrylye, 1958).

34.
Sama, G. Atlas of the Cerambycidae of Europe and the Mediterranean Area (ed. Sama, G.) 98–99 (Vit Kabourek, 2002).

35.
Sama, G. & Löbl, L. Cerambycidae, Western Palaearctic taxa.In:Catalogue of Palaearctic Coleoptera. Volume 6. Chrysomeloidea (ed. Löbl, I. & Smetana, A.) 281–283 (Apollo Books, 2010).

36.
Wallin, H., Schroeder, M. & Kvamme, T. A review of the European species of Monochamus Dejean, 1821 (Coleoptera, Cerambycidae) -with a description of the genitalia characters. Nor. J. Entomol. 60, 11–38 (2013).
Google Scholar 

37.
Rossa, R., Goczał, J. & Tofilski, A. Within and between-species variation of wing venation in genus Monochamus (Coleoptera: Cerambycidae). J. Insect Sci. 16, 5 (2016).
PubMed  PubMed Central  Article  Google Scholar 

38.
Ravazzi, C. Late quaternary history of spruce in southern Europe. Rev. Palaeobot. Palynol. 120, 131–177 (2002).
Article  Google Scholar 

39.
Terhürne-Berson, R. Changing distribution patterns of selected conifers in the Quaternary of Europe caused by climatic variations.https://d-nb.info/975944576/34 (2005).

40.
Ralska-Jasiewiczowa, M Latałowa, M. et al. Late Glacial and Holocene history of vegetation in Poland based on isopollen maps(ed. Ralska-Jasiewiczowa, M Latałowa, M. et al.) 147–159 (W. Szafer Institute of Botany, Polish Academy of Sciences, 2004).

41.
Adams, D. C., Rohlf, F. J. & Slice, D. E. Geometric morphometrics: ten years of progress following the ‘revolution’. Ital. J. Zool. 71, 5–16 (2004).
Article  Google Scholar 

42.
Zelditch, M. L., Swiderski, D. L., Sheets, H. D. & Fink, W. L. Geometric Morphometrics for Biologist. A Primer(ed. Zelditch, M. L., Swiderski, D. L., Sheets, H. D. & Fink, W. L.). 263–490 (2004).

43.
Oleksa, A. & Tofilski, A. Wing geometric morphometrics and microsatellite analysis provide similar discrimination of honey bee subspecies. Apidologie 46, 49–60 (2015).
Article  Google Scholar 

44.
Hurtado-Burillo, M. et al. A geometric morphometric and microsatellite analyses of Scaptotrigona mexicana and S. pectoralis (Apidae: Meliponini) sheds light on the biodiversity of Mesoamerican stingless bees. J. Insect Conserv. 5, 753–763 (2016).
Article  Google Scholar 

45.
Haack, R. A. Exotic bark- and wood-boring Coleoptera in the United States: recent establishments and interceptions. Can. J. For. Res. 36, 269–288 (2006).
Article  Google Scholar 

46.
Shoda, E., Kubota, K. & Makihara, H. Geographical structuring of mitochondrial DNA in Semanotus japonicus (Coleoptera: Cerambycidae). Appl. Entomol. Zool. 38, 339–345 (2003).
CAS  Article  Google Scholar 

47.
Shoda, E., Kubota, K. & Makihara, H. Geographical structure of morphological characters in Semanotus japonicus (Coleoptera: Cerambycidae) in Japan. Appl. Entomol. Zool. 38, 369–377 (2003).
Article  Google Scholar 

48.
Rossa, R., Goczał, J., Pawliczek, B. & Ohbayashi, N. Hind wing variation in Leptura annularis complex among European and Asiatic populations (Coleoptera, Cerambycidae). Zookeys 724, 31–42 (2017).
Article  Google Scholar 

49.
Kolk, A. & Starzyk, J. Atlas szkodliwych owadów lesnych (The atlas of harmful forest insects). 268–488 (Multico,1996).

50.
Cherepanov, A. Usachi Severnoj Azii (Lamiinae) (ed. Cherepanov, A.) 97–102 (Nauka Publishers, 1983).

51.
Danilevsky, M. L. A check list of the longicorn beetles (Cerambycoidea) of Russia.https://www.zin.ru/Animalia/Coleoptera/doc/List_of_Russia.doc (2015).

52.
Haran, J. & Roux-Morabito, G. Development of 12 microsatellites loci for the longhorn beetle Monochamus galloprovincialis (Coleoptera Cerambycidae), vector of the Pine Wood Nematode in Europe. Conserv. Genet. Res. 6, 975–977 (2014).
Article  Google Scholar 

53.
Chybicki, I. J. & Burczyk, J. Simultaneous estimation of null alleles and inbreeding coefficients. J. Hered. 100, 106–113 (2009).
CAS  PubMed  Article  Google Scholar 

54.
Guo, S. W. & Thompson, E. A. Performing the exact test of Hardy-Weinberg proportion for multiple alleles. Biometrics 1, 361–372 (1992).
MATH  Article  Google Scholar 

55.
Paradis, E. Pegas: An R package for population genetics with an integrated-modular approach. Bioinformatics 26, 419–420 (2010).
CAS  PubMed  Article  Google Scholar 

56.
Agapow, P. M. & Burt, A. Indices of multilocus linkage disequilibrium. Mol. Ecol. Notes 1, 101–102 (2001).
CAS  Article  Google Scholar 

57.
Kamvar, Z. N., Tabima, J. F. & Gr̈unwald, N. J. Poppr, ,. An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ e281, 1–14 (2014).
Google Scholar 

58.
Storey, J. D., Bass, A. J., Dabney, A. & Robinson, D. qvalue: Q-value estimation for false discovery rate control. R package version 2.14.1. https://qvalue.princeton.edu (2019).

59.
Goudet, J. & Jombart, T. hierfstat: estimation and tests of hierarchical F-statistics. https://github.com/jgx65/hierfstat (2015).

60.
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: iNterpolation and EXTrapolation for species diversity. R package version 2.0.19. https://chao.stat.nthu.edu.tw/blog/software-download/ (2019).

61.
Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).
Article  Google Scholar 

62.
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

63.
Jombart, T. et al. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).
PubMed  PubMed Central  Article  Google Scholar 

64.
Jombart, T. Adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).
CAS  PubMed  Article  Google Scholar 

65.
Pastore, M. Overlapping: a R package for estimating overlapping in empirical distributions. J. Open Source Softw. 3, 1023 (2018).
ADS  Article  Google Scholar 

66.
Cornuet, J.-M. et al. DIYABC v20: a software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).
CAS  PubMed  Article  Google Scholar 

67.
Goczał, J., Rossa, R. & Tofilski, A. Intersexual and intrasexual patterns of horn size and shape variation in the European rhinoceros beetle: quantifying the shape of weapons. Biol. J. Linn. Soc. 127, 34–43 (2019).
Article  Google Scholar 

68.
Rohlf, F. & Slice, D. Extensions of the procrustes method for the optimal superimposition of landmarks. Syst. Zool. 39, 40–59 (1990).
Article  Google Scholar 

69.
Cattell, R. B. The scree test for the number of factors. Multivariate Behav. Res. 1, 245–276 (1966).
CAS  PubMed  Article  Google Scholar 

70.
TIBCO Software Inc. Statistica (data analysis software system) version 13. https://www.statsoft.pl/statistica-i-tibco-software/ (2017).

71.
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.r-project.org/ (2015). More

1.
Mante, V., Sussillo, D., Shenoy, K. V. & Newsome, W. T. Context-dependent computation by recurrent dynamics in prefrontal cortex. Nature 503, 78–84 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
Raposo, D., Kaufman, M. T. & Churchland, A. K. A category-free neural population supports evolving demands during decision-making. Nat. Neurosci. 17, 1784–1792 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Roy, J. E., Buschman, T. J. & Miller, E. K. PFC neurons reflect categorical decisions about ambiguous stimuli. J. Cogn. Neurosci. 26, 1283–1291 (2014).
PubMed  PubMed Central  Article  Google Scholar 

4.
Siegel, M., Buschman, T. J. & Miller, E. K. Cortical information flow during flexible sensorimotor decisions. Science 348, 1352–1355 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

5.
Katz, L. N., Yates, J. L., Pillow, J. W. & Huk, A. C. Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–288 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

6.
Hanks, T. D. et al. Distinct relationships of parietal and prefrontal cortices to evidence accumulation. Nature 520, 220–223 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Erlich, J. C., Brunton, B. W., Duan, C. A., Hanks, T. D. & Brody, C. D. Distinct effects of prefrontal and parietal cortex inactivations on an accumulation of evidence task in the rat. eLife 4, e05457 (2015).
PubMed Central  Article  PubMed  Google Scholar 

8.
Goard, M. J., Pho, G. N., Woodson, J. & Sur, M. Distinct roles of visual, parietal, and frontal motor cortices in memory-guided sensorimotor decisions. eLife 5, e13764 (2016).
PubMed  PubMed Central  Article  Google Scholar 

9.
Bruce, C. J. & Goldberg, M. E. Primate frontal eye fields. I. Single neurons discharging before saccades. J. Neurophysiol. 53, 603–635 (1985).
CAS  PubMed  Article  Google Scholar 

10.
Kim, J. N. & Shadlen, M. N. Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nat. Neurosci. 2, 176–185 (1999).
PubMed  Article  Google Scholar 

11.
Ding, L. & Gold, J. I. Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field. Cereb. Cortex 22, 1052–1067 (2011).
PubMed  PubMed Central  Article  Google Scholar 

12.
Stokes, M. G. et al. Dynamic coding for cognitive control in prefrontal cortex. Neuron 78, 364–375 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Rigotti, M. et al. The importance of mixed selectivity in complex cognitive tasks. Nature 497, 585–590 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Purcell, B. A. et al. Neurally constrained modeling of perceptual decision making. Psychol. Rev. 117, 1113–1143 (2010).
PubMed Central  Article  PubMed  Google Scholar 

15.
Heitz, R. P. & Schall, J. D. Neural mechanisms of speed-accuracy tradeoff. Neuron 76, 616–628 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

16.
Parthasarathy, A. et al. Mixed selectivity morphs population codes in prefrontal cortex. Nat. Neurosci. 20, 1770–1779 (2017).
CAS  PubMed  Article  Google Scholar 

17.
Beck, J. M. et al. Probabilistic population codes for Bayesian decision making. Neuron 60, 1142–1152 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51–56 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

19.
Kaufman, M. T., Churchland, M. M., Ryu, S. I. & Shenoy, K. V. Cortical activity in the null space: permitting preparation without movement. Nat. Neurosci. 17, 440–448 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Lara, A. H., Cunningham, J. P. & Churchland, M. M. Different population dynamics in the supplementary motor area and motor cortex during reaching. Nat. Commun. 9, 2754 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Akaike, H. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974).
Article  Google Scholar 

22.
Aoi, M. & Pillow, J. W. Model-based targeted dimensionality reduction for neuronal population data. Adv. Neural Inform. Process. Syst. 31, 6690–6699 (2018).
Google Scholar 

23.
Elsayed, G. F. & Cunningham, J. P. Structure in neural population recordings: an expected byproduct of simpler phenomena? Nat. Neurosci. 20, 1310–1318 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Rossi-Pool, R. et al. Decoding a decision process in the neuronal population of dorsal premotor cortex. Neuron 96, 1432–1446 (2017).
CAS  PubMed  Article  Google Scholar 

25.
Williamson, R. C. et al. Scaling properties of dimensionality reduction for neural populations and network models. PLoS Comput. Biol. 12, e1005141 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Cunningham, J. P. & Byron, M. Y. Dimensionality reduction for large-scale neural recordings. Nature Neurosci. 17, 1500–1509 (2014).
CAS  PubMed  Article  Google Scholar 

27.
Kobak, D. et al. Demixed principal component analysis of neural population data. eLife 5, e10989 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

28.
Machens, C. K. Demixing population activity in higher cortical areas. Front. Comput. Neurosci. 4, 126 (2010).
PubMed  PubMed Central  Article  Google Scholar 

29.
Machens, C. K., Romo, R. & Brody, C. D. Functional, but not anatomical, separation of ‘what’ and ‘when’ in prefrontal cortex. J. Neurosci. 30, 350–360 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

30.
Romo, R., Brody, C. D., Hernández, A. & Lemus, L. Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399, 470–473 (1999).
CAS  PubMed  Article  Google Scholar 

31.
Hernández, A., Zainos, A. & Romo, R. Temporal evolution of a decision-making process in medial premotor cortex. Neuron 33, 959–972 (2002).
PubMed  Article  Google Scholar 

32.
Romo, R., Hernández, A., Zainos, A., Lemus, L. & Brody, C. D. Neuronal correlates of decision-making in secondary somatosensory cortex. Nat. Neurosci. 5, 1217–1225 (2002).
CAS  PubMed  Article  Google Scholar 

33.
Romo, R., Hernández, A. & Zainos, A. Neuronal correlates of a perceptual decision in ventral premotor cortex. Neuron 41, 165–173 (2004).
CAS  PubMed  Article  Google Scholar 

34.
Elsayed, G. F., Lara, A. H., Kaufman, M. T., Churchland, M. M. & Cunningham, J. P. Reorganization between preparatory and movement population responses in motor cortex. Nat. Commun. 7, 13239 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

35.
Gold, J. I. & Shadlen, M. N. The influence of behavioral context on the representation of a perceptual decision in developing oculomotor commands. J. Neurosci. 23, 632–651 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

36.
Kiani, R., Hanks, T. D. & Shadlen, M. N. Bounded integration in parietal cortex underlies decisions even when viewing duration is dictated by the environment. J. Neurosci. 28, 3017–3029 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

37.
Meister, M. L. R., Hennig, J. A. & Huk, A. C. Signal multiplexing and single-neuron computations in lateral intraparietal area during decision-making. J. Neurosci. 33, 2254–2267 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Latimer, K. W., Yates, J. L., Meister, M. L. R., Huk, A. C. & Pillow, J. W. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science 349, 184–187 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Yates, J. L., Park, I. M., Katz, L. N., Pillow, J. W. & Huk, A. C. Functional dissection of signal and noise in mt and lip during decision-making. Nat. Neurosci. 20, 1285–1292 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

40.
Vernet, M., Quentin, R., Chanes, L., Mitsumasu, A. & Valero-Cabré, A. Frontal eye field, where art thou? anatomy, function, and non-invasive manipulation of frontal regions involved in eye movements and associated cognitive operations. Front. Integr. Neurosci. 8, 1–24 (2014).
Google Scholar 

41.
Bruce, C. J., Goldberg, M. E., Bushnell, M. C. & Stanton, G. B. Primate frontal eye fields. II. Physiological and anatomical correlates of electrically evoked eye movements. J. Neurophysiol. 54, 714–734 (1985).
CAS  PubMed  Article  Google Scholar 

42.
Rizzolatti, G., Riggio, L., Dascola, I. & Umiltá, C. Reorienting attention across the horizontal and vertical meridians: evidence in favor of a premotor theory of attention. Neuropsychologia 25, 31–40 (1987).
CAS  PubMed  Article  Google Scholar 

43.
Thompson, K. G., Biscoe, K. L. & Sato, T. R. Neuronal basis of covert spatial attention in the frontal eye field. J. Neurosci. 25, 9479–9487 (2005).
CAS  PubMed  PubMed Central  Article  Google Scholar 

44.
Schall, J. D. On the role of frontal eye field in guiding attention and saccades. Vision Res. 44, 1453–1467 (2004).
PubMed  Article  Google Scholar 

45.
Storey, J. D. A direct approach to false discovery rates. J. R. Stat. Soc. Series B Stat. Methodol. 64, 479–498 (2002).
Article  Google Scholar 

46.
Brody, C. D., Hernández, A., Zainos, A. & Romo, R. Timing and neural encoding of somatosensory parametric working memory in macaque prefrontal cortex. Cereb. Cortex 13, 1196–1207 (2003).
PubMed  Article  Google Scholar 

47.
Roitman, J. D. & Shadlen, M. N. Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. J. Neurosci. 22, 9475–9489 (2002).
PubMed Central  Article  PubMed  Google Scholar 

48.
Lawrence, N. Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6, 1783–1816 (2005).
Google Scholar 

49.
Cunningham, J. P. & Ghahramani, Z. Linear dimensionality reduction: survey, insights, and generalizations. J. Mach. Learn. Res. 16, 2859–2900 (2015).

50.
Mackevicius, E. L. et al. Unsupervised discovery of temporal sequences in high-dimensional datasets, with applications to neuroscience. eLife 8, e38471 (2019).
PubMed  PubMed Central  Article  Google Scholar  More

1.
Ferrier, S. et al. IPBES Summary for Policymakers of the Methodological Assessment of Scenarios and Models of Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2019).
2.
Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P. & Moomaw, W. R. World scientists’ warning of a climate emergency. BioScience 70, 8–12 (2020).
Google Scholar 

3.
IPCC in Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) Summary for Policymakers (WMO, 2018).

4.
Díaz, S. et al. Pervasive human-driven decline of life on Earth points to the need for transformative change. Science 366, eaax3100 (2019).
Google Scholar 

5.
Fazey, I. et al. Ten essentials for action-oriented and second order energy transitions, transformations and climate change research. Energy Res. Soc. Sci. 40, 54–70 (2018).
Google Scholar 

6.
Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 1259855 (2015).
Google Scholar 

7.
Turnhout, E., Metze, T., Wyborn, C., Klenk, N. & Louder, E. The politics of co-production: participation, power, and transformation. Curr. Opin. Environ. Sustain. 42, 15–21 (2020).
Google Scholar 

8.
Wolfram, M. Conceptualizing urban transformative capacity: a framework for research and policy. Cities 51, 121–130 (2016).
Google Scholar 

9.
Etzion, D. Management for sustainability. Nat. Sustain. 1, 744–749 (2018).
Google Scholar 

10.
Ostrom, E. A diagnostic approach for going beyond panaceas. Proc. Natl Acad. Sci. USA 104, 15181–15187 (2007).
CAS  Google Scholar 

11.
Jerneck, A. et al. Structuring sustainability science. Sustain. Sci. 6, 69–82 (2011).
Google Scholar 

12.
Kates, R. W. What kind of a science is sustainability science? Proc. Natl Acad. Sci. USA 108, 19449–19450 (2011).
CAS  Google Scholar 

13.
Cash, D. W. et al. Knowledge systems for sustainable development. Proc. Natl Acad. Sci. USA 100, 8086–8091 (2003).
CAS  Google Scholar 

14.
Cumming, G. S., Olsson, P., Chapin, F. S. & Holling, C. S. Resilience, experimentation, and scale mismatches in social-ecological landscapes. Landsc. Ecol. 28, 1139–1150 (2013).
Google Scholar 

15.
Mitchell, S. D. Unsimple Truths: Science, Complexity, and Policy (Univ. Chicago Press, 2009).

16.
Davidson, D. Actions, reasons, and causes. J. Philos. 60, 685–700 (1963).
Google Scholar 

17.
Anscombe, G. E. M. Intention (Harvard Univ. Press, 1957).

18.
Dewey, J. The Quest for Certainty: A Study of the Relation of Knowledge and Action (Minton, Balch, 1929).

19.
Lubchenco, J. Entering the century of the environment: a new social contract for science. 279, 491–497 (1998).

20.
Nowotny, H., Scott, P., Gibbons, M. & Scott, P. B. Re-thinking Science: Knowledge and the Public in an Age of Uncertainty (SciELO Argentina, 2001).

21.
Ravetz, J. R. What is post-normal science. Futures 31, 647–653 (1999).
Google Scholar 

22.
König, A. Sustainability Science: Key Issues (Routledge, 2018).

23.
Wyborn, C. et al. Co-producing sustainability: reordering the governance of science, policy, and practice. Annu. Rev. Environ. Resour. 44, 319–346 (2019).
Google Scholar 

24.
Lang, D. J. et al. Transdisciplinary research in sustainability science: practice, principles, and challenges. Sustain. Sci. 7, 25–43 (2012).
Google Scholar 

25.
Umpleby, S. A. Second-order cybernetics as a fundamental revolution in science. Constr. Found. 11, 455–465 (2016).
Google Scholar 

26.
Irwin, E. G. et al. Bridging barriers to advance global sustainability. Nat. Sustain. 1, 324–326 (2018).
Google Scholar 

27.
Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).
Google Scholar 

28.
Scoones, I. et al. Transformations to sustainability: combining structural, systemic and enabling approaches. Curr. Opin. Environ. Sustain. 42, 65–75 (2020).
Google Scholar 

29.
Clark, W. C., van Kerkhoff, L., Lebel, L. & Gallopin, G. Crafting usable knowledge for sustainable development. Proc. Natl Acad. Sci. USA 113, 4570–4578 (2016).
CAS  Google Scholar 

30.
Norton, B. G. Sustainability: A Philosophy of Adaptive Ecosystem Management (Univ. Chicago Press, 2005).

31.
Popa, F., Guillermin, M. & Dedeurwaerdere, T. A pragmatist approach to transdisciplinarity in sustainability research: from complex systems theory to reflexive science. Futures 65, 45–56 (2015).
Google Scholar 

32.
Ansell, C. K. & Bartenberger, M. Varieties of experimentalism. Ecol. Econ. 130, 64–73 (2016).
Google Scholar 

33.
Caniglia, G. et al. Experiments and evidence in sustainability science: a typology. J. Clean. Prod. 169, 39–47 (2017).
Google Scholar 

34.
Wiek, A., Ness, B., Schweizer-Ries, P., Brand, F. S. & Farioli, F. From complex systems analysis to transformational change: a comparative appraisal of sustainability science projects. Sustain. Sci. 7, 5–24 (2012).
Google Scholar 

35.
Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems: organizing principles for advancing research methods and approaches. Ecol. Soc. 23, 46 (2018).
Google Scholar 

36.
Grunwald, A. Working towards sustainable development in the face of uncertainty and incomplete knowledge. J. Environ. Policy Plan. 9, 245–262 (2007).
Google Scholar 

37.
Bratman, M. Intention, Plans, and Practical Reason (Harvard Univ. Press, 1987).

38.
Midgley, G. in Systemic Intervention 113–133 (Springer, 2000).

39.
Tengö, M., Brondizio, E. S., Elmqvist, T., Malmer, P. & Spierenburg, M. Connecting diverse knowledge systems for enhanced ecosystem governance: the multiple evidence base approach. Ambio 43, 579–591 (2014).
Google Scholar 

40.
Sperling, G. B., Winthrop, R., Kwauk, C. & Yousafzai, M. What Works in Girls’ Education (The Brookings Institution, 2016).

41.
Jones, T. M. Ethical decision making by individuals in organizations: an issue-contingent model. Acad. Manag. Rev. 16, 366–395 (1991).
Google Scholar 

42.
Pelenc, J., Bazile, D. & Ceruti, C. Collective capability and collective agency for sustainability: a case study. Ecol. Econ. 118, 226–239 (2015).
Google Scholar 

43.
Farjoun, M., Ansell, C. & Boin, A. PERSPECTIVE—Pragmatism in organization studies: meeting the challenges of a dynamic and complex world. Organ. Sci. 26, 1787–1804 (2015).
Google Scholar 

44.
Djoudi, H. et al. Beyond dichotomies: gender and intersecting inequalities in climate change studies. Ambio 45, 248–262 (2016).
Google Scholar 

45.
Schlüter, M. et al. Capturing emergent phenomena in social-ecological systems: an analytical framework. Ecol. Soc. 24, 11 (2019).
Google Scholar 

46.
von Wirth, T., Fuenfschilling, L., Frantzeskaki, N. & Coenen, L. Impacts of urban living labs on sustainability transitions: mechanisms and strategies for systemic change through experimentation. Eur. Plan. Stud. 27, 229–257 (2019).
Google Scholar 

47.
Luederitz, C. et al. Learning through evaluation – a tentative evaluative scheme for sustainability transition experiments. J. Clean. Prod. 169, 61–76 (2017).
Google Scholar 

48.
Berkhout, F. et al. Sustainability experiments in Asia: innovations shaping alternative development pathways? Environ. Sci. Policy 13, 261–271 (2010).
Google Scholar 

49.
Freeth, R. & Caniglia, G. Learning to collaborate while collaborating: advancing interdisciplinary sustainability research. Sustain. Sci. 15, 247–261 (2020).
Google Scholar 

50.
Polanyi, M. The Tacit Dimension (Univ. Chicago Press, 2009).

51.
Lansing, J. S. Priests and Programmers: Technologies of Power in the Engineered Landscape of Bali (Princeton Univ. Press, 2009).

52.
Nonaka, I. & Takeuchi, H. The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation (Oxford Univ. Press, 1995).

53.
Lam, D. P. M. et al. Indigenous and local knowledge in sustainability transformations research: a literature review. Ecol. Soc. 25, 3 (2020).
Google Scholar 

54.
Ryle, G. Knowing how and knowing that: the presidential address. Proc. Aristot. Soc. 46, 1–16 (1945).
Google Scholar 

55.
Sahni, U. From Learning Outcomes to Life Outcomes: What Can You Do and Who Can You Be? A Case Study in Girls’ Education in India (Center for Universal Education at Brookings, 2012).

56.
Luederitz, C., Abson, D. J., Audet, R. & Lang, D. J. Many pathways toward sustainability: not conflict but co-learning between transition narratives. Sustain. Sci. 12, 393–407 (2017).
Google Scholar 

57.
Messerli, P. et al. Expansion of sustainability science needed for the SDGs. Nat. Sustain. 2, 892–894 (2019).
Google Scholar 

58.
Balvanera, P. et al. Key features for more successful place-based sustainability research on social-ecological systems: a programme on ecosystem change and society (PECS) perspective. Ecol. Soc. 22, 14 (2017).
Google Scholar 

59.
Schäpke, N. et al. Jointly experimenting for transformation? Shaping real-world laboratories by comparing them. GAIA 27, 85–96 (2018).
Google Scholar 

60.
Brundiers, K., Wiek, A. & Redman, C. L. Real-world learning opportunities in sustainability: from classroom into the real world. Int. J. Sustain. High. Educ. 11, 308–324 (2010).
Google Scholar 

61.
Ploum, L., Blok, V., Lans, T. & Omta, O. Toward a validated competence framework for sustainable entrepreneurship. Organ. Environ. 31, 113–132 (2018).
Google Scholar 

62.
Blok, V., Gremmen, B. & Wesselink, R. Dealing with the wicked problem of sustainability in advance. Bus. Prof. Ethics J. https://doi.org/10.5840/bpej201621737 (2016). More

1.
van Kleunen, M., Bossdorf, O. & Dawson, W. The ecology and evolution of alien plants. Annu. Rev. Ecol. Evol. Syst. 49, 25–47 (2018).
Article  Google Scholar 
2.
Seebens, H. et al. No saturation in the accumulation of alien species worldwide. Nat. Commun. 8, 14435 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Vilà, M. et al. Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecol. Lett. 14, 702–708 (2011).
PubMed  Article  Google Scholar 

4.
Elton, C. S. The Ecology of Invasion by Animals and Plants (Univ. of Chicago Press, 1958).

5.
Kuebbing, S. E. & Nunez, M. A. Invasive non-native plants have a greater effect on neighbouring natives than other non-natives. Nat. Plants 2, 16134 (2016).
PubMed  Article  Google Scholar 

6.
Golivets, M. & Wallin, K. F. Neighbour tolerance, not suppression, provides competitive advantage to non-native plants. Ecol. Lett. 21, 745–759 (2018).
PubMed  Article  Google Scholar 

7.
Zhang, Z. & van Kleunen, M. Common alien plants are more competitive than rare natives but not than common natives. Ecol. Lett. 22, 1378–1386 (2019).
PubMed  Article  Google Scholar 

8.
White, E. M., Wilson, J. C. & Clarke, A. R. Biotic indirect effects: a neglected concept in invasion biology. Divers. Distrib. 12, 443–455 (2006).
Article  Google Scholar 

9.
Sotomayor, D. A. & Lortie, C. J. Indirect interactions in terrestrial plant communities: emerging patterns and research gaps. Ecosphere 6, art103 (2015).
Article  Google Scholar 

10.
Aschehoug, E. T. & Callaway, R. M. Diversity increases indirect interactions, attenuates the intensity of competition, and promotes coexistence. Am. Nat. 186, 452–459 (2015).
PubMed  Article  Google Scholar 

11.
Feng, Y. & van Kleunen, M. Phylogenetic and functional mechanisms of direct and indirect interactions among alien and native plants. J. Ecol. 104, 1136–1148 (2016).
Article  Google Scholar 

12.
Stotz, G. C. et al. Not a melting pot: plant species aggregate in their non‐native range. Glob. Ecol. Biogeogr. 29, 482–490 (2019).
Article  Google Scholar 

13.
Wardle, D. A. & Peltzer, D. A. Impacts of invasive biota in forest ecosystems in an aboveground–belowground context. Biol. Invasions 19, 3301–3316 (2017).
Article  Google Scholar 

14.
Kulmatiski, A., Beard, K. H. & Stark, J. M. Soil history as a primary control on plant invasion in abandoned agricultural fields. J. Appl. Ecol. 43, 868–876 (2006).
Article  Google Scholar 

15.
Simberloff, D. & Von Holle, B. Positive interactions of nonindigenous species: invasional meltdown? Biol. Invasions 1, 21–32 (1999).
Article  Google Scholar 

16.
Simberloff, D. Invasional meltdown 6 years later: important phenomenon, unfortunate metaphor, or both? Ecol. Lett. 9, 912–919 (2006).
PubMed  Article  Google Scholar 

17.
Braga, R. R., Gómez-Aparicio, L., Heger, T., Vitule, J. R. S. & Jeschke, J. M. Structuring evidence for invasional meltdown: broad support but with biases and gaps. Biol. Invasions 20, 923–936 (2018).
Article  Google Scholar 

18.
Maynard, D. S., Miller, Z. R. & Allesina, S. Predicting coexistence in experimental ecological communities. Nat. Ecol. Evol. 4, 91–100 (2020).
PubMed  Article  Google Scholar 

19.
May, R. M. Will a large complex system be stable? Nature 238, 413–414 (1972).
CAS  PubMed  Article  PubMed Central  Google Scholar 

20.
Godoy, O., Stouffer, D. B., Kraft, N. J. B. & Levine, J. M. Intransitivity is infrequent and fails to promote annual plant coexistence without pairwise niche differences. Ecology 98, 1193–1200 (2017).
PubMed  Article  Google Scholar 

21.
Vandermeer, J. H. The competitive structure of communities: an experimental approach with protozoa. Ecology 50, 362–371 (1969).
Article  Google Scholar 

22.
Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 0109 (2017).
Article  Google Scholar 

23.
Case, T. J. & Bender, E. A. Testing for higher order interactions. Am. Nat. 118, 920–929 (1981).
Article  Google Scholar 

24.
Levine, J. M., Bascompte, J., Adler, P. B. & Allesina, S. Beyond pairwise mechanisms of species coexistence in complex communities. Nature 546, 56–64 (2017).
CAS  PubMed  Article  Google Scholar 

25.
Prince, E. K., Myers, T. L., Naar, J. & Kubanek, J. Competing phytoplankton undermines allelopathy of a bloom-forming dinoflagellate. Proc. R. Soc. B 275, 2733–2741 (2008).
PubMed  Article  Google Scholar 

26.
Tilman, D. Resource Competition and Community Structure (Princeton Univ. Press, 1982).

27.
Dawson, W., Fischer, M. & van Kleunen, M. Common and rare plant species respond differently to fertilisation and competition, whether they are alien or native. Ecol. Lett. 15, 873–880 (2012).
PubMed  Article  Google Scholar 

28.
Godoy, O., Valladares, F. & Castro-Díez, P. Multispecies comparison reveals that invasive and native plants differ in their traits but not in their plasticity. Funct. Ecol. 25, 1248–1259 (2011).
Article  Google Scholar 

29.
Liu, Y. J. & van Kleunen, M. Nitrogen acquisition of Central European herbaceous plants that differ in their global naturalization success. Funct. Ecol. 33, 566–575 (2019).
Article  Google Scholar 

30.
Holt, R. D. Predation, apparent competition, and the structure of prey communities. Theor. Popul. Biol. 12, 197–229 (1977).
CAS  PubMed  Article  Google Scholar 

31.
Bever, J. D., Westover, K. M. & Antonovics, J. Incorporating the soil community into plant population dynamics: the utility of the feedback approach. J. Ecol. 85, 561–573 (1997).
Article  Google Scholar 

32.
Kulmatiski, A., Beard, K. H., Stevens, J. R. & Cobbold, S. M. Plant-soil feedbacks: a meta-analytical review. Ecol. Lett. 11, 980–992 (2008).
PubMed  Article  Google Scholar 

33.
Lekberg, Y. et al. Relative importance of competition and plant-soil feedback, their synergy, context dependency and implications for coexistence. Ecol. Lett. 21, 1268–1281 (2018).
PubMed  Article  Google Scholar 

34.
Latz, E. et al. Plant diversity improves protection against soil-borne pathogens by fostering antagonistic bacterial communities. J. Ecol. 100, 597–604 (2012).
Article  Google Scholar 

35.
Kardol, P., Cornips, N. J., van Kempen, M. M. L., Bakx-Schotman, J. M. T. & van der Putten, W. H. Microbe-mediated plant–soil feedback causes historical contingency effects in plant community assembly. Ecol. Monogr. 77, 147–162 (2007).
Article  Google Scholar 

36.
Dawson, W., Schrama, M. & Austin, A. Identifying the role of soil microbes in plant invasions. J. Ecol. 104, 1211–1218 (2016).
Article  Google Scholar 

37.
Callaway, R. M., Thelen, G. C., Rodriguez, A. & Holben, W. E. Soil biota and exotic plant invasion. Nature 427, 731–733 (2004).

38.
Ke, P. J. & Wan, J. Effects of soil microbes on plant competition: a perspective from modern coexistence theory. Ecol. Monogr. 90, e01391 (2020).
Article  Google Scholar 

39.
Kuebbing, S. E., Classen, A. T., Call, J. J., Henning, J. A. & Simberloff, D. Plant–soil interactions promote co-occurrence of three nonnative woody shrubs. Ecology 96, 2289–2299 (2015).
PubMed  Article  PubMed Central  Google Scholar 

40.
Callaway, R. M. et al. Novel weapons: invasive plant suppresses fungal mutualists in America but not in its native Europe. Ecology 89, 1043–1055 (2008).
PubMed  Article  Google Scholar 

41.
Darwin, C. On the Origin of Species (J. Murray, 1859).

42.
Keane, R. M. & Crawley, M. J. Exotic plant invasions and the enemy release hypothesis. Trends Ecol. Evol. 17, 164–170 (2002).
Article  Google Scholar 

43.
Mangla, S. & Callaway, R. M. Exotic invasive plant accumulates native soil pathogens which inhibit native plants. J. Ecol. 96, 58–67 (2008).
Google Scholar 

44.
Saul, W. C. & Jeschke, J. M. Eco-evolutionary experience in novel species interactions. Ecol. Lett. 18, 236–245 (2015).
PubMed  Article  Google Scholar 

45.
van Kleunen, M. et al. Global exchange and accumulation of non-native plants. Nature 525, 100–103 (2015).
PubMed  Article  CAS  Google Scholar 

46.
Pyšek, P. et al. Naturalized alien flora of the world. Preslia 89, 203–274 (2017).
Article  Google Scholar 

47.
Essl, F. et al. Drivers of the relative richness of naturalized and invasive plant species on Earth. AoB PLANTS 11, plz051 (2019).
PubMed  PubMed Central  Article  Google Scholar 

48.
Seebens, H. et al. Global rise in emerging alien species results from increased accessibility of new source pools. Proc. Natl Acad. Sci. USA 115, E2264–E2273 (2018).
CAS  PubMed  Article  Google Scholar 

49.
Adler, P. B. et al. Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition. Ecol. Lett. 21, 1319–1329 (2018).
PubMed  Article  Google Scholar 

50.
Mangan, S. A. et al. Negative plant–soil feedback predicts tree-species relative abundance in a tropical forest. Nature 466, 752–755 (2010).
CAS  PubMed  Article  Google Scholar 

51.
Dal Co, A., van Vliet, S., Kiviet, D. J., Schlegel, S. & Ackermann, M. Short-range interactions govern the dynamics and functions of microbial communities. Nat. Ecol. Evol. 4, 366–375 (2020).
PubMed  Article  Google Scholar 

52.
Reinhart, K. O., Packer, A., Van der Putten, W. H. & Clay, K. Plant–soil biota interactions and spatial distribution of black cherry in its native and invasive ranges. Ecol. Lett. 6, 1046–1050 (2003).
Article  Google Scholar 

53.
Liu, H. & Stiling, P. Testing the enemy release hypothesis: a review and meta-analysis. Biol. Invasions 8, 1535–1545 (2006).
Article  Google Scholar 

54.
Zhang, Z. et al. Contrasting effects of specialist and generalist herbivores on resistance evolution in invasive plants. Ecology 99, 866–875 (2018).
PubMed  Article  Google Scholar 

55.
Chun, Y. J., van Kleunen, M. & Dawson, W. The role of enemy release, tolerance and resistance in plant invasions: linking damage to performance. Ecol. Lett. 13, 937–946 (2010).
PubMed  Google Scholar 

56.
Dickie, I. A. et al. The emerging science of linked plant–fungal invasions. New Phytol. 215, 1314–1332 (2017).
CAS  PubMed  Article  Google Scholar 

57.
Shipunov, A., Newcombe, G., Raghavendra, A. K. H. & Anderson, C. L. Hidden diversity of endophytic fungi in an invasive plant. Am. J. Bot. 95, 1096–1108 (2008).
PubMed  Article  Google Scholar 

58.
Hardoim, P. R. et al. The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol. Mol. Biol. Rev. 79, 293–320 (2015).
PubMed  PubMed Central  Article  Google Scholar 

59.
Busby, P. E., Peay, K. G. & Newcombe, G. Common foliar fungi of Populus trichocarpa modify Melampsora rust disease severity. New Phytol. 209, 1681–1692 (2016).
CAS  PubMed  Article  Google Scholar 

60.
Großkopf, T. & Soyer, O. S. Synthetic microbial communities. Curr. Opin. Microbiol. 18, 72–77 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

61.
Divíšek, J. et al. Similarity of introduced plant species to native ones facilitates naturalization, but differences enhance invasion success. Nat. Commun. 9, 4631 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

62.
Feng, Y., Fouqueray, T. D., van Kleunen, M. & Cornelissen, H. Linking Darwin’s naturalisation hypothesis and Elton’s diversity–invasibility hypothesis in experimental grassland communities. J. Ecol. 107, 794–805 (2019).
Article  Google Scholar 

63.
Li, S. P. et al. The effects of phylogenetic relatedness on invasion success and impact: deconstructing Darwin’s naturalisation conundrum. Ecol. Lett. 18, 1285–1292 (2015).
PubMed  Article  Google Scholar 

64.
van Kleunen, M., Dawson, W., Bossdorf, O. & Fischer, M. The more the merrier: multi-species experiments in ecology. Basic Appl. Ecol. 15, 1–9 (2014).
Article  Google Scholar 

65.
FloraWeb (Bundesamt für Naturschutz, 2003); http://www.floraweb.de/

66.
Richardson, D. M. et al. Naturalization and invasion of alien plants: concepts and definitions. Divers. Distrib. 6, 93–107 (2000).
Article  Google Scholar 

67.
Brinkman, E. P., Van der Putten, W. H., Bakker, E.-J. & Verhoeven, K. J. F. Plant–soil feedback: experimental approaches, statistical analyses and ecological interpretations. J. Ecol. 98, 1063–1073 (2010).
Article  Google Scholar 

68.
Rinella, M. J. & Reinhart, K. O. Toward more robust plant–soil feedback research. Ecology 99, 550–556 (2018).
PubMed  Article  Google Scholar 

69.
Zhang, Z., Liu, Y., Brunel, C. & van Kleunen, M. Evidence for Elton’s diversity–invasibility hypothesis from belowground. Ecology https://doi.org/10.1002/ecy.3187 (accepted).

70.
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).
CAS  PubMed  Article  Google Scholar 

71.
Orgiazzi, A. et al. Unravelling soil fungal communities from different Mediterranean land-use backgrounds. PLoS ONE 7, e34847 (2012).
CAS  PubMed  PubMed Central  Article  Google Scholar 

72.
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–13 (2011).
Article  Google Scholar 

73.
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2018).
PubMed Central  Article  CAS  PubMed  Google Scholar 

75.
Nguyen, N. H. et al. FUNGuild: an open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).
Article  Google Scholar 

76.
R: A language and environment for statistical computing v.3.6.1 (R Foundation for Statistical Computing, 2019); http://www.R-project.org/

77.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & R Core Team. nlme: Linear and nonlinear mixed effect s models. R package version 3.1-140 (2019).

78.
Gibson, D., Connolly, J., Hartnett, D. & Weidenhamer, J. Designs for greenhouse studies of interactions between plants. J. Ecol. 87, 1–16 (1999).
Article  Google Scholar 

79.
Aschehoug, E. T., Brooker, R., Atwater, D. Z., Maron, J. L. & Callaway, R. M. The mechanisms and consequences of interspecific competition among plants. Annu. Rev. Ecol. Syst. 47, 263–281 (2016).
Article  Google Scholar 

80.
Hart, S. P., Burgin, J. R. & Marshall, D. J. Revisiting competition in a classic model system using formal links between theory and data. Ecology 93, 2015–2022 (2012).
PubMed  Article  Google Scholar 

81.
Zuur, A., Ieno, E., Walker, N., Saveliev, A. & Smith, G. Mixed Effects Models and Extensions in Ecology with R (Springer, 2009).

82.
Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).
Article  Google Scholar 

83.
Bennett, J. A. & Klironomos, J. Mechanisms of plant–soil feedback: interactions among biotic and abiotic drivers. New Phytol. 222, 91–96 (2019).
PubMed  Article  Google Scholar 

84.
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6 (2019).

85.
Wei, T. & Simko, V. corrplot: Visualization of a correlation matrix. R package version 0.84 (2017). More

Ocean temperature
The independent bottom water temperature estimates all showed variable but increasing autumn temperatures from 1996 to 2010, followed by a decline through to 2018 in the mid-depth offshore stations and the survey, but less of a decline in the inshore or deep offshore stations (Fig. 1, 2a). Both deep offshore stations warmed by ~ 0.2 °C over the time series. The ~ 1 °C temperature range evident in the southwestern mid-depth offshore FX8 autumn hydrographic series was larger than that of the less variable northern mid-depth offshore SI7 hydrographic station, but neither of the mid-depth offshore stations showed any net warming over the 22-year period. The shallow inshore stations both warmed considerably (1.5–2 °C) over the time series. Although no net increase was apparent in either of the mid-depth offshore hydrographic station autumn time series, 0.25–0.40 °C increases were observed in both the winter and spring measurement time series at both mid-depth hydrographic stations.
Figure 1

Map of 245 autumn groundfish survey core stations (black dots) sampled around Iceland every October between 1996 and 2018 (except 2011). Bottom-temperature profiles were collected independently at hydrographic stations FX3, FX8 and FX9 (red triangles) and SI1, SI7 and SI8 (blue triangles). Depth contours are 200, 500 and 1000 m.

Full size image

The mean environmental temperature from the autumn survey (Tempe) lay midway between that of the two mid-depth offshore hydrographic stations, over a similar ~ 1 °C temperature range. Based on a fitted regression to the environmental temperature time series, there was a net warming of 0.33 °C across the survey area over the 22-year time period, although the increase was certainly not linear (Fig. 2c). There were large differences in both the temperature range and temperature-at-depth among some of the regions (Fig. 2b). Bottom water temperatures at 200 m depth were 3–4 °C warmer in the south (SW = 7.3 °C; SE = 8.2 °C) than in the north (NW = 5.4 °C; NE = 4.5 °C), and 4–5 °C warmer at a depth of 500 m (NW = 1.4 °C; NE = −0.5 °C; SW = 6.6 °C; SE = 3.9 °C). Although most stations deeper than 500 m in the NE were  3 °C.
Figure 2

Bottom-temperatures from hydrographic stations (A: left panel) and from the autumn groundfish survey (B: top right and C: bottom right panel). (A) Near-bottom temperatures at hydrographic stations FX3 and SI1 (~ 70 m), FX8 and SI7 (~ 400 m), and FX9 and SI8 (~ 1000 m) between 1996 and 2018 are shown for winter (January–March), spring (May–June) and autumn (October–December). A geometric smooth has been fitted to the annual means. Note different scales on the y-axes. (B) Mean depth-temperature profiles for each region based on bottom temperatures from the autumn survey. Fitted lines are loess regressions. (C) The mean annual environmental temperature time series estimated from a GLM of the autumn survey bottom temperature measurements (see text for details). The linear regression fitted to the annual means is intended only to show the overall rate of warming through the time series.

Full size image

Survey catches
A total of 5390 tows were fished over the 22-year time series. Station depths ranged from 26 to 1203 m (mean of 377 m) while bottom temperatures ranged between − 2 and 11 °C (mean of 4.85 °C). Of the 7,246,474 fish representing 200 species that were caught, 82 species were caught during at least 19 of the survey years, and thus are the primary subject of this analysis (Suppl. Table 1). The mean standardized annual abundance of these 82 species ranged between 1 and 27,513 fish, with an overall mean across species of 4096; 70% of the species had a mean standardized annual abundance of less than 100.
There was no clear identifier of species that were rare, and thus caught sporadically, versus those that were newly immigrated to the survey area in response to the warming environment. Nor was it possible to exclude the possibility that certain rare species were incorrectly identified in some survey years. However, there were 6 species that were never caught prior to 1998, yet appeared in increasing numbers in subsequent years (Table 1). All of these species were caught in at least 8 survey years, and as many as 19. However, three of the species were deepwater ( > 800 m), and thus unlikely to have been subject to temperature increases of > 0.2 °C. Of the remaining three species, Atlantic mackerel (Scomber scombrus), blackbelly rosefish (Helicolenus dactylopterus) and hollowsnout grenadier (Coelorinchus caelorhincus) were all warmwater (TB  > 2) and stenothermal (Steno  More

1.
Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant diversity over time. Proc. Natl Acad. Sci. USA 110, 19456–19459 (2013).
CAS  PubMed  Google Scholar 
2.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but no systematic loss. Science 344, 296–299 (2014).
CAS  PubMed  Google Scholar 

3.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
CAS  PubMed  Google Scholar 

4.
McGill, B. J., Dornelas, M., Gotelli, N. J. & Magurran, A. E. Fifteen forms of biodiversity trend in the Anthropocene. Trends Ecol. Evol. 30, 104–113 (2015).
PubMed  Google Scholar 

5.
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).

6.
Schroeder-Georgi, T. et al. From pots to plots: hierarchical trait-based prediction of plant performance in a mesic grassland. J. Ecol. 104, 206–218 (2016).
Google Scholar 

7.
Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
Google Scholar 

8.
Funk, J. L. et al. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol. Rev. 92, 1156–1173 (2017).
PubMed  Google Scholar 

9.
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).
PubMed  Google Scholar 

10.
Violle, C. et al. Let the concept of trait be functional! Oikos 116, 882–892 (2007).
Google Scholar 

11.
Chapin, F. S. III et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).
CAS  PubMed  Google Scholar 

12.
Díaz, S. et al. Incorporating plant functional diversity effects in ecosystem service assessments. Proc. Natl Acad. Sci. USA 104, 20684–20689 (2007).
PubMed  Google Scholar 

13.
Grigulis, K. et al. Relative contributions of plant traits and soil microbial properties to mountain grassland ecosystem services. J. Ecol. 101, 47–57 (2013).
Google Scholar 

14.
Liu, J. et al. Explaining maximum variation in productivity requires phylogenetic diversity and single functional traits. Ecology 96, 176–183 (2015).
PubMed  Google Scholar 

15.
Yuan, Z. et al. Multiple metrics of diversity have different effects on temperate forest functioning over succession. Oecologia 182, 1175–1185 (2016).
PubMed  Google Scholar 

16.
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).
CAS  Google Scholar 

17.
Moles, A. T. & Westoby, M. Seed size and plant strategy across the whole life cycle. Oikos 113, 91–105 (2006).
Google Scholar 

18.
Reich, P. B. The world-wide ‘fast-slow’ plant economics spectrum: a traits manifesto. J. Ecol. 102, 275–301 (2014).
Google Scholar 

19.
Huang, Y. et al. Impacts of species richness on productivity in a large-scale subtropical forest experiment. Science 362, 80–83 (2018).
CAS  PubMed  Google Scholar 

20.
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).
CAS  Google Scholar 

21.
Butterfield, B. J. & Suding, K. N. Single-trait functional indices outperform multi-trait indices in linking environmental gradients and ecosystem services in a complex landscape. J. Ecol. 101, 9–17 (2013).
Google Scholar 

22.
Gustafsson, C. & Norkko, A. Quantifying the importance of functional traits for primary production in aquatic plant communities. J. Ecol. 107, 154–166 (2018).
Google Scholar 

23.
Craven, D. et al. Multiple facets of biodiversity drive the diversity–stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
PubMed  Google Scholar 

24.
Clark, C. M., Flynn, D. F. B., Butterfield, B. J. & Reich, P. B. Testing the link between functional diversity and ecosystem functioning in a Minnesota grassland experiment. PLoS ONE 7, e52821 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

25.
Flombaum, P. & Sala, O. E. Effects of plant species traits on ecosystem processes: experiments in Patagonian steppe. Ecology 93, 227–234 (2012).
PubMed  Google Scholar 

26.
Laliberté, E. & Tylianikis, J. M. Cascading effects of long-term land-use changes on plant traits and ecosystem functioning. Ecology 93, 145–155 (2012).
PubMed  Google Scholar 

27.
Lienin, P. & Kleyer, M. Plant trait responses to the environment and effects on ecosystem properties. Basic Appl. Ecol. 13, 301–311 (2012).
Google Scholar 

28.
Chanteloup, P. & Bonis, A. Functional diversity in root and above-ground traits in a fertile grassland shows a detrimental effect on productivity. Basic Appl. Ecol. 14, 208–216 (2013).
Google Scholar 

29.
Jiang, J. et al. Litter species traits, but not richness, contribute to carbon and nitrogen dynamics in an alpine meadow on the Tibetan Plateau. Plant Soil 373, 931–941 (2013).
CAS  Google Scholar 

30.
Lavorel, S. et al. A novel framework for linking functional diversity of plants with other trophic levels for the quantification of ecosystem services. J. Veg. Sci. 24, 942–948 (2013).
Google Scholar 

31.
Makkonen, M., Berg, M. P., van Logtestijn, R. S. P., van Hal, J. R. & Aerts, R. Do physical plant litter traits explain non-additivity in litter mixtures? A test of the improved microenvironmental conditions theory. Oikos 122, 987–997 (2013).
Google Scholar 

32.
Ziter, C., Bennett, E. M. & Gonzalez, A. Functional diversity and management mediate aboveground carbon stocks in small forest fragments. Ecosphere 4, 1–21 (2013).

33.
Barrufol, M. et al. Biodiversity promotes tree growth during succession in subtropical forest. PLoS ONE 8, e81246 (2014).
Google Scholar 

34.
Bu, W., Zang, R. & Ding, Y. Field observed relationships between biodiversity and ecosystem functioning during secondary succession in a tropical lowland rainforest. Acta Oecol. (Montrouge) 55, 1–7 (2014).
Google Scholar 

35.
Carvalho, G. H., Batalha, M. A., Silva, I. A., Cianciaruso, M. V. & Petchey, O. L. Are fire, soil fertility and toxicity, water availability, plant functional diversity, and litter decomposition related in a Neotropical savannah? Oecologia 175, 923–935 (2014).
PubMed  Google Scholar 

36.
Cavanaugh, K. C. et al. Carbon storage in tropical forests correlates with taxonomic diversity and functional dominance on a global scale. Glob. Ecol. Biogeogr. 23, 563–573 (2014).
Google Scholar 

37.
Hantsch, L. et al. No plant functional diversity effects of foliar fungal pathogens in experimental tree communities. Fungal Divers. 66, 139–151 (2014).
Google Scholar 

38.
Lasky, J. R. et al. The relationship between tree biodiversity and biomass dynamics changes with tropical forest succession. Ecol. Lett. 17, 1158–1167 (2014).
PubMed  Google Scholar 

39.
Milcu, A. et al. Functional diversity of leaf nitrogen concentrations drives grassland carbon fluxes. Ecol. Lett. 17, 435–444 (2014).
PubMed  Google Scholar 

40.
Ruiz-Benito, P. et al. Diversity increases carbon storage and tree productivity in Spanish forests. Glob. Ecol. Biogeogr. 23, 311–322 (2014).
Google Scholar 

41.
Sapijanskas, J., Paquette, A., Potvin, C., Kunert, N. & Loreau, M. Tropical tree diversity enhances light capture through crown plasticity and spatial and temporal niche differences. Ecology 95, 2479–2492 (2014).
Google Scholar 

42.
Schuldt, A. et al. Functional and phylogenetic diversity of woody plants drive herbivory in a highly diverse forest. New Phytol. 202, 864–873 (2014).
PubMed  PubMed Central  Google Scholar 

43.
Álvarex-Yépiz, J. C. & Dovĉiak, M. Enhancing ecosystem function through conservation: threatened plants increase local carbon storage in tropical dry forests. Trop. Conserv. Sci. 8, 999–1008 (2015).
Google Scholar 

44.
Debouk, H., de Bello, F. & Sebastià, M.-T. Functional trait changes, productivity shifts and vegetation stability in mountain grasslands during a short-term warming. PLoS ONE 10, e0141899 (2015).
PubMed  PubMed Central  Google Scholar 

45.
Deraison, H., Badenhausser, I., Loeuille, N., Scherber, C. & Gross, N. Functional trait diversity across trophic levels determines herbivore impact on plant community biomass. Ecol. Lett. 18, 1346–1355 (2015).
PubMed  Google Scholar 

46.
Frainer, A., Moretti, M. S., Xu, W. & Gessner, M. O. No evidence for leaf-trait dissimilarity effects on litter decomposition, fungal decomposers, and nutrient dynamics. Ecology 96, 550–561 (2015).
PubMed  Google Scholar 

47.
Haase, J. et al. Contrasting effects of tree diversity on young tree growth and resistance to insect herbivores across three biodiversity experiments. Oikos 124, 1674–1685 (2015).
Google Scholar 

48.
Kröber, W. et al. Early subtropical forest growth is driven by community mean trait values and functional diversity rather than the abiotic environment. Ecol. Evol. 5, 3541–3556 (2015).
PubMed  PubMed Central  Google Scholar 

49.
Lohbeck, M., Poorter, L., Martínez-Ramos, M. & Bongers, F. Biomass is the main driver of changes in ecosystem process rates during tropical forest succession. Ecology 96, 1242–1252 (2015).
PubMed  Google Scholar 

50.
Paquette, A., Joly, S. & Messier, C. Explaining forest productivity using tree functional traits and phylogenetic information: two sides of the same coin over evolutionary scale? Ecol. Evol. 5, 1774–1783 (2015).
PubMed  PubMed Central  Google Scholar 

51.
Rivest, D., Paquette, A., Shipley, B., Reich, P. B. & Messier, C. Tree communities rapidly alter soil microbial resistance and resilience to drought. Funct. Ecol. 29, 570–578 (2015).
Google Scholar 

52.
Salisbury, C. L. & Potvin, C. Does tree species composition affect productivity in a tropical planted forest? Biotropica 47, 559–568 (2015).
Google Scholar 

53.
Schwarz, B. et al. Non-significant tree diversity but significant identity effects on earthworm communities in three tree diversity experiments. Eur. J. Soil Biol. 67, 17–26 (2015).
Google Scholar 

54.
Tardif, A. & Shipley, B. The relationship between functional dispersion of mixed-leaf litter mixtures and species’ interactions during decomposition. Oikos 124, 1050–1057 (2015).
Google Scholar 

55.
Valencia, E. et al. Functional diversity enhances the resistance of ecosystem multifunctionality to aridity in Mediterranean drylands. New Phytol. 206, 660–671 (2015).
PubMed  Google Scholar 

56.
Van Rooijen, N. M. et al. Plant species diversity mediates ecosystem stability of natural dune grasslands in response to drought. Ecosystems 18, 1383–1394 (2015).
Google Scholar 

57.
Zhang, Y., Wang, R., Kaplan, D. & Liu, J. Which components of plant diversity are most correlated with ecosystem properties? A case study in a restored wetland in northern China. Ecol. Indic. 49, 228–236 (2015).
Google Scholar 

58.
Barkaoui, K., Roumet, C. & Volaire, F. Mean root trait more than root trait diversity determines drought resilience in native and cultivated Mediterranean grass mixtures. Agric. Ecosyst. Environ. 231, 122–132 (2016).
Google Scholar 

59.
Chiang, J.-M. et al. Functional composition drives ecosystem function through multiple mechanisms in a broadleaved subtropical forest. Oecologia 182, 829–840 (2016).
PubMed  Google Scholar 

60.
De Vries, F. T. & Bardgett, R. Plant community controls on short-term ecosystem nitrogen retention. New Phytol. 210, 861–874 (2016).
PubMed  PubMed Central  Google Scholar 

61.
Jewell, M. D. et al. Partitioning the effect of composition and diversity of tree communities on leaf litter decomposition and soil respiration. Oikos 126, 959–971 (2016).
Google Scholar 

62.
Lin, D. et al. Traits of dominant tree species predict local scale variation in forest aboveground and topsoil carbon stocks. Plant Soil 409, 435–446 (2016).
CAS  Google Scholar 

63.
Mason, N. W. H. et al. Leaf economics spectrum-productivity relationships in intensively grazed pastures depend on dominant species identity. Ecol. Evol. 6, 3079–3091 (2016).
PubMed  PubMed Central  Google Scholar 

64.
Mensah, S., Veldtman, R., Assogbadjo, A. E., Kakaï, R. G. & Seifert, T. Tree species diversity promotes aboveground carbon storage through functional diversity and functional dominance. Ecol. Evol. 6, 7546–7557 (2016).
PubMed  PubMed Central  Google Scholar 

65.
Milcu, A. et al. Plant functional diversity increases grassland productivity-related water vapor fluxes: an Ecotron and modelling approach. Ecology 97, 2044–2054 (2016).
PubMed  Google Scholar 

66.
Na, Z., Zhengwen, W., Xinqing, S. & Kun, W. Diversity components and assembly patterns of plant functional traits determine community spatial stability under resource gradients in a desert steppe. Rangel. J. 38, 511–521 (2016).
Google Scholar 

67.
Ouyang, S. et al. Significant effects of biodiversity on forest biomass during the succession of subtropical forest in South China. For. Ecol. Manage. 372, 291–302 (2016).
Google Scholar 

68.
Prado-Junior, J. A. et al. Conservative species drive biomass productivity in tropical dry forests. J. Ecol. 104, 817–827 (2016).
Google Scholar 

69.
Ratcliffe, S. et al. Modes of functional biodiversity control on tree productivity across the European continent. Glob. Ecol. Biogeogr. 25, 251–262 (2016).
Google Scholar 

70.
Roscher, C. et al. Convergent high diversity in naturally colonized experimental grasslands is not related to increased productivity. Perspect. Plant Ecol. Evol. Syst. 20, 32–45 (2016).
Google Scholar 

71.
Spasojevic, M. J. et al. Scaling up the diversity–resilience relationship with trait databases and remote sensing data: the recovery of productivity after wildfire. Glob. Change Biol. 22, 1421–1432 (2016).
Google Scholar 

72.
Tobner, C. M. et al. Functional identity is the main driver of diversity effects in young tree communities. Ecol. Lett. 19, 638–647 (2016).
PubMed  Google Scholar 

73.
Wu, J., Wurst, S. & Zhang, X. Plant functional trait diversity regulates the nonlinear response of productivity to regional climate change in Tibetan alpine grasslands. Sci. Rep. 6, 35649 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

74.
Zhu, J., Jiang, L. & Zhang, Y. Relationships between functional diversity and aboveground biomass production in the Northern Tibetan alpine grasslands. Sci. Rep. 6, 34105 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

75.
Zuo, X. et al. Testing associations of plant functional diversity with carbon and nitrogen storage along a restoration gradient of sandy grassland. Front. Plant Sci. 7, 189 (2016).
PubMed  PubMed Central  Google Scholar 

76.
Alberti, G. et al. Tree functional diversity influences belowground ecosystem functioning. Appl. Soil Ecol. 120, 160–168 (2017).
Google Scholar 

77.
Ali, A. & Yan, E.-R. Functional identity of overstorey tree height and understorey conservative traits drive aboveground biomass in a subtropical forest. Ecol. Indic. 83, 158–168 (2017).
Google Scholar 

78.
Ali, H. E., Reineking, B. & Münkemüller, T. Effects of plant functional traits on soil stability: intraspecific variability matters. Plant Soil 411, 359–375 (2017).
CAS  Google Scholar 

79.
Barbe, L. et al. Functionally dissimilar neighbors accelerate litter decomposition in two grass species. New Phytol. 214, 1092–1102 (2017).
PubMed  Google Scholar 

80.
Cadotte, M. W. Functional traits explain ecosystem function through opposing mechanisms. Ecol. Lett. 20, 989–996 (2017).
PubMed  Google Scholar 

81.
Chillo, V., Ojeda, R. A., Capmourteres, V. & Anand, M. Functional diversity loss with increasing livestock grazing intensity in drylands: the mechanisms and their consequences depend on the taxa. J. Appl. Ecol. 54, 986–996 (2017).
Google Scholar 

82.
Finney, D. M. & Kaye, J. P. Functional diversity in cover crop polycultures increases multifunctionality of an agricultural system. J. Appl. Ecol. 54, 509–517 (2017).
Google Scholar 

83.
Fornoff, F. et al. Functional flower traits and their diversity drive pollinator visitation. Oikos 126, 1020–1030 (2017).
CAS  Google Scholar 

84.
Fujii, S. et al. Disentangling relationships between plant diversity and decomposition processes under forest restoration. J. Appl. Ecol. 54, 80–90 (2017).
Google Scholar 

85.
Grace, J. B., Harrison, S. & Cornell, H. Is biotic resistance enhanced by natural variation in diversity? Oikos 126, 1484–1492 (2017).
Google Scholar 

86.
Gross, N. et al. Functional trait diversity maximizes ecosystem multifunctionality. Nat. Ecol. Evol. 1, 0132 (2017).
PubMed  PubMed Central  Google Scholar 

87.
Grossman, J. J., Cavender-Bares, J., Hobbie, S. E., Reich, P. B. & Montgomery, R. A. Species richness and traits predict overyielding in stem growth in an early-successional tree diversity experiment. Ecology 98, 2601–2614 (2017).
PubMed  Google Scholar 

88.
Henneron, L. et al. Plant interactions as biotic drivers of plasticity in leaf litter traits and decomposability of Quercus petraea. Ecol. Monogr. 87, 321–340 (2017).
Google Scholar 

89.
Hertzog, L. R., Ebeling, A., Weisser, W. W. & Meyer, S. T. Plant diversity increases predation by ground-dwelling invertebrate predators. Ecosphere 8, e01990 (2017).
Google Scholar 

90.
Khlifa, R., Paquette, A., Messier, C., Reich, P. B. & Munson, A. D. Do temperate tree species diversity and identity influence soil microbial community function and composition? Ecol. Evol. 7, 7965–7974 (2017).
PubMed  PubMed Central  Google Scholar 

91.
Laforest-Lapointe, I., Paquette, A., Messier, C. & Kembel, S. W. Leaf bacterial diversity mediates plant diversity and ecosystem function relationships. Nature 546, 145–147 (2017).
CAS  PubMed  Google Scholar 

92.
Laird-Hopkins, B. C., Bréchet, L. M., Trujillo, B. C. & Sayer, E. J. Tree functional diversity affects litter decomposition and arthropod community composition in a tropical forest. Biotropica 49, 903–911 (2017).
Google Scholar 

93.
Li, W. et al. Community-level trait responses and intra-specific trait variability play important roles in driving community productivity in an alpine meadow on the Tibetan Plateau. J. Plant Ecol. 10, 592–600 (2017).
Google Scholar 

94.
Mao, W., Felton, A. J. & Zhang, T. Linking changes to intraspecific trait diversity to community functional diversity and biomass in response to snow and nitrogen addition within an Inner Mongolian grassland. Front. Plant Sci. 8, 339 (2017).
PubMed  PubMed Central  Google Scholar 

95.
Meyer, S. T. et al. Consistent increase in herbivory along two experimental plant diversity gradients over multiple years. Ecosphere 8, e01876 (2017).
Google Scholar 

96.
Mori, A. S., Osono, T., Cornelissen, J. H. C., Craine, J. & Uchida, M. Biodiversity ecosystem function relationships change through primary succession. Oikos 126, 1637–1649 (2017).
Google Scholar 

97.
Pan, Y. et al. Climatic and geographic factors affect ecosystem multifunctionality through biodiversity in the Tibetan alpine grasslands. J. Mt. Sci. 14, 1604–1614 (2017).
Google Scholar 

98.
Peco, B., Navarro, E., Carmona, C. P., Medina, N. G. & Marques, M. J. Effects of grazing abandonment on soil multifunctionality: the role of plant functional traits. Agric. Ecosyst. Environ. 249, 215–225 (2017).
Google Scholar 

99.
Pérez-Ramos, I. M. et al. Climate variability and community stability in Mediterranean shrublands: the role of functional diversity and soil environment. J. Ecol. 105, 1335–1346 (2017).
Google Scholar 

100.
Poorter, L. et al. Biodiversity and climate determine the functioning of Neotropical forests. Glob. Ecol. Biogeogr. 26, 1423–1434 (2017).
Google Scholar 

101.
Refsland, T. K. & Fraterrigo, J. M. Both canopy and understory traits act as response–effect traits in fire-managed forests. Ecosphere 8, e02036 (2017).
Google Scholar 

102.
Sasaki, T. et al. Differential responses and mechanisms of productivity following experimental species loss scenarios. Oecologia 183, 785–795 (2017).
PubMed  Google Scholar 

103.
Shihan, A. et al. Changes in soil microbial substrate utilization in response to altered litter diversity and precipitation in a Mediterranean shrubland. Biol. Fertil. Soils 53, 171–185 (2017).
Google Scholar 

104.
Steinauer, K., Fischer, F. M., Roscher, C., Scheu, S. & Eisenhauer, N. Spatial plant resource acquisition traits explain plant community effects on soil microbial communities. Pedobiologia (Jena) 65, 50–57 (2017).
Google Scholar 

105.
Sun, Z. et al. Positive effects of tree species richness on fine-root production in a subtropical forest in SE-China. J. Plant Ecol. 10, 146–157 (2017).
Google Scholar 

106.
van der Sande, M. T. et al. Abiotic and biotic drivers of biomass change in a Neotropical forest. J. Ecol. 105, 1223–1234 (2017).
Google Scholar 

107.
Wei, X., Reich, P. B., Hobbie, S. E. & Kazanski, C. E. Disentangling species and functional group richness effects on soil N cycling in a grassland ecosystem. Glob. Change Biol. 23, 4717–4727 (2017).
Google Scholar 

108.
Zhang, Q. et al. Functional dominance rather than taxonomic diversity and functional diversity mainly affects community aboveground biomass in the Inner Mongolia grassland. Ecol. Evol. 7, 1605–1615 (2017).
PubMed  PubMed Central  Google Scholar 

109.
Zirbel, C. R., Bassett, T., Grman, E. & Brudvig, L. A. Plant functional traits and environmental conditions shape community assembly and ecosystem functioning during restoration. J. Appl. Ecol. 54, 1070–1079 (2017).
Google Scholar 

110.
Blesh, J. Functional traits in cover crop mixtures: biological nitrogen fixation and multifunctionality. J. Appl. Ecol. 55, 38–48 (2018).
Google Scholar 

111.
Chillo, V., Vázquez, D. P., Amoroso, M. M. & Bennett, E. M. Land-use intensity indirectly affects ecosystem services mainly through plant functional identity in a temperate forest. Funct. Ecol. 32, 1390–1399 (2018).
Google Scholar 

112.
Fu, H. et al. Hydrological gradients and functional diversity of plants drive ecosystem processes on Poyang Lake wetland. Ecohydrology 11, e1950 (2018).
Google Scholar 

113.
Hao, M., Zhang, C., Zhao, X. & von Gadow, K. Functional and phylogenetic diversity determine wood productivity in temperate forest. Ecol. Evol. 8, 2395–2406 (2018).
PubMed  PubMed Central  Google Scholar 

114.
Mori, A. S. Environmental controls on the causes and functional consequences of tree species diversity. J. Ecol. 106, 113–125 (2018).
Google Scholar 

115.
Navarro-Cano, J. A., Verdú, M. & Goberna, M. Trait-based selection of nurse plants to restore ecosystem functions in mine tailings. J. Appl. Ecol. 55, 1195–1206 (2018).
Google Scholar 

116.
Orwin, K. H. et al. Season and dominant species effects on plant trait–ecosystem function relationships in intensively grazed grassland. J. Appl. Ecol. 55, 236–245 (2018).
Google Scholar 

117.
Roscher, C. et al. Origin context of trait data matters for predictions of community performance of a grassland biodiversity experiment. Ecology 99, 1214–1226 (2018).
PubMed  Google Scholar 

118.
Van de Peer, T., Verheyen, K., Ponette, Q., Setiawan, N. N. & Muys, B. Overyielding in young tree plantations is driven by local complementarity and selection effects related to shade tolerance. J. Ecol. 106, 1096–1105 (2018).
Google Scholar 

119.
Xie, G., Lundholm, J. T. & MacIvor, J. S. Phylogenetic diversity and plant trait composition predict multiple ecosystem functions in green roofs. J. Total Environ. 628–629, 1017–1026 (2018).
Google Scholar 

120.
Poorter, H. et al. Biomass allocation to leaves, stems and roots: meta-analyses of interspecific variation and environmental control. New Phytol. 193, 30–50 (2012).
CAS  PubMed  Google Scholar 

121.
Roscher, C. et al. The role of biodiversity for element cycling and trophic interactions: an experimental approach in a grassland community. Basic Appl. Ecol. 5, 107–121 (2004).
Google Scholar 

122.
Weisser, W. W. et al. Biodiversity effects on ecosystem functioning in a 15-year grassland experiment: patterns, mechanisms, and open questions. Basic Appl. Ecol. 23, 1–73 (2017).
Google Scholar 

123.
Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).
Google Scholar 

124.
Botta-Dukát, Z. Rao’s quadratic entropy as a measure of functional diversity based on multiple traits. J. Veg. Sci. 16, 533–540 (2005).
Google Scholar 

125.
Cadotte, M. W., Carscadden, K. & Mirotchnick, N. Beyond species: functional diversity and the maintenance of ecological processes and services. J. Appl. Ecol. 48, 1079–1087 (2011).
Google Scholar 

126.
van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).
PubMed  Google Scholar 

127.
Sørenson, T. A method of establishing groups of equal amplitude in plant sociology based on similarity of species and its application to analyses of the vegetation on Danish commons. K. Dan. Vid. Selsk. 5, 1–34 (1948).
Google Scholar 

128.
Hector, A. & Bagchi, R. Biodiversity and ecosystem multifunctionality. Nature 448, 188–191 (2007).
CAS  PubMed  Google Scholar 

129.
Siefert, A. et al. A global meta-analysis of the relative extent of intraspecific trait variation in plant communities. Ecol. Lett. 18, 1406–1419 (2015).
PubMed  Google Scholar 

130.
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2017).
PubMed  Google Scholar 

131.
Raffard, A., Santoul, F., Cucherousset, J. & Blanchet, S. The community and ecosystem consequences of intraspecific diversity: a meta-analysis. Biol. Rev. 94, 648–661 (2019).
PubMed  Google Scholar 

132.
Roscher, C. et al. Interspecific trait differences rather than intraspecific trait variation increase the extent and filling of plant community space with increasing plant diversity in experimental grasslands. Perspect. Plant Ecol. Evol. Syst. 33, 42–50 (2018).
Google Scholar 

133.
Bardgett, R. D., Mommer, L. & De Vries, F. T. Going underground: root traits as drivers of ecosystem processes. Trends Ecol. Evol. 29, 692–699 (2014).
PubMed  Google Scholar 

134.
Gounand, I., Little, C. J., Harvey, E. & Altermatt, F. Global quantitative synthesis of ecosystem functioning across climatic zones and ecosystem types. Glob. Ecol. Biogeogr. 29, 1139–1176 (2020).

135.
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).
Google Scholar 

136.
Reich, P. B. et al. Relationship of leaf dark respiration to leaf nitrogen, specific leaf area and leaf life-span: a test across biomes and functional groups. Oecologia 114, 471–482 (1998).
PubMed  Google Scholar 

137.
Cramer, W. et al. Global response of terrestrial ecosystem structure and function to CO2 and climate change: results from six dynamic global vegetation models. Glob. Change Biol. 7, 357–373 (2001).
Google Scholar 

138.
Scheiter, S., Langan, L. & Higgins, S. I. Next-generation dynamic vegetation models: learning from community ecology. New Phytol. 198, 957–969 (2013).
PubMed  Google Scholar 

139.
Millenium Ecosystem Assessment Ecosystems and Human Well-being: Synthesis (Island Press, 2005).

140.
Summary for Policymakers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES, 2019).

141.
Garnier, E. et al. Towards a thesaurus of plant characteristics: an ecological contribution. J. Ecol. 105, 298–309 (2016).
Google Scholar 

142.
Ellenberg, H. Vegetation Mitteleuropas mit den Alpen. In ökologischer, dynamischer und historischer Sicht 5th edn (Ulmer, 1996).

143.
Jochum, M. et al. The results of biodiversity-ecosystem functioning experiments are realistic. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-1280-9 (2020).

144.
de Groot, R., Wilson, M. & Boumans, R. A typology for the classification description and valuation of ecosystem functions, goods and services. Ecol. Econ. 41, 393–408 (2002).
Google Scholar 

145.
Gotschall, F. et al. Tree species identity determines wood decomposition via microclimatic effects. Ecol. Evol. 9, 12113–12127 (2019).
Google Scholar 

146.
Salamanca, F., Kaneko, N. & Katagiri, S. Rainfall manipulation effects on litter decomposition and the microbial biomass of the forest floor. Appl. Soil Ecol. 22, 271–281 (2003).
Google Scholar 

147.
Hu, W. et al. Nitrogen along the hydrological gradient of marsh sediments in a subtropical estuary: pools, processes and fluxes. Int. J. Environ. Res. Public Health 16, 2043 (2019).
CAS  PubMed Central  Google Scholar 

148.
Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. https://doi.org/10.18637/jss.v067.i01 (2015).

149.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018).

150.
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar 

151.
Bartón, K. Package ‘MuMIn’. Model selection and model averaging based on information criteria. R package version 3.0.2. (2018); https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf

152.
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar  More