More stories

  • in

    Javanese Homo erectus on the move in SE Asia circa 1.8 Ma

    Dubois, E. On Pithecanthropus Erectus: a transitional form between man and the apes. J. Anthropol. Inst. G. B. Irel. 25, 240–255 (1896).
    Google Scholar 
    von Koenigswald, G. H. R. Neue Pithecanthropus-funde, 1936-1938 : ein beitrag zur Kenntnis der Praehominiden Wetenschappelijke Mededeelingen ; no. 28 (Landsdrukkerij, Batavia, 1940).Janssen, R. et al. Tooth enamel stable isotopes of Holocene and Pleistocene fossil fauna reveal glacial and interglacial paleoenvironments of hominins in Indonesia. Quatern. Sci. Rev. 144, 145–154 (2016).ADS 

    Google Scholar 
    Bettis, E. A. et al. Way out of Africa: Early Pleistocene paleoenvironments inhabited by Homo erectus in Sangiran, Java. J. Hum. Evol. 56(1), 11–24 (2009).PubMed 

    Google Scholar 
    Huffman, O. Geologic context and age of the Perning/Mojokerto Homo erectus, East Java. J. Hum. Evol. 40(4), 353–362 (2001).PubMed 

    Google Scholar 
    Sarr, A.-C. et al. Subsiding Sundaland. Geology (Boulder) 47(2), 119–122 (2019).ADS 

    Google Scholar 
    Salles, T. et al. Quaternary landscape dynamics boosted species dispersal across Southeast Asia. Commun. Earth Environ. 2(1), 1–12 (2021).MathSciNet 

    Google Scholar 
    Husson, L., Boucher, F. C., Sarr, A., Sepulchre, P. & Cahyarini, S. Y. Evidence of Sundaland’s subsidence requires revisiting its biogeography. J. Biogeogr. 47(4), 843–853 (2020).Winder, I. C. et al. Evolution and dispersal of the genus Homo: A landscape approach. J. Hum. Evol. 87, 48–65 (2015).PubMed 

    Google Scholar 
    Carotenuto, F. et al. Venturing out safely: The biogeography of Homo erectus dispersal out of Africa. J. Hum. Evol. 95, 1–12 (2016).PubMed 

    Google Scholar 
    Larick, R. et al. Early Pleistocene 40Ar/39Ar ages for Bapang Formation hominins, Central Jawa, Indonesia. Proc. Natl. Acad. Sci. PNAS 98(9), 4866–4871 (2001).ADS 
    PubMed 

    Google Scholar 
    Swisher, C. C., Curtis, G. H., Jacob, T., Getty, A. G. & Suprijo, A. Age of the earliest known hominids in Java, Indonesia. Science 263(5150), 1118–1121 (1994).ADS 
    PubMed 

    Google Scholar 
    Sémah, F., Saleki, H., Falguŕes, C., Féraud, G. & Djubiantono, T. Did early man reach Java during the Late Pliocene?. J. Archaeol. Sci. 27(9), 763–769 (2000).
    Google Scholar 
    Bettis, E. et al. Landscape development preceding Homo erectus immigration into Central Java, Indonesia: The Sangiran Formation Lower Lahar. Palaeogeogr. Palaeoclimatol. Palaeoecol. 206(1), 115–131 (2004).
    Google Scholar 
    Matsu’ura, S. et al. Age control of the first appearance datum for Javanese Homo erectus in the Sangiran area. Science 367(6474), 210–214 (2020).ADS 
    PubMed 

    Google Scholar 
    Granger, D. E. & Muzikar, P. F. Dating sediment burial with in situ-produced cosmogenic nuclides: Theory, techniques, and limitations. Earth Planet. Sci. Lett. 188(1), 269–281 (2001).ADS 

    Google Scholar 
    Shen, G., Gao, X., Gao, B. & Granger, D. E. Age of Zhoukoudian Homo erectus determined with 26Al/10Be burial dating. Nature 458(7235), 198–200 (2009).ADS 
    PubMed 

    Google Scholar 
    Pappu, S. et al. Early Pleistocene presence of Acheulian Hominins in South India. Science 331(6024), 1596–1599 (2011).ADS 
    PubMed 

    Google Scholar 
    Lebatard, A.-E. et al. Dating the Homo erectus bearing travertine from Kocabaş (Denizli, Turkey) at at least 1.1 Ma. Earth Planet. Sci. Lett.390, 8–18 (2014).Lebatard, A.-E., Bourlès, D. L. & Braucher, R. Absolute dating of an Early Paleolithic site in Western Africa based on the radioactive decay of in situ-produced 10Be and 26Al. Nucl. Instrum. Methods Phys. Res. Sect. B 456, 169–179 (2019).ADS 

    Google Scholar 
    Braucher, R., Oslisly, R., Mesfin, I., Ntoutoume Mba, P. P. & Team, A. In situ-produced 10 Be and 26 Al indirect dating of Elarmékora Earlier Stone Age artifacts: First attempt in a savannah forest mosaic in the middle Ogooué valley, Gabon. Philos. Trans. Biol. Sci. (2021) .Grimaud-Hervé, D. et al. Position of the posterior skullcap fragment from Sendang Klampok (Sangiran Dome, Java, Indonesia) among the Javanese Homo erectus record. Quatern. Int. 416, 193–209 (2016).
    Google Scholar 
    Sartono, S. Observations on a new skull of Pithecanthropus erectus (Pithecanthropus VIII), from Sangiran, Central Java. Koninklijke Akademie Wetenschappen te Amsterdam 74, 185–194 (1971).
    Google Scholar 
    Wessel, P., Smith, W. H. F., Scharroo, R., Luis, J. & Wobbe, F. Generic mapping tools: Improved version released. EOS Trans. Am. Geophys. Union 94(45), 409–410. https://doi.org/10.1002/2013EO450001 (2013).Article 
    ADS 

    Google Scholar 
    Antón, S., Potts, R. & Aiello, L. Evolution of Early Homo: An integrated biological perspective. Science (New York, N.Y.)345 (2014). https://doi.org/10.1126/science.1236828.Luo, L. et al. The first radiometric age by isochron 26Al/10Be burial dating for the Early Pleistocene Yuanmou hominin site, southern China. Quat. Geochronol. 55, 101022. https://doi.org/10.1016/j.quageo.2019.101022 (2019).Article 

    Google Scholar 
    Zaim, Y. et al. New 1.5 million-year-old Homo erectus maxilla from Sangiran (Central Java, Indonesia). J. Hum. Evol.61(4), 363–376 (2011).Rizal, Y. et al. Last appearance of Homo erectus at Ngandong, Java, 117,000–108,000 years ago. Nature 577(7790), 381–385 (2020).PubMed 

    Google Scholar 
    McRae, B. & Beier, P. Circuit theory predicts gene flow in plant and animal populations. Proc. Natl. Acad. Sci. USA 104, 19885–90. https://doi.org/10.1073/pnas.0706568104 (2008).Article 
    ADS 

    Google Scholar 
    Quaglietta, L. & Porto, M. SiMRiv: An R package for mechanistic simulation of individual, spatially-explicit multistate movements in rivers, heterogeneous and homogeneous spaces incorporating landscape bias. Mov. Ecol. https://doi.org/10.1186/s40462-019-0154-8 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Landau, V. A., Shah, V. B., Anantharaman, R. & Hall, K. R. Omniscape.jl: Software to compute omnidirectional landscape connectivity. J. Open Source Softw.6(57), 2829 (2021). https://doi.org/10.21105/joss.02829.Salles, T., Mallard, C. & Zahirovic, S. gospl: Global Scalable Paleo Landscape Evolution. J. Open Source Softw.5(56), 2804 (2020). https://doi.org/10.21105/joss.02804.Husson, L. et al. Slow geodynamics and fast morphotectonics in the far East Tethys. Geochem. Geophys. Geosyst. 23(1), n/a (2022).Valdes, P., Scotese, C. & Lunt, D. Deep ocean temperatures through time. Climate Past 17, 1483–1506. https://doi.org/10.5194/cp-17-1483-2021 (2021).Article 
    ADS 

    Google Scholar 
    Hyodo, M. et al. High-resolution record of the Matuyama–Brunhes transition constrains the age of Javanese Homo erectus in the Sangiran dome, Indonesia. Proc. Natl. Acad. Sci. PNAS 108(49), 19563–19568 (2011).ADS 
    PubMed 

    Google Scholar 
    Brasseur, B., Sémah, F., Sémah, A.-M. & Djubiantono, T. Pedo-sedimentary dynamics of the Sangiran dome hominid bearing layers (Early to Middle Pleistocene, central Java, Indonesia): A palaeopedological approach for reconstructing ‘Pithecanthropus’ (Javanese Homo erectus) palaeoenvironment. Quatern. Int. 376, 84–100 (2015).
    Google Scholar 
    Falguéres, C. et al. Geochronology of early human settlements in Java: What is at stake?. Quatern. Int. 416, 5–11 (2016).
    Google Scholar 
    Roach, N. et al. Pleistocene footprints show intensive use of lake margin habitats by Homo erectus groups. Sci. Rep. 121 (2016). https://doi.org/10.1038/srep26374.Simandjuntak, T. O. & Barber, A. J. Contrasting tectonic styles in the Neogene orogenic belts of Indonesia. Geol. Soc. Spec. Pub. 106(1), 185–201 (1996).
    Google Scholar 
    Clements, B., Hall, R., Smyth, H. R. & Cottam, M. A. Thrusting of a volcanic arc; a new structural model for Java. Pet. Geosci. 15(2), 159–174 (2009).
    Google Scholar 
    Joordens, J., Wesselingh, F., de Vos, J., Vonhof, H. & Kroon, D. Relevance of aquatic environments for hominins: A case study from Trinil (Java, Indonesia). J. Hum. Evol. 57(6), 656–671 (2009).PubMed 

    Google Scholar 
    Berghuis, H. et al. Hominin homelands of East Java: Revised stratigraphy and landscape reconstructions for Plio-Pleistocene Trinil. Quatern. Sci. Rev. 260, 106912 (2021).
    Google Scholar 
    Fort, J., Pujol, T. & Cavalli-Sforza, L. Palaeolithic populations and waves of advance. Camb. Archaeol. J. 14, 53–61. https://doi.org/10.1017/S0959774304000046 (2004).Article 

    Google Scholar 
    Hamilton, M. & Buchanan, B. Spatial gradients in Clovis-age radiocarbon dates across North America suggest rapid colonization from the north. Proc. Natl. Acad. Sci. USA 104, 15625–30. https://doi.org/10.1073/pnas.0704215104 (2007).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hazelwood, L. & Steele, J. Spatial dynamics of human dispersals: Constraints on modelling and archaeological validation. J. Archaeol. Sci. 31, 669–679. https://doi.org/10.1016/j.jas.2003.11.009 (2004).Article 

    Google Scholar 
    Bae, C., Li, F., Liuling, C., Wang, W. & Hanlie, H. Hominin distribution and density patterns in pleistocene China: Climatic influences. Palaeogeogr. Palaeoclimatol. Palaeoecol. 512 (2018). https://doi.org/10.1016/j.palaeo.2018.03.015.Timmermann, A. et al. Climate effects on archaic human habitats and species successions. Nature 604, 1–7. https://doi.org/10.1038/s41586-022-04600-9 (2022).Article 

    Google Scholar 
    Bailey, G. N., Reynolds, S. C. & King, G. C. Landscapes of human evolution: Models and methods of tectonic geomorphology and the reconstruction of hominin landscapes. J. Hum. Evol. 60(3), 257–280 (2011).PubMed 

    Google Scholar 
    Sarr, A., Sepulchre, P. & Husson, L. Impact of the Sunda Shelf on the Climate of the Maritime Continent. J. Geophys. Res. Atmos. 124(5), 2574–2588 (2019).ADS 

    Google Scholar 
    Louys, J. & Roberts, P. Environmental drivers of megafauna and hominin extinction in Southeast Asia. Nature 586(7829), 402–406 (2020).ADS 
    PubMed 

    Google Scholar 
    Raia, P. et al. Past extinctions of homo species coincided with increased vulnerability to climatic change. One Earth 3(4), 480–490 (2020).ADS 

    Google Scholar 
    Zhu, Z. et al. Hominin occupation of the Chinese Loess Plateau since about 2.1 million years ago. Nature 559(7715), 608–612 (2018).Gabunia, L. et al. Earliest Pleistocene hominid cranial remains from Dmanisi, Republic of Georgia: Taxonomy, geological setting, and age. Science 288, 1019–1025. https://doi.org/10.1126/science.288.5468.1019 (2000).Article 
    ADS 
    PubMed 

    Google Scholar 
    Lordkipanidze, D. et al. A complete skull from Dmanisi, Georgia, and the evolutionary biology of early homo. Science 342(6156), 326–331 (2013).ADS 
    PubMed 

    Google Scholar 
    Baba, H. et al. Homo erectus calvarium from the pleistocene of java. Sci. (Am. Assoc. Adv. Sci.) 299 (5611), 1384–1388 (2003) .Ciochon, R. L. & Bettis, E. A. III. Asian Homo erectus converges in time. Nature 458(7235), 153–154 (2009).ADS 
    PubMed 

    Google Scholar 
    Dennell, R. & Roebroeks, W. An Asian perspective on early human dispersal from Africa. Nature 438(7071), 1099–1104 (2005).ADS 
    PubMed 

    Google Scholar 
    Martinon-Torres, M. et al. Dental evidence on the hominin dispersals during the Pleistocene. Proc. Natl. Acad. Sci. PNAS 104(33), 13279–13282 (2007).ADS 
    PubMed 

    Google Scholar 
    Wood, B. Did early Homo migrate “out of’’ or “in to’’ Africa?. Proc. Natl. Acad. Sci. PNAS 108(26), 10375–10376 (2011).ADS 
    PubMed 

    Google Scholar 
    Shen, G. et al. Isochron 26Al/10Be burial dating of Xihoudu: Evidence for the earliest human settlement in northern China. Anthropologie 124, 102790. https://doi.org/10.1016/j.anthro.2020.102790 (2020).Article 

    Google Scholar 
    Chmeleff, J., von Blanckenburg, F., Kossert, K. & Jakob, D. Determination of the 10Be half-life by multicollector ICP-MS and liquid scintillation counting. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms268(2), 192–199 (2010).Korschinek, G. et al. A new value for the half-life of 10Be by Heavy-Ion Elastic Recoil Detection and liquid scintillation counting. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 268(2), 187–191 (2010) .Nishiizumi, K. Preparation of 26Al AMS standards. Nucl. Inst. and Meth. in Phys. Res. 223-224, 388–392 (2004).Norris, T. L., Gancarz, A. J., Rokop, D. J. & Thomas, K. W. Half-life of 26Al. J. Geophys. Res. Solid Earth 88(S01), B331–B333 (1983).ADS 

    Google Scholar 
    Braucher, R., Merchel, S., Borgomano, J. & Bourlès, D. Production of cosmogenic radionuclides at great depth: A multi element approach. Earth Planet. Sci. Lett. 309(1), 1–9 (2011).ADS 

    Google Scholar 
    Braucher, R. et al. Preparation of ASTER in-house 10Be/9Be standard solutions. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms361, 335–340 (2015) .Merchel, S. & Bremser, W. First international 26Al interlaboratory comparison—Part I. Nucl. Instrum. Methods Phys. Res. 223–224, 393–400 (2004).ADS 

    Google Scholar 
    Arnold, M. et al. The French accelerator mass spectrometry facility ASTER: Improved performance and developments. Nucl. Instrum. Methods Phys. Res. 268(11), 1954–1959 (2010).ADS 

    Google Scholar 
    Borchers, B. et al. Geological calibration of spallation production rates in the CRONUS-Earth project. Quat. Geochronol. 31, 188–198 (2016).
    Google Scholar 
    Stone, J. O. Air pressure and cosmogenic isotope production. J. Geophys. Res. Solid Earth 105(B10), 23753–23759 (2000).
    Google Scholar 
    Bintanja, R. & van de Wal, R. S. W. North American ice-sheet dynamics and the onset of 100,000-year glacial cycles. Nature 454, 869–872. https://doi.org/10.1038/nature07158 (2008).Article 
    ADS 
    PubMed 

    Google Scholar 
    Field, J. & Mirazon Lahr, M. Assessment of the Southern Dispersal: GIS-Based Analyses of Potential Routes at Oxygen Isotopic Stage 4. J. World Prehist. 19, 1–45 (2005). https://doi.org/10.1007/s10963-005-9000-6.Howey, M. Multiple pathways across past landscapes: Circuit theory as a complementary geospatial method to least cost path for modeling past movement. J. Archaeol. Sci. 38, 2523–2535. https://doi.org/10.1016/j.jas.2011.03.024 (2011).Article 

    Google Scholar 
    Tassi, F. et al. Early modern human dispersal from Africa: Genomic evidence for multiple waves of migration. Investig. Genet. 6, 13. https://doi.org/10.1186/s13323-015-0030-2 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kealy, S., Louys, J. & O’Connor, S. Least-cost pathway models indicate northern human dispersal from Sunda to Sahul. J. Hum. Evol. 125, 59–70. https://doi.org/10.1016/j.jhevol.2018.10.003 (2018).Article 
    PubMed 

    Google Scholar 
    Dennell, R. W., Rendell, H. M. & Hailwood, E. Late pliocene artefacts from northern Pakistan. Curr. Anthropol. 29(3), 495–498 (1988).
    Google Scholar 
    Zhu, R. et al. Early evidence of the genus homo in east asia. J. Hum. Evol. 55(6), 1075–1085 (2008).PubMed 

    Google Scholar 
    Gowen, K. M. & de Smet, T. S. Testing least cost path (LCP) models for travel time and kilocalorie expenditure: Implications for landscape genomics. PLoS ONE 15(9), 1–20. https://doi.org/10.1371/journal.pone.0239387 (2020).Article 

    Google Scholar 
    Walt, S. et al. scikit-image: Image processing in Python. PeerJ 2, e453. https://doi.org/10.7287/peerj.preprints.336v2 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mueller, T. & Fagan, W. Search and navigation in dynamic environments—From individual behaviors to population distributions. Oikos 117, 654–664. https://doi.org/10.1111/j.0030-1299.2008.16291.x (2008).Article 

    Google Scholar 
    Bastille-Rousseau, G., Douglas-Hamilton, I., Blake, S., Northrup, J. & Wittemyer, G. Applying network theory to animal movements to identify properties of landscape space use. Ecol. Appl. 28 (2018). https://doi.org/10.1002/eap.1697.Michelot, T., Langrock, R. & Patterson, T. moveHMM: An R package for the statistical modelling of animal movement data using hidden Markov models. Methods Ecol. Evol. 7 (2016). https://doi.org/10.1111/2041-210X.12578 .Benhamou, S. How many animals really do the Lévy Walk. Ecology 88, 1962–9. https://doi.org/10.1890/06-1769.1 (2007).Article 
    PubMed 

    Google Scholar 
    Turchin, P. Quantitative Analysis of Movement: Measuring and Modeling Population Redistribution of Plants and Animals (Sinauer Associates, Sunderland, 1998).
    Google Scholar 
    Lieberman, D. E. The Story of the Human Body: Evolution, Health, and Disease (Pantheon Books, New York, 2013).
    Google Scholar 
    Braun, D. et al. Early hominin diet included diverse terrestrial and aquatic animals 1.95 Ma in East Turkana, Kenya. Proc. Natl. Acad. Sci. USA 107, 10002–7 (2010). https://doi.org/10.1073/pnas.1002181107.O’Connor, S., Louys, J., Kealy, S. & Samper Carro, S. C. Hominin dispersal and settlement east of huxley’s line: The role of sea level changes, island size, and subsistence behavior. Curr. Anthropol. 58(S17), S567–S582 (2017).Macaulay, V. et al. Single, rapid coastal settlement of asia revealed by analysis of complete mitochondrial genomes. Science (New York, N.Y.)308, 1034–6 (2005). https://doi.org/10.1126/science.1109792. More

  • in

    The expanding value of long-term studies of individuals in the wild

    Lack, D. J. Anim. Ecol. 33, 159–173 (1964).Article 

    Google Scholar 
    Pemberton, J. et al. The unusual value of long-term studies of individuals: the example of the Isle of Rum red deer project. Annu. Rev. Ecol. Evol. Syst. (in the press).Clutton-Brock, T. & Sheldon, B. C. Trends Ecol. Evol. 25, 562–573 (2010).Article 
    PubMed 

    Google Scholar 
    Weimerskirch, H. J. Anim. Ecol. 87, 945–955 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Höner, O. P. et al. Nature 448, 798–801 (2007).Article 
    PubMed 

    Google Scholar 
    Rodríguez-Muñoz, R. et al. Evolution 73, 317–328 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Czorlich, Y., Aykanat, T., Erkinaro, J., Orell, P. & Primmer, C. R. Science 376, 420–423 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sparkman, A. M., Arnold, S. J. & Bronikowski, A. M. Proc. R. Soc. Lond. B 274, 943–950 (2007).
    Google Scholar 
    Doak, D. F. & Morris, W. F. Nature 467, 959–962 (2010).Article 
    CAS 
    PubMed 

    Google Scholar 
    Campos, F. A. et al. Proc. Natl Acad. Sci. USA 119, e2117669119 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Forchhammer, M. C., Clutton-Brock, T. H., Lindström, J. & Albon, S. D. J. Anim. Ecol. 70, 721–729 (2001).Article 

    Google Scholar 
    Bonnet, T. et al. PLoS Biol. 17, e3000493 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    McCleery, R. H. & Perrins, C. M. Nature 391, 30–31 (1998).Article 
    CAS 

    Google Scholar 
    Charmantier, A. et al. Science 320, 800–803 (2008).Article 
    CAS 
    PubMed 

    Google Scholar 
    Vedder, O., Bouwhuis, S. & Sheldon, B. C. PLoS Biol. 11, e1001605 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Simmonds, E. G., Cole, E. F., Sheldon, B. C. & Coulson, T. Ecol. Lett. 23, 1766–1775 (2020).Article 
    PubMed 

    Google Scholar 
    Cole, E. F., Regan, C. E. & Sheldon, B. C. Nat. Clim. Chang. 11, 872–878 (2021).Article 

    Google Scholar 
    Huisman, J., Kruuk, L. E., Ellis, P. A., Clutton-Brock, T. & Pemberton, J. M. Proc. Natl Acad. Sci. USA 113, 3585–3590 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Johnston, S. E., Bérénos, C., Slate, J. & Pemberton, J. M. Genetics 203, 583–598 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stoffel, M. A., Johnston, S. E., Pilkington, J. G. & Pemberton, J. M. Nat. Commun. 12, 2972 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Grieneisen, L. et al. Science 373, 181–186 (2021).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Björk, J. R. et al. Nat. Ecol. Evol. 6, 955–964 (2022).Article 
    PubMed 

    Google Scholar 
    Lamichhaney, S. et al. Science 352, 470–474 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bosse, M. et al. Science 358, 365–368 (2017).Article 
    CAS 
    PubMed 

    Google Scholar 
    De Villemereuil, P. et al. Proc. Natl Acad. Sci. USA 117, 31969–31978 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bailey, L. D. et al. Nat. Commun. 13, 2112 (2022).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bonnet, T. et al. Science 376, 1012–1016 (2022).Article 
    CAS 
    PubMed 

    Google Scholar 
    Culina, A. et al. J. Anim. Ecol. 90, 2147–2160 (2021).Article 
    PubMed 

    Google Scholar  More

  • in

    Rebooting GDP: new ways to measure economic growth gain momentum

    The numbers are heading in the wrong direction. If the world continues on its current track, it will fall well short of achieving almost all of the 17 Sustainable Development Goals (SDGs) that the United Nations set to protect the environment and end poverty and inequality by 2030.The projected grade for:Eliminating hunger: F.Ensuring healthy lives for all: F.Protecting and sustainably using ocean resources: F.The trends were there before 2020, but then problems increased with the COVID-19 pandemic, war in Ukraine and the worsening effects of climate change. The world is in “a new uncertainty complex”, says economist Pedro Conceição, lead author of the United Nations Human Development Report.One measure of this is the drastic change in the Human Development Index (HDI), which combines educational outcomes, income and life expectancy into a single composite indicator. After 2019, the index has fallen for two successive years for the first time since its creation in 1990. “I don’t think this is a one-off, or a blip. I think this could be a new reality,” Conceição says.UN secretary-general António Guterres is worried. “We need an urgent rescue effort for the SDGs,” he wrote in the foreword to the latest progress report, published in July. Over the past year, Guterres and the heads of big UN agencies, such as the Statistics Division and the UN Development Programme, have been assessing what’s gone wrong and what needs to be done. They’re converging on the idea that it’s time to stop using gross domestic product (GDP) as the world’s main measure of prosperity, and to complement it with a dashboard of indicators, possibly ones linked to the SDGs. If this happens, it would be the biggest shift in how economies are measured since nations first started using GDP in 1953, almost 70 years ago1.
    Get the Sustainable Development Goals back on track
    Guterres’s is the latest in a crescendo of voices calling for GDP to be dropped as the world’s primary go-to indicator, and for a dashboard of metrics instead. In 2008, then French president Nicolas Sarkozy endorsed such a call from a team of economists, including Nobel laureates Amartya Sen and Joseph Stiglitz.And in August, the White House announced a 15-year plan to develop a new summary statistic that would show how changes to natural assets — the natural wealth on which economies depend — affect GDP. The idea, according to the project’s main architect, economist Eli Fenichel at the White House Office of Science and Technology Policy, is to help society to determine whether today’s consumption is being accomplished without compromising the future opportunities that nature provides. “GDP only gives a partial and — for many common uses — an incomplete, picture of economic progress,” Fenichel says.The fact that Guterres has made this a priority, amid so many major crises, is a sign that “going beyond GDP has been picked up at the highest level”, says Stefan Schweinfest, the director of the UN Statistics Division, based in New York City.Grappling with growth GDP is a measure of economic activity that has ended up becoming the world’s main index for economic progress. By a commonly used definition, it is the numerical sum of countries’ consumer and government spending and their business investments, adding the value of exports minus imports. When governments and businesses talk, as they regularly do, about boosting ‘economic growth’, what they mean is boosting GDP.But GDP is more than a growth target. It is also the benchmark for how countries measure themselves against each other (see ‘Growth gaps’). The United States is the world’s largest economy, as measured by GDP. China, currently second, is on a path to overtake it.

    Source: World Bank

    GDP also matters greatly to politicians. When India leapfrogged the United Kingdom to become the world’s fifth largest economy earlier this year, it made headline news. Last month, China reportedly delayed publication of its latest (and less-than-flattering) quarterly GDP figures so they would not appear during the Communist party’s national congress, at which Xi Jinping took a third term as president.“GDP is without question the superstar of indicators,” says Rutger Hoekstra, a researcher who studies sustainability metrics at Leiden University in the Netherlands and author of Replacing GDP by 2030.The problem with using GDP as a proxy for prosperity, says Hoekstra, is that it doesn’t reflect equally important indicators that have been heading in the opposite direction. Global GDP has increased exponentially since the Industrial Revolution, but this has coincided with high levels of income and wealth inequality, according to data compiled by the economist Thomas Piketty at the World Inequality Lab in Paris2. This is not a coincidence. Back in the 1950s, when countries pivoted economies to maximizing GDP, they knew it would mean “making the labourer produce more than he is allowed to consume”, as Pakistan’s then chief economist Mahbub ul Haq graphically put it3. “It is well to recognize that economic growth is a brutal, sordid process.”What is more, to boost GDP, nations need to indulge in environmentally damaging behaviour. In his 2021 report, entitled Our Common Agenda, Guterres writes: “Absurdly, GDP rises when there is overfishing, cutting of forests or burning of fossil fuels. We are destroying nature, but we count it as an increase in wealth.”This tension is apparent when it comes to the SDGs. GDP growth is associated with several SDG targets; in fact SDG 8 is about boosting growth. But GDP growth “can also come at the expense of progress towards other goals”, such as climate and biodiversity action, says environmental economist Pushpam Kumar, who directs a UN Environment Programme (UNEP) project, called the Inclusive Wealth Report, to measure sustainability and inequality. The latest report will be published next month.The one-number problemThe present effort by Guterres and his colleagues is not the first time policymakers have tried to improve on GDP. In 1990, a group of economists led by ul Haq and Sen designed the HDI. They were motivated in part by frustration that their countries’ often impressive growth rates masked more-dismal quality-of-life data, such as life expectancy or education.More recently, environment ministers have found that GDP-boosting priorities have got in the way of their SDG efforts. Carlos Manuel Rodríguez, the former environment minister of Costa Rica, says he urged his finance and economics colleagues to take account of the impact of economic development on water, soils, forests and fish. But they were concerned about possible reductions in GDP calculations, says Rodríguez, now chief executive of the Global Environment Facility, based in Washington DC. Costa Rica didn’t want to be the first country to implement such a change only to possibly see itself slide down the growth rankings as a result.

    Industrial production, such as the work at this automobile plant in Japan, goes into GDP calculations.Credit: Akio Kon/Bloomberg via Getty

    China’s environmental policymakers were confronted with a similar response when, in 2006, they tried to implement a plan called Green GDP4. Local authorities were asked to measure the economic cost of pollution and environmental damage, and offset that against their economic growth targets. “They panicked and the project was shelved,” says Vic Li, a political economist at the Education University of Hong Kong, who has studied the episode. “Reducing GDP would have affected their performance reviews, which needed GDP to always increase,” he says.It’s been a similar story in Italy. In 2019, then research minister Lorenzo Fioramonti helped to establish an agency, Well-being Italy, attached to the prime minister’s office. It was intended to test economic policy decisions against sustainability targets. “It was an uphill battle because the various economic ministries did not see this as a priority,” says Fioramonti, now an economist at the University of Surrey in Guildford, UK.Revising the rulesSo, can the latest attempt to complement GDP succeed? Economists and national statisticians who help to determine GDP’s rules say it will be a struggle.Guterres and his colleagues are proposing to include 10–20 indicators alongside GDP. But that’s a tough sell because countries see a lot of value (not to mention ease of use) in relying on one number. And GDP’s great success is that countries produce their own figures, according to internationally agreed rules, which allow for cross-comparison over time. “It’s not a metric compiled by Washington DC, Beijing or London,” says Schweinfest.At the same time, GDP is not something that can just be turned on or off. In each country, tracking the data that goes into calculating GDP is an industrial-scale operation involving government data as well as surveys of households and businesses.
    Are there limits to economic growth? It’s time to call time on a 50-year argument
    China, Costa Rica and Italy’s experiences suggest that an environment-adjusted GDP might be accepted only if every country signs up to the concept at the same time. In theory, this could happen at the point when GDP’s rules — known as the System of National Accounts — are being reviewed, an event that takes place roughly once every 15 years.The next revision to the rules is under way and is due to be completed in 2025. The final decision will be made by the UN Statistical Commission, a group of chief statisticians from different nations, together with the European Commission, the International Monetary Fund, the World Bank and the Organisation for Economic Co-operation and Development (OECD), the network of the world’s wealthy countries.Because the UN oversees this process, Guterres has some influence over the questions that the review is asking. As part of their research, national statisticians are exploring how to measure well-being and sustainability, along with improving the way the digital economy is valued. Economists Diane Coyle and Annabel Manley, both at the University of Cambridge, say that technology and data companies, which make up seven out of the global top ten firms by stock-market capitalization, are probably undervalued in national accounts5.However, according to Peter van de Ven, a former OECD statistician who is the lead editor of the GDP revision effort, some aspects of digital-economy valuation, along with putting a value on the environment, are unlikely to make it into a revised GDP formula, and will instead be part of the report’s supplementary data tables. One of the reasons, he says, is that national statisticians have not agreed on a valuation methodology for the environment. Nor is there agreement on how to value digital services such as when people use search engines or social-media accounts that (like the environment) are not bought and sold for money.Yet other economists, including Fenichel, say that there are well-established methods that economists use to value both digital and environmental goods and services. One way involves asking people what they would be willing to pay to keep or use something that might otherwise be free, such as a forest or an Internet search engine. Another method involves asking what people would be willing to accept in exchange for losing something otherwise free. Management scientists Erik Brynjolfsson and Avinash Collis, both at the Massachusetts Institute of Technology in Cambridge, did an experiment6 in which they computed the value of social media by paying people to give up using it.The value of natureEconomist Gretchen Daily at Stanford University in California says it’s not true that valuing the environment would make economies look smaller. It all depends on what you value. Daily is among the principal investigators of a project called Gross Ecosystem Product (GEP) that has been trialled across China and is now set to be replicated in other countries. GEP adds together the value of different kinds of ecosystem goods and services, such as agricultural products, water, carbon sequestration and recreational sites. The researchers found7 that in the Chinese province of Qinghai, the region’s total GEP exceeded its GDP.Although past efforts to avoid using GDP have stalled, this time could be different. It’s likely, as van de Ven says, that national statisticians will not add nature (or indeed the value of social media and Internet search) to the GDP formula. But the pressure for change is greater than at any time in the past.GDP is like a technical standard, such as the voltage of household electricity or driving on the left, says Coyle. “So if you want to switch to the right, you need to align people on the same approach. Everyone needs to agree.” More

  • in

    Phosphate limitation intensifies negative effects of ocean acidification on globally important nitrogen fixing cyanobacterium

    Laboratory experimentsCulturingThe marine cyanobacterium Trichodesmium erythraeum IMS101 was obtained from the National Center for Marine Algae and Microbiota (Maine, USA) and was grown in Aquil-tricho medium prepared with 0.22 µm-filtered and microwave-sterilized oligotrophic South China Sea surface water6. The medium was enriched with various concentrations of chelexed and filter-sterilized NaH2PO4 as where indicated, and filter-sterilized vitamins and trace metals buffered with 20 µM EDTA6. The cultures were unialgal, and although they were not axenic, sterile trace metal clean techniques were applied for culturing and experimental manipulations. T. erythraeum was pre-adapted to low P condition by semi-continuously culturing at 0.5 μM PO43− and at two pCO2 levels (400 and 750 µatm) for more than one year. To start the chemostat culture, three replicates per treatment were grown in 1-L Nalgene® magnetic culture vessels (Nalgene Nunc International, Rochester, NY, USA), in which the cultures were continuously mixed by bubbling with humidified and 0.22 µm-filtered CO2–air mixtures and stirring using a suspended magnetic stir bar. The reservoirs contained Aquil-tricho medium with 1.2 μM NaH2PO4, which was delivered to the culture vessels using a peristaltic pump (Masterflex® L/S®, USA) at the dilution rate of 0.2 d−1. In all experiments, cultures were grown at ;27 °C and ~80 μmol photons m−2 s−1 (14 h:10 h light–dark cycle) in an AL-41L4 algae chamber (Percival). The concentration of Chlorophyll a (Chla) was monitored daily in the middle of the photoperiod as an indicator of biomass. When the Chla concentration remained constant for more than one generation, the system was considered to have reached steady-state, and was maintained for at least another four generations prior to sampling for further analysis.Carbonate chemistry manipulationpCO2/pH of seawater media in the culture vessels and in the reservoir was controlled by continuously bubbling with humidified and 0.22 µm-filtered CO2-air mixtures generated by CO2 mixers (Ruihua Instrument & Equipment Ltd.). During the experimental period, the pHT (pH on the total scale) of media was monitored daily using a spectrophotometric method46. The dissolved inorganic carbon (DIC) of media was analyzed by acidification and subsequent quantification of released CO2 with a CO2 analyzer (LI 7000, Apollo SciTech). Calculations of alkalinity and pCO2 were made using the CO2Sys program47, based on measurements of pHT and DIC, and the carbonate chemistry of the experiments are shown in Supplementary Table 1.Chla concentration and cell density and sizeChla concentration was measured daily following Hong et al.6. Briefly, T. erythraeum was filtered onto 3 μm polycarbonate membrane filters (Millipore), followed by heating at 65 °C for 6 min in 90% (vol/vol) methanol. After extraction the filter was removed and cell debris were spun down via centrifugation (5 min at 20,000×g) before spectrophotometric analysis. Cell density and the average cell length and width were determined at regular intervals when the chemostat cultures reached steady-state using ImageJ software. Photographs of Trichodesmium were taken using a camera (Canon DS126281, Japan) connected with an inverted microscope (Olympus CKX41, Japan). Total number and length of filaments in 1 mL of culture were measured, and the cell number of ~20 filaments was counted. The average length of cells was obtained by dividing the total length of the 20 filaments by their total cell number. The cell density of the culture was then calculated by dividing the total length of filaments in 1 mL culture by the average cell length. The average cell width was determined by measuring the width of around 1000 cells in each treatment.Elemental compositionTo determine particulate organic C (POC) and N (PON), at the end of the chemostat culturing T. erythraeum cells were collected on pre-combusted 25 mm GF/F filters (Whatman) and stored at −80 °C. Prior to analysis, the filters were dried overnight at 60 °C, treated with fuming HCl for 6 h to remove all inorganic carbon, and dried overnight again at 60 °C. After being packed in tin cups, the samples were subsequently analyzed on a PerkinElmer Series II CHNS/O Analyzer 2400.Particulate organic P (POP) was measured following Solorzano et al.48. Cells were filtered on pre-combusted 25 mm GF/F filters and rinsed twice with 2 mL of 0.17 M Na2SO4. The filters were then placed in combusted glass bottles with the addition of 2 mL of 0.017 M MgSO4, and subsequently evaporated to dryness at 95 °C and baked at 450 °C for 2 h. After cooling, 5 mL of 0.2 M HCl was added to each bottle. The bottle was then tightly capped and heated at 80 °C for 30 min, after which 5 mL Milli-Q H2O was added. Dissolved phosphate from the digested POP sample was measured colorimetrically following the standard phosphomolybdenum blue method.C uptake and N2 fixation ratesRates of short-term C uptake were determined at the end of the chemostat culturing. 100 µM NaH14CO3 (PerkinElmer) was added to 50 mL of cultures in the middle of the photoperiod, which was then incubated for 20 min under the growth conditions. After incubation, the samples were collected onto 3 μm polycarbonate membrane filters (Millipore), which were then washed with 0.22 µm-filtered oligotrophic seawater and placed on the bottom of scintillation vials. The filters were acidified to remove inorganic C by adding 500 µL of 2% HCl. The radioactivity was determined using a Tri-Carb 2800TR Liquid Scintillation Analyzer (PerkinElmer). Rates of N2 fixation (nitrogenase activity) were measured in the middle of the photoperiod for 2 h by the acetylene reduction assay49, using a ratio of 4:1 to convert ethylene production to N2 fixation.Soluble reactive phosphate (SRP) analysisWhen the chemostat cultures reached a steady-state, SRP concentrations in the culture vessels were measured at regular intervals, using the classic phosphomolybdenum blue (PMB) method with an additional step to enrich PMB on an Oasis HLB cartridge50. Briefly, 100 mL of GF/F filtered medium sample was fortified with 2 mL of ascorbic acid (100 g L−1) and 2 mL of mixed reagent (MR, the mixture of 100 mL of 130 g L−1 ammonium molybdate tetrahydrate, 100 mL of 3.5 g L−1 potassium antimony tartrate, and 300 mL of 1:1 diluted H2SO4), and then mixed completely. After standing at room temperature for 5 min, the solution was loaded onto a preconditioned Oasis HLB cartridge (3 cm3/60 mg, P/N: WAT094226, Waters Corp.) via a peristaltic pump, and then 1 mL eluent solution (0.2 M NaOH) was added to elute the sample into a cuvette, to which 0.06 mL of MR and 0.03 mL of ascorbic acid solution was added to fully develop PMB. Finally, the absorbance of PMB was measured at 700 nm using a spectrophotometer.Alkaline phosphatase (AP) activityAP activities were measured in the middle of the photoperiod using p-nitrophenylphosphate (pNPP) as a substrate51. Briefly, 5 mL of culture was incubated with 250 μL of 10 mM pNPP, 675 μL of Tris-glycine buffer (50 mM, pH 8.5) and 67.5 μL of 1 mM MgCl2 for 2 h under growth conditions. The absorbance of formed p-nitrophenol (pNP) was measured at 410 nm using a spectrophotometer.PolyP analysisAt the end of the chemostat culturing, T. erythraeum cells were filtered in the middle of the photoperiod onto 3 μm polycarbonate membrane filters (Millipore), flash frozen in liquid nitrogen, and stored at −80 °C until analysis. PolyP was quantified fluorometrically following Martin and Van Mooy22 and Martin et al.23. Briefly, samples were re-suspended in 1 mL Tris buffer (pH 7.0), sonicated for 30 s, immersed in boiling water for 5 min, sonicated for another 30 s, and then digested by 10 U DNase (Takara), RNase (2.5 U RNase A + 100 U RNase T1) (Invitrogen) and 20 μl of 20 mg mL−1 proteinase K at 37 °C for 30 min. After centrifugation for 5 min at 14,000×g, the supernatant was diluted with Tris buffer according to the range of standards curve, stained with 60 μL of 100 μM 4, 6-diamidino-2-phenylindole (DAPI) per 500 μL of samples, incubated for 7 min and then vortexed. The samples were then loaded onto a black 96-well plate and the absorption of fluorescence at an excitation wavelength of 415 nm and emission wavelength of 550 nm was measured using a PerkinElmer EnSpire® Multimode Plate Reader. PolyP standard (sodium phosphate glass Type 45) was purchased from Sigma-Aldrich. This method gives a relative measure of polyP concentration23 that is expressed as femto-equivalents of the standard per cell (feq cell−1).Cellular ATP measurementCellular ATP contents were determined when the chemostat cultures reached a steady state. T. erythraeum cells were collected in the middle of the photoperiod using an ATP Assay Kit (Beyotime Biotechnology, Shanghai, China) according to the manufacturer’s instructions. Briefly, the sample was lysed and centrifuged, and the supernatant (100 μL) was mixed with ATP detection working reagent (100 μL) and loaded onto a black 96-well plate. The luminescence was measured using a PerkinElmer EnSpire® Multimode Plate Reader.Intracellular metabolites measurementsNAD(H), NADP(H), and Glu were measured at the end of the chemostat culturing, using the liquid chromatography-tandem quadrupole mass spectrometry (LC–MS/MS) method modified from Luo et al.52. Briefly, T. erythraeum cells were gently filtered at the middle of photoperiod onto 3 μm polycarbonate membrane filters (Millipore), rapidly suspended in −80 °C precooled methanol-water (60%, v/v) mixture. After being kept in −80 °C freezer for 30 min, the sample was sonicated for 30 s, centrifuged at 12,000×g and 4 °C for 5 min, and the supernatant was filtered through a 0.2 μm filter (Jinteng®, China) and stored at −80 °C for further LC–MS/MS analysis.A 2.0 × 50 mm Phenomenex® Gemini 5u C18 110 Å column (particle size 5.2 µm, Phenomenex, USA) was used for the analysis. The mobile phases consisted of two solvents: mobile phase A (10 mM tributylamine aqueous solution, pH 4.95 with 15 mM acetic acid) and mobile phase B (100% methanol), which were delivered using an Agilent 1290 UPLC binary pump (Agilent Technologies, Palo Alto, CA, USA) at a flow rate of 200 µL min−1, with a linear gradient program implemented as follows: hold isocratic at 0% B (0–2 min); linear gradient from 0% to 85% B (2–28 min); hold isocratic at 0% B (28–34 min). The effluent from the LC column was delivered to an Agilent 6490 triple-quadrupole mass spectrometer, equipped with an electrospray ionization source operating in negative-ion mode. NAD, NADH, NADP, NADPH, and Glu were monitored in the multiple reaction monitoring modes with the transition events at m/z 662.3  > 540, 664.3  > 79, 742  > 620, 744  > 79, and 147  > 84, respectively.RNA extraction, library preparation, and sequencingAt the end of the chemostat culturing, T. erythraeum was collected in the middle of the photoperiod by filtering onto 3 μm polycarbonate membrane filters (Millipore), flash frozen in liquid nitrogen and stored at −80 °C until extraction. Total RNA was extracted using TRIzol® Reagent (Invitrogen) combined with a physical cell disruption approach by glass beads according to the manufacturer’s instructions. Genomic DNA was removed thoroughly by treating it with RNAase-free DNase I (Takara, Japan). Ribosomal RNA was removed from a total amount of 3 µg RNA using Ribo-Zero rRNA Removal kit (Illumina, USA). Subsequently, cDNA libraries were generated according to the manufacturer’s protocol of NEBNext® UltraTM Directional RNA Library Prep Kit for Illumina® (NEB, USA). The quality of the library was assessed on the Agilent Bioanalyzer 2100 system (Agilent Technologies, CA, USA). Libraries were sequenced on an Illumina Hiseq 2500 platform, yielding 136-bp paired-end reads.RNA-Seq bioinformaticsClean reads were obtained from raw data by removing reads containing adapter, ploy-N and low-quality read. Qualified sequences were mapped to the Trichodesmium erythraeum IMS101 genome (https://www.ncbi.nlm.nih.gov/nuccore/NC_008312.1) by using Bowtie2-2.2.353. Differential expression analysis for high/low pCO2 with P limitation was performed using the DESeq2 R package54. The resulting p-values were adjusted using Benjamini and Hochberg’s approach for controlling the false discovery rate. Genes with an adjusted p-value  More

  • in

    Factors influencing lion movements and habitat use in the western Serengeti ecosystem, Tanzania

    Pacifici, M., Di Marco, M. & Watson, J. E. M. Protected areas are now the last strongholds for many imperiled mammal species. Conserv. Lett. 13, 1–7 (2020).
    Google Scholar 
    Cardillo, M. et al. Human population density and extinction risk in the world’s carnivores. PLoS Biol. 2, 909–914 (2004).CAS 

    Google Scholar 
    Steffen, W., Broadgate, W., Deutsch, L., Gaffney, O. & Ludwig, C. The trajectory of the anthropocene: the great acceleration. Anthr. Rev. 2, 81–98 (2015).
    Google Scholar 
    Wolf, C. & Ripple, W. J. Range contractions of the world’s large carnivores. R. Soc. Open Sci. 4, 170052 (2017).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wolf, C. & Ripple, W. J. Prey depletion as a threat to the world’s large carnivores. R. Soc. Open Sci. 3, 160252 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 1241484 (2014).PubMed 

    Google Scholar 
    Geldmann, J. et al. Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol. Conserv. 161, 230–238 (2013).
    Google Scholar 
    Benítez-López, A. et al. The impact of hunting on tropical mammal and bird populations. Science 356, 180–183 (2017).ADS 
    PubMed 

    Google Scholar 
    Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).ADS 
    PubMed 

    Google Scholar 
    Rija, A. A., Critchlow, R., Thomas, C. D. & Beale, C. M. Global extent and drivers of mammal population declines in protected areas under illegal hunting pressure. PLoS One 15, 1–14 (2020).
    Google Scholar 
    Bamford, A. J., Ferrol-Schulte, D. & Wathan, J. Human and wildlife usage of a protected area buffer zone in an area of high immigration. Oryx 48, 504–513 (2014).
    Google Scholar 
    Snyder, K. D., Mneney, P. B. & Wittemyer, G. Predicting the risk of illegal activity and evaluating law enforcement interventions in the western Serengeti. Conserv. Sci. Pract. 1, 1–13 (2019).
    Google Scholar 
    Woodroffe, R. & Ginsberg, J. R. Edge effects and the extinction of populations inside protected areas. Science 280, 2126–2128 (1998).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Lynagh, F. M. & Urich, P. B. A critical review of buffer zone theory and practice: A Philippine case study. Soc. Nat. Resour. 15, 129–145 (2002).
    Google Scholar 
    Paolino, R. M. et al. Buffer zone use by mammals in a Cerrado protected area. Biota Neotrop. 16, (2016).
    Mills, K. L. et al. Comparable space use by lions between hunting concessions and national parks in West Africa. J. Appl. Ecol. 57, 975–984 (2020).ADS 

    Google Scholar 
    Lindsey, P. A. et al. The performance of African protected areas for lions and their prey. Biol. Conserv. 209, 137–149 (2017).
    Google Scholar 
    Tyrrell, P., Russell, S. & Western, D. Seasonal movements of wildlife and livestock in a heterogenous pastoral landscape: Implications for coexistence and community based conservation. Glob. Ecol. Conserv. 12, 59–72 (2017).
    Google Scholar 
    Veldhuis, M. P. et al. Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Science 363, 1424–1428 (2019).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Everatt, K. T., Andresen, L. & Somers, M. J. The influence of prey, pastoralism and poaching on the hierarchical use of habitat by an apex predator. Afr. J. Wildl. Res. 45, 187–196 (2015).
    Google Scholar 
    Oriol-Cotterill, A., Macdonald, D. W., Valeix, M., Ekwanga, S. & Frank, L. G. Spatiotemporal patterns of lion space use in a human-dominated landscape. Anim. Behav. 101, 27–39 (2015).
    Google Scholar 
    Schuette, P., Creel, S. & Christianson, D. Coexistence of African lions, livestock, and people in a landscape with variable human land use and seasonal movements. Biol. Conserv. 157, 148–154 (2013).
    Google Scholar 
    Beattie, K., Olson, E. R., Kissui, B., Kirschbaum, A. & Kiffner, C. Predicting livestock depredation risk by African lions (Panthera leo) in a multi-use area of northern Tanzania. Eur. J. Wildl. Res. 66, 1–4 (2020).
    Google Scholar 
    Loveridge, A. J., Hemson, G., Davidson, Z. & Macdonald, D. W. African lions on the edge: Reserve boundaries as ‘attractive sinks’. In Biology and Conservation of Wild Felids (eds. Macdonald, D. W. & Loveridge, A. J.) 283–304 (Oxford University Press, 2010).Boyers, M., Parrini, F., Owen-Smith, N., Erasmus, B. F. N. & Hetem, R. S. How free-ranging ungulates with differing water dependencies cope with seasonal variation in temperature and aridity. Conserv. Physiol. 7, 1–12 (2019).
    Google Scholar 
    Abade, L. et al. The relative effects of prey availability, anthropogenic pressure and environmental variables on lion (Panthera leo) site use in Tanzania’s Ruaha landscape during the dry season. J. Zool. 310, 135–144 (2020).
    Google Scholar 
    Hopcraft, J. G. C., Sinclair, A. R. E. & Packer, C. Planning for success: Serengeti lions seek prey accessibility rather than abundance. J. Anim. Ecol. 74, 559–566 (2005).
    Google Scholar 
    Treves, A. & Karanth, K. U. Human-carnivore conflict and perspectives on carnivore management worldwide. Conserv. Biol. 17, 1491–1499 (2003).
    Google Scholar 
    Kisingo, A. W. Governance of Protected Areas in the Serengeti Ecosystem, Tanzania (University of Victoria, 2013).UNEP-WCMC & IUCN. Protected planet: the world database on protected areas (WDPA). www.protectedplanet.net (2020).Zella, A. Y. The management of protected areas in Serengeti ecosystem: A case study of Ikorongo and Grumeti Game Reserves (IGGRs). Int. J. Eng. Sci. 6, 22–50 (2016).
    Google Scholar 
    IUCN. Ngorongoro Conservation Area conservation outlook assessment. The IUCN World Heritage Outlook https://worldheritageoutlook.iucn.org/explore-sites/wdpaid/2010 (2020).Kittle, A. M., Bukombe, J. K., Sinclair, A. R. E., Mduma, S. A. R. & Fryxell, J. M. Landscape-level movement patterns by lions in western Serengeti: Comparing the influence of inter-specific competitors, habitat attributes and prey availability. Mov. Ecol. 4, 1–18 (2016).
    Google Scholar 
    Packer, C. et al. Ecological change, group territoriality, and population dynamics in Serengeti lions. Science 307, 390–393 (2005).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Mwampeta, S. B. et al. Lion and spotted hyena distributions within a buffer area of the Serengeti-Mara ecosystem. Sci. Rep. 11, 1–8 (2021).
    Google Scholar 
    Grumeti Fund. Protecting wildlife and human lives in the western corridor of the Serengeti. https://www.grumetifund.org/blog/updates/protecting-wildlife-and-human-lives-in-the-western-corridor-of-the-serengeti/ (2020).IUCN. Serengeti National Park conservation outlook assessment. The IUCN World Heritage Outlook https://worldheritageoutlook.iucn.org/explore-sites/wdpaid/2575 (2017).Veldhuis, M. P. et al. Data from: Cross-boundary human impacts compromise the Serengeti-Mara ecosystem. Dryad https://doi.org/10.5061/dryad.b303788 (2021).Larsen, F. et al. Wildebeest migration drives tourism demand in the Serengeti. Biol. Conserv. 248, 108688 (2020).
    Google Scholar 
    Norton-Griffiths, M., Herlocker, D. & Pennycuick, L. The patterns of rainfall in the Serengeti Ecosystem, Tanzania. Afr. J. Ecol. 13, 347–374 (1975).
    Google Scholar 
    McNaughton, S. J. Serengeti grassland ecology: The role of composite environmental factors and contingency in community organization. Ecol. Monogr. 53, 291–320 (1983).
    Google Scholar 
    Buchhorn, M. et al. Copernicus global land service: land cover 100m: version 3 globe 2015-2019. Copernicus Global Land Operations. Zenodo. https://doi.org/10.5281/zenodo.3938963.Boone, R. B., Thirgood, S. J. & Hopcraft, J. G. C. Serengeti wildebeest migratory patterns modeled from rainfall and new vegetation growth. Ecology 87, 1987–1994 (2006).PubMed 

    Google Scholar 
    Ogutu, J. O. & Dublin, H. T. The response of lions and spotted hyaenas to sound playbacks as a technique for estimating population size. Afr. J. Ecol. 36, 83–95 (1998).
    Google Scholar 
    Fyumagwa, R. D. et al. Comparison of anaesthesia and cost of two immobilization protocols in free-ranging lions. S. Afr. J. Wildl. Res. 42, 67–70 (2012).
    Google Scholar 
    Rija, A. A. Spatial Pattern of Illegal Activities and the Impact on Wildlife Populations in Protected Areas in the Serengeti Ecosystem. (University of York, 2017).Kideghesho, J. R. Wildlife Conservation and Local Land Use Conflicts in Western Serengeti Corridor, Tanzania (Norwegian University of Science and Technology, 2006).Holmern, T., Muya, J. & Røskaft, E. Local law enforcement and illegal bushmeat hunting outside the Serengeti National Park, Tanzania. Environ. Conserv. 34, 55–63 (2007).
    Google Scholar 
    Schmitt, J. A. Improving Conservation Efforts in the Serengeti Ecosystem, Tanzania: An Examination of Knowledge, Benefits, Costs, and Attitudes (University of Minnesota, 2010).Kaaya, E. & Chapman, M. Micro-credit and community wildlife management: Complementary strategies to improve conservation outcomes in Serengeti National Park, Tanzania. Environ. Manag. 60, 464–475 (2017).ADS 

    Google Scholar 
    Kideghesho, J. R., Røskaft, E. & Kaltenborn, B. P. Factors influencing conservation attitudes of local people in Western Serengeti, Tanzania. Biodivers. Conserv. 16, 2213–2230 (2007).
    Google Scholar 
    Kegamba, J. J., Sangha, K. K., Wurm, P. & Garnett, S. T. A review of conservation-related benefit-sharing mechanisms in Tanzania. Glob. Ecol. Conserv. 33, e01955 (2022).
    Google Scholar 
    Rija, A. A. & Kideghesho, J. R. Poachers’ strategies to surmount anti-poaching efforts in Western Serengeti, Tanzania. In Protected Areas in Northern Tanzania (eds. Durrant, J. O. et al.) 91–112 (Springer Nature Switzerland AG, 2020).Mfunda, I. M. & Røskaft, E. Wildlife or crop production: The dilemma of conservation and human livelihoods in Serengeti, Tanzania. Int. J. Biodivers. Sci. Ecosyst. Serv. Manag. 7, 39–49 (2011).
    Google Scholar 
    Kideghesho, J. R. Reversing the trend of wildlife crime in Tanzania: Challenges and opportunities. Biodivers. Conserv. 25, 427–449 (2016).
    Google Scholar 
    Sisya, E., Frankfurt Zoological Society & Tanzania National Parks Authority. Serengeti Park Roads. Serengeti GIS and Data Centre and ArcGIS Online. ArcGIS online https://www.arcgis.com/home/item.html?id=f8d9e2cb6ab24b92bd6d645a0d659963. (2018).Maliti, H., von Hagen, C., Frankfurt Zoological Society, Tanzania National Parks Authority & Hopcraft, J. G. C. Serengeti Park rivers. https://serengetidata.weebly.com/rivers-and-lakes.html (2008).Worldpop & Center for International Earth Science Information Network. The spatial distribution of population density in 2018, Tanzania. https://doi.org/10.5258/SOTON/WP00674 (2018).Gilbert, M. et al. Global cattle distribution in 2010 (5 minutes of arc). Harvard Dataverse, Version 3. https://doi.org/10.7910/DVN/GIVQ7 (2018).Gilbert, M. et al. [dataset] Global goat distribution in 2010 (5 minutes of arc). Harvard Dataverse, Version 3. https://doi.org/10.7910/DVN/OCPH42 (2018).Gilbert, M. et al. Global sheep distribution in 2010 (5 minutes of arc). Harvard Dataverse, Version 3. https://doi.org/10.7910/DVN/BLWPZN (2018).Gilbert, M. et al. Global distribution data for cattle, buffaloes, horses, sheep, goats, pigs, chickens and ducks in 2010. Sci. Data 5, 1–11 (2018).
    Google Scholar 
    Swihart, R. K. & Slade, N. A. Testing for independence of observations in animal movements. Ecology 66, 1176–1184 (1985).
    Google Scholar 
    Seaman, D. E. & Powell, R. A. An evaluation of the accuracy of kernel density estimators for home range analysis. Ecology 77, 2075–2085 (1996).
    Google Scholar 
    Calenge, C. The package adehabitat for the R software: Tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2006).
    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Version 4.0.4. https://www.r-project.org/ (2021).Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography (Cop.) 36, 27–46 (2013).
    Google Scholar 
    Thomas, D. L. & Taylor, E. J. Study designs and tests for comparing resource use and availability II. J. Wildl. Manag. 70, 324–336 (2006).
    Google Scholar 
    Swets, J. A. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 

    Google Scholar 
    Sommer, S. & Huggins, R. M. Variables selection using the Wald test and a robust CP. J. R. Stat. Soc. 45, 15–29 (1996).MATH 

    Google Scholar 
    Burnham, K. P. & Anderson, D. D. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).Lenth, R. emmeans: Estimated Marginal Means, aka Least-Squares Means. (2020).Ogutu, J. O. & Dublin, H. T. Demography of lions in relation to prey and habitat in the Maasai Mara National Reserve, Kenya. Afr. J. Ecol. 40, 120–129 (2002).
    Google Scholar 
    Henschel, P. et al. Determinants of distribution patterns and management needs in a critically endangered lion (Panthera leo) population. Front. Ecol. Evol. 4, 1–14 (2016).
    Google Scholar 
    Melubo, K. & Lovelock, B. Living inside a UNESCO World Heritage Site: The perspective of the Maasai community in Tanzania. Tour. Plan. Dev. 16, 197–216 (2019).
    Google Scholar 
    Makupa, E. E. Conservation Efforts and Local Livelihoods in Western Serengeti, Tanzania: Experiences from Ikona Community Wildlife Management Area (University of Victoria, 2013).Ndibalema, V. G. & Songorwa, A. N. Illegal meat hunting in serengeti: Dynamics in consumption and preferences. Afr. J. Ecol. 46, 311–319 (2008).
    Google Scholar 
    Geldmann, J., Joppa, L. N. & Burgess, N. D. Mapping change in human pressure globally on land and within protected areas. Conserv. Biol. 28, 1604–1616 (2014).PubMed 

    Google Scholar 
    Tuqa, J. H. et al. Impact of severe climate variability on lion home range and movement patterns in the Amboseli ecosystem, Kenya. Glob. Ecol. Conserv. 2, 1–10 (2014).
    Google Scholar 
    Blackburn, S., Hopcraft, J. G. C., Ogutu, J. O., Matthiopoulos, J. & Frank, L. Human–wildlife conflict, benefit sharing and the survival of lions in pastoralist community-based conservancies. J. Appl. Ecol. 53, 1195–1205 (2016).
    Google Scholar 
    Thirgood, S. et al. Can parks protect migratory ungulates? The case of the Serengeti wildebeest. Anim. Conserv. 7, 113–120 (2004).
    Google Scholar 
    Wittemyer, G., Elsen, P., Bean, W. T., Burton, A. C. O. & Brashares, J. S. Accelerated human population growth at protected area edges. Science 321, 123–126 (2008).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hayward, M. W. & Kerley, G. I. H. Prey preferences and dietary overlap amongst Africa’s large predators. S. Afr. J. Wildl. Res. 38, 93–108 (2008).
    Google Scholar 
    Mkonyi, F. J., Estes, A. B., Lichtenfeld, L. L. & Durant, S. M. Large carnivore distribution in relationship to environmental and anthropogenic factors in a multiple-use landscape of northern Tanzania. Afr. J. Ecol. 56, 972–983 (2018).
    Google Scholar 
    Hill, J. E., De Vault, T. L. & Belant, J. L. A review of ecological factors promoting road use by mammals. Mamm. Rev. 51, 214–227 (2021).
    Google Scholar 
    Hägerling, H. G. & Ebersole, J. J. Roads as travel corridors for mammals and ground birds in Tarangire National Park, Tanzania. Afr. J. Ecol. 55, 701–704 (2017).
    Google Scholar 
    Bateman, P. W. & Fleming, P. A. Are negative effects of tourist activities on wildlife over-reported? A review of assessment methods and empirical results. Biol. Conserv. 211, 10–19 (2017).
    Google Scholar 
    de Boer, W. F. et al. Spatial distribution of lion kills determined by the water dependency of prey species. J. Mammal. 91, 1280–1286 (2010).
    Google Scholar 
    Loveridge, A. J., Valeix, M., Elliot, N. B. & Macdonald, D. W. The landscape of anthropogenic mortality: How African lions respond to spatial variation in risk. J. Appl. Ecol. 54, 815–825 (2017).
    Google Scholar 
    Suraci, J. P. et al. Behavior-specific habitat selection by African lions may promote their persistence in a human-dominated landscape. Ecology 100, 1–11 (2019).
    Google Scholar 
    Snyman, A., Raynor, E., Chizinski, C., Powell, L. & Carroll, J. African lion (Panthera leo) space use in the Greater Mapungubwe Transfrontier Conservation Area. Afr. J. Wildl. Res. 48, 023001 (2018).
    Google Scholar 
    Mwakaje, A. G. et al. Community-based conservation, income governance, and poverty alleviation in Tanzania: The case of the Serengeti Ecosystem. J. Environ. Dev. 22, 51–73 (2013).
    Google Scholar 
    Everatt, K. T., Moore, J. F. & Kerley, G. I. H. Africa’s apex predator, the lion, is limited by interference and exploitative competition with humans. Glob. Ecol. Conserv. 20, e00758 (2019).
    Google Scholar  More

  • in

    Dryland productivity under a changing climate

    Schimel, D. S. Drylands in the Earth system. Science 327, 418–419 (2010).Article 
    CAS 

    Google Scholar 
    Whitford, W. G. Ecology of Desert Systems (Academic Press, 2002).D’Odorico, P., Porporato, A. & Runyan, C. W. Dryland Ecohydrology Vol. 9 (Springer, 2019). A comprehensive introduction to dryland ecohydrology.Lal, R. Carbon cycling in global drylands. Curr. Clim. Change Rep. 5, 221–232 (2019).Article 

    Google Scholar 
    Ahlström, A. et al. The dominant role of semi-arid ecosystems in the trend and variability of the land CO2 sink. Science 348, 895–899 (2015). Illustrates the role drylands play in determining the variability and long-term trend of the terrestrial CO2 sink.Article 

    Google Scholar 
    Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014). Illustrates the role drylands play in determining the variability of the terrestrial CO2 sink.Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Ecol. Evol. Syst. 47, 215–237 (2016). A comprehensive review of dryland structure and functioning.Article 

    Google Scholar 
    Wang, L., Kaseke, K. F. & Seely, M. K. Effects of non-rainfall water inputs on ecosystem functions. WIREs Water 4, e1179 (2017). Highlights the often-ignored role of non-rainfall water inputs to dryland ecosystem dynamics.Article 

    Google Scholar 
    Li, C. et al. Drivers and impacts of changes in China’s drylands. Nat. Rev. Earth Environ. 2, 858–873 (2021).Article 

    Google Scholar 
    Thornton, P. K., Ericksen, P. J., Herrero, M. & Challinor, A. J. Climate variability and vulnerability to climate change: a review. Glob. Change Biol. 20, 3313–3328 (2014).Article 

    Google Scholar 
    IPCC Climate Change 2022: Impacts, Adaptation, and Vulnerability (eds Pörtner, H.-O. et al.) (Cambridge Univ. Press, 2022).Gonsamo, A. et al. Greening drylands despite warming consistent with carbon dioxide fertilization effect. Glob. Change Biol. 27, 3336–3349 (2021).Article 

    Google Scholar 
    Kaptué, A. T., Prihodko, L. & Hanan, N. P. On regreening and degradation in Sahelian watersheds. Proc. Natl Acad. Sci. USA 112, 12133–12138 (2015).Article 

    Google Scholar 
    Brookshire, E. J., Stoy, P. C., Currey, B. & Finney, B. The greening of the Northern Great Plains and its biogeochemical precursors. Glob. Change Biol. 26, 5404–5413 (2020).Article 

    Google Scholar 
    Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).Article 
    CAS 

    Google Scholar 
    Ravi, S. et al. Biological invasions and climate change amplify each other’s effects on dryland degradation. Glob. Change Biol. 28, 285–295 (2022).Article 
    CAS 

    Google Scholar 
    Allen, C. D., Breshears, D. D. & McDowell, N. G. On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere https://doi.org/10.1890/ES15-00203.1 (2015).Yu, K. et al. The competitive advantage of a constitutive CAM species over a C4 grass species under drought and CO2 enrichment. Ecosphere 10, e02721 (2019).Article 

    Google Scholar 
    Fensholt, R. et al. in Remote Sensing Time Series (eds Kuenzer, C. et al.) 183–292 (Springer, 2015).Andela, N., Liu, Y., Van Dijk, A., De Jeu, R. & McVicar, T. Global changes in dryland vegetation dynamics (1988-2008) assessed by satellite remote sensing: comparing a new passive microwave vegetation density record with reflective greenness data. Biogeosciences 10, 6657–6676 (2013).Article 

    Google Scholar 
    Lu, X., Wang, L. & McCabe, M. F. Elevated CO2 as a driver of global dryland greening. Sci. Rep. 6, 20716 (2016).Article 
    CAS 

    Google Scholar 
    Venter, Z., Cramer, M. & Hawkins, H.-J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).Article 
    CAS 

    Google Scholar 
    Ukkola, A. M. et al. Annual precipitation explains variability in dryland vegetation greenness globally but not locally. Glob. Change Biol. 27, 4367–4380 (2021).Article 
    CAS 

    Google Scholar 
    Zhang, W., Brandt, M., Tong, X., Tian, Q. & Fensholt, R. Impacts of the seasonal distribution of rainfall on vegetation productivity across the Sahel. Biogeosciences 15, 319–330 (2018).Article 

    Google Scholar 
    Fensholt, R. & Rasmussen, K. Analysis of trends in the Sahelian ‘rain-use efficiency’ using GIMMS NDVI, RFE and GPCP rainfall data. Remote Sens. Environ. 115, 438–451 (2011).Article 

    Google Scholar 
    Zhang, W. et al. Ecosystem structural changes controlled by altered rainfall climatology in tropical savannas. Nat. Commun. 10, 671 (2019).Article 
    CAS 

    Google Scholar 
    Brandt, M. et al. Reduction of tree cover in West African woodlands and promotion in semi-arid farmlands. Nat. Geosci. 11, 328–333 (2018).Article 
    CAS 

    Google Scholar 
    Hufkens, K. et al. Productivity of North American grasslands is increased under future climate scenarios despite rising aridity. Nat. Clim. Change 6, 710–714 (2016).Article 

    Google Scholar 
    Choler, P., Sea, W., Briggs, P., Raupach, M. & Leuning, R. A simple ecohydrological model captures essentials of seasonal leaf dynamics in semi-arid tropical grasslands. Biogeosciences 7, 907–920 (2010).Article 

    Google Scholar 
    Huang, J., Yu, H., Dai, A., Wei, Y. & Kang, L. Drylands face potential threat under 2 °C global warming target. Nat. Clim. Change 7, 417–422 (2017).Article 

    Google Scholar 
    Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).Article 

    Google Scholar 
    Lian, X. et al. Multifaceted characteristics of dryland aridity changes in a warming world. Nat. Rev. Earth Environ. 2, 232–250 (2021). Provides a comprehensive analysis on the dryland expansion debates.Article 

    Google Scholar 
    Fatichi, S. et al. Partitioning direct and indirect effects reveals the response of water-limited ecosystems to elevated CO2. Proc. Natl Acad. Sci. USA 113, 12757–12762 (2016).Article 
    CAS 

    Google Scholar 
    Daramola, M. T. & Xu, M. Recent changes in global dryland temperature and precipitation. Int. J. Climatol. 42, 1267–1282 (2022).Article 

    Google Scholar 
    Berg, A. & McColl, K. A. No projected global drylands expansion under greenhouse warming. Nat. Clim. Change 11, 331–337 (2021).Article 

    Google Scholar 
    Berg, A. & Sheffield, J. Climate change and drought: the soil moisture perspective. Curr. Clim. Change Rep. 4, 180–191 (2018).Article 

    Google Scholar 
    Jiao, W. et al. Observed increasing water constraint on vegetation growth over the last three decades. Nat. Commun. 12, 3777 (2021). This study found that vegetation growth in the Northern Hemisphere is becoming increasingly water limited.Article 
    CAS 

    Google Scholar 
    Gherardi, L. A. & Sala, O. E. Effect of interannual precipitation variability on dryland productivity: a global synthesis. Glob. Change Biol. 25, 269–276 (2019).Article 

    Google Scholar 
    D’Odorico, P. & Bhattachan, A. Hydrologic variability in dryland regions: impacts on ecosystem dynamics and food security. Phil. Trans. R. Soc. B 367, 3145–3157 (2012).Article 

    Google Scholar 
    Hou, E. et al. Divergent responses of primary production to increasing precipitation variability in global drylands. Glob. Change Biol. 27, 5225–5237 (2021).Article 
    CAS 

    Google Scholar 
    Ritter, F., Berkelhammer, M. & Garcia-Eidell, C. Distinct response of gross primary productivity in five terrestrial biomes to precipitation variability. Commun. Earth Environ. 1, 34 (2020).Article 

    Google Scholar 
    Ridolfi, L., D’Odorico, P. & Laio, F. Noise-Induced Phenomena in the Environmental Sciences (Cambridge Univ. Press, 2011).Zeng, N. & Neelin, J. D. The role of vegetation–climate interaction and interannual variability in shaping the African savanna. J. Clim. 13, 2665–2670 (2000).Article 

    Google Scholar 
    Borgogno, F., D’Odorico, P., Laio, F. & Ridolfi, L. Mathematical models of vegetation pattern formation in ecohydrology. Rev. Geophysics 47, RG1005 (2009).Article 

    Google Scholar 
    van de Koppel, J. & Rietkerk, M. Spatial interactions and resilience in arid ecosystems. Am. Nat. 163, 113–121 (2004).Article 

    Google Scholar 
    Lefever, R. & Lejeune, O. On the origin of tiger bush. Bull. Math. Biol. 59, 263–294 (1997).Article 

    Google Scholar 
    Gherardi, L. A. & Sala, O. E. Enhanced precipitation variability decreases grass- and increases shrub-productivity. Proc. Natl Acad. Sci. USA 112, 12735–12740 (2015). Highlights the role of precipitation varibility in plant community composition in drylands.Article 
    CAS 

    Google Scholar 
    Cleland, E. E. et al. Sensitivity of grassland plant community composition to spatial vs. temporal variation in precipitation. Ecology 94, 1687–1696 (2013).Article 

    Google Scholar 
    Good, S. P. & Caylor, K. K. Climatological determinants of woody cover in Africa. Proc. Natl Acad. Sci. USA 108, 4902–4907 (2011).Article 
    CAS 

    Google Scholar 
    Lu, X., Wang, L., Pan, M., Kaseke, K. F. & Li, B. A multi-scale analysis of Namibian rainfall over the recent decade—comparing TMPA satellite estimates and ground observations. J. Hydrol. Reg. Stud. 8, 59–68 (2016).Article 

    Google Scholar 
    Franz, T., Caylor, K., Nordbotten, J., Rodriguez-Itubre, I. & Celia, M. An ecohydrological approach to predicting regional woody species distribution patterns in dryland ecosystems. Adv. Water Res. 33, 215–230 (2010).Article 

    Google Scholar 
    Knapp, A. K., Chen, A., Griffin-Nolan, R. J., Baur, L. E. & Smith, M. Resolving the Dust Bowl paradox of grassland responses to extreme drought. Proc. Natl Acad. Sci. USA 117, 201922030 (2020).Article 

    Google Scholar 
    Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nat. Clim. Change 6, 75–78 (2016).Article 

    Google Scholar 
    Austin, A. T. et al. Water pulses and biogeochemical cycles in arid and semiarid ecosystems. Oecologia 141, 221–235 (2004). Illustrates the close linkage between water pulses and biogeochemical cycles in drylands.Article 

    Google Scholar 
    Schwinning, S. & Sala, O. E. Hierarchy of responses to resource pulses in arid and semi-arid ecosystems. Oecologia 141, 211–220 (2004).Article 

    Google Scholar 
    Collins, S. L. et al. A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems. Annu. Rev. Ecol. Evol. Syst. 45, 397–419 (2014).Article 

    Google Scholar 
    Barnard, R. L., Blazewicz, S. J. & Firestone, M. K. Rewetting of soil: revisiting the origin of soil CO2 emissions. Soil Biol. Biochem. 147, 107819 (2020).Article 
    CAS 

    Google Scholar 
    Manzoni, S. et al. Rainfall intensification increases the contribution of rewetting pulses to soil heterotrophic respiration. Biogeosciences 17, 4007–4023 (2020).Article 
    CAS 

    Google Scholar 
    Leizeaga, A., Meisner, A., Rousk, J. & Bååth, E. Repeated drying and rewetting cycles accelerate bacterial growth recovery after rewetting. Biol. Fertil. Soils 58, 365–374 (2022).Article 
    CAS 

    Google Scholar 
    Gao, D. et al. Responses of soil nitrogen and phosphorus cycling to drying and rewetting cycles: a meta-analysis. Soil Biol. Biochem. 148, 107896 (2020).Article 
    CAS 

    Google Scholar 
    Homyak, P. M., Allison, S. D., Huxman, T. E., Goulden, M. L. & Treseder, K. K. Effects of drought manipulation on soil nitrogen cycling: a meta-analysis. J. Geophys. Res. Biogeosci. 122, 3260–3272 (2017).Article 
    CAS 

    Google Scholar 
    Delgado-Baquerizo, M. et al. Decoupling of soil nutrient cycles as a function of aridity in global drylands. Nature 502, 672–676 (2013).Article 
    CAS 

    Google Scholar 
    Nippert, J. B., Knapp, A. K. & Briggs, J. M. Intra-annual rainfall variability and grassland productivity: can the past predict the future? Plant Ecol. 184, 65–74 (2006).Article 

    Google Scholar 
    Kaseke, K. F., Wang, L. & Seely, M. K. Nonrainfall water origins and formation mechanisms. Sci. Adv. 3, e1603131 (2017).Article 

    Google Scholar 
    Dawson, T. E. & Goldsmith, G. R. The value of wet leaves. N. Phytol. 219, 1156–1169 (2018).Article 

    Google Scholar 
    Feng, T. et al. Dew formation reduction in global warming experiments and the potential consequences. J. Hydrol. 593, 125819 (2021).Article 

    Google Scholar 
    Gerlein-Safdi, C. et al. Dew deposition suppresses transpiration and carbon uptake in leaves. Agric. For. Meteorol. 259, 305–316 (2018).Article 

    Google Scholar 
    Tomaszkiewicz, M., Abou Najm, M., Beysens, D., Alameddine, I. & El-Fadel, M. Dew as a sustainable non-conventional water resource: a critical review. Environ. Rev. 23, 425–442 (2015).Article 

    Google Scholar 
    Fessehaye, M. et al. Fog-water collection for community use. Renew. Sustain. Energy Rev. 29, 52–62 (2014).Article 

    Google Scholar 
    Kidron, G. J. Angle and aspect dependent dew and fog precipitation in the Negev desert. J. Hydrol. 301, 66–74 (2005).Article 

    Google Scholar 
    Chiodi, A. M., Potter, B. E. & Larkin, N. K. Multi-decadal change in western US nighttime vapor pressure deficit. Geophys. Res. Lett. 48, e2021GL092830 (2021).Article 

    Google Scholar 
    Tomaszkiewicz, M. et al. Projected climate change impacts upon dew yield in the Mediterranean basin. Sci. Total Environ. 566, 1339–1348 (2016).Article 

    Google Scholar 
    Walker, B. H., Ludwig, D., Holling, C. S. & Peterman, R. N. Stability of semi-arid savanna grazing systems. J. Ecol. 69, 473–498 (1981).Article 

    Google Scholar 
    Schlesinger, W. H. et al. Biological feedbacks in global desertification. Science 247, 1043–1048 (1990).Article 
    CAS 

    Google Scholar 
    D’Odorico, P., Bhattachan, A., Davis, K., Ravi, S. & Runyan, C. Global desertification: drivers and feedbacks. Adv. Water Res. 51, 326–344 (2013).Article 

    Google Scholar 
    Reynolds, J. F. et al. Global desertification: building a science for dryland development. Science 316, 847–851 (2007). Highlights the loss of ecosystem services as a result of dryland desertification.Article 
    CAS 

    Google Scholar 
    Eldridge, D. J. et al. Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis. Ecol. Lett. 14, 709–722 (2011). Provides a compehenseive analysis of the shrub enrochment effects on dryland functions.Article 

    Google Scholar 
    IPCC Special Report on Climate Change and Land (eds Shukla, P. R. et al.) (IPCC, 2019).Yang, H. et al. Tropical expansion driven by poleward advancing midlatitude meridional temperature gradients. J. Geophys. Res. Atmos. 125, e2020JD033158 (2020).Article 

    Google Scholar 
    Berghuijs, W. R., Woods, R. A. & Hrachowitz, M. A precipitation shift from snow towards rain leads to a decrease in streamflow. Nat. Clim. Change 4, 583–586 (2014).Article 

    Google Scholar 
    Ayyad, M. A., Fakhry, A. M. & Moustafa, A.-R. A. Plant biodiversity in the Saint Catherine area of the Sinai peninsula. Egypt. Biodivers. Conserv. 9, 265–281 (2000).Article 

    Google Scholar 
    Global Land Outlook 2017 (UNCCD, 2017).Van Ittersum, M. K. et al. Can sub-Saharan Africa feed itself? Proc. Natl Acad. Sci. USA 113, 14964–14969 (2016).Article 

    Google Scholar 
    Redo, D., Aide, T. M. & Clark, M. L. Vegetation change in Brazil’s dryland ecoregions and the relationship to crop production and environmental factors: Cerrado, Caatinga, and Mato Grosso, 2001–2009. J. Land Use Sci. 8, 123–153 (2013).Article 

    Google Scholar 
    Meyfroidt, P., Lambin, E. F., Erb, K.-H. & Hertel, T. W. Globalization of land use: distant drivers of land change and geographic displacement of land use. Curr. Opin. Environ. Sustain. 5, 438–444 (2013).Article 

    Google Scholar 
    Rulli, M. C., Saviori, A. & D’Odorico, P. Global land and water grabbing. Proc. Natl Acad. Sci. USA 110, 892–897 (2013).Article 
    CAS 

    Google Scholar 
    Müller, M. F. et al. Impact of transnational land acquisitions on local food security and dietary diversity. Proc. Natl Acad. Sci. USA 118, e2020535118 (2021).Article 

    Google Scholar 
    Chiarelli, D. D. et al. Competition for water induced by transnational land acquisitions for agriculture. Nat. Commun. 13, 505 (2022).Article 
    CAS 

    Google Scholar 
    Dell’Angelo, J., D’Odorico, P., Rulli, M. C. & Marchand, P. The tragedy of the grabbed commons: coercion and dispossession in the global land rush. World Dev. 92, 1–12 (2017).Article 

    Google Scholar 
    Rosa, L. et al. Potential for sustainable irrigation expansion in a 3 °C warmer climate. Proc. Natl Acad. Sci. USA 117, 29526–29534 (2020).Article 
    CAS 

    Google Scholar 
    Wang, L. & D’Odorico, P. The limits of water pumps. Science 321, 36–37 (2008).Article 
    CAS 

    Google Scholar 
    OECD-FAO Agricultural Outlook 2021–2030 (OECD and FAO, 2021).Qi, J., Xin, X., John, R., Groisman, P. & Chen, J. Understanding livestock production and sustainability of grassland ecosystems in the Asian Dryland Belt. Ecol. Process. 6, 22 (2017).Article 

    Google Scholar 
    Godde, C. M. et al. Global rangeland production systems and livelihoods at threat under climate change and variability. Environ. Res. Lett. 15, 044021 (2020).Article 

    Google Scholar 
    Herrero, M. et al. Exploring future changes in smallholder farming systems by linking socio-economic scenarios with regional and household models. Glob. Environ. Change 24, 165–182 (2014).Article 

    Google Scholar 
    Bannari, A., Morin, D., Bonn, F. & Huete, A. A review of vegetation indices. Remote Sens. Rev. 13, 95–120 (1995).Article 

    Google Scholar 
    Qiu, B. et al. Dense canopies browning overshadowed by global greening dominant in sparse canopies. Sci. Total Environ. 826, 154222 (2022).Article 
    CAS 

    Google Scholar 
    Burrell, A. L., Evans, J. P. & Liu, Y. Detecting dryland degradation using time series segmentation and residual trend analysis (TSS-RESTREND). Remote Sens. Environ. 197, 43–57 (2017).Article 

    Google Scholar 
    Bastin, J.-F. et al. The extent of forest in dryland biomes. Science 356, 635–638 (2017).Article 
    CAS 

    Google Scholar 
    Griffith, D. M. et al. Comment on ‘The extent of forest in dryland biomes’. Science 358, eaao1309 (2017).Article 

    Google Scholar 
    Teckentrup, L. et al. Assessing the representation of the Australian carbon cycle in global vegetation models. Biogeosciences 18, 5639–5668 (2021).Article 
    CAS 

    Google Scholar 
    MacBean, N. et al. Dynamic global vegetation models underestimate net CO2 flux mean and inter-annual variability in dryland ecosystems. Environ. Res. Lett. 16, 094023 (2021). Highlights the often-neglected uncertainties in the prediction of dryland productivity.Paschalis, A. et al. Rainfall manipulation experiments as simulated by terrestrial biosphere models: where do we stand? Glob. Change Biol. 26, 3336–3355 (2020).Article 

    Google Scholar 
    Whitley, R. et al. A model inter-comparison study to examine limiting factors in modelling Australian tropical savannas. Biogeosciences 13, 3245–3265 (2016).Article 

    Google Scholar 
    Hartley, A. J., MacBean, N., Georgievski, G. & Bontemps, S. Uncertainty in plant functional type distributions and its impact on land surface models. Remote Sens. Environ. 203, 71–89 (2017).Article 

    Google Scholar 
    MacBean, N. et al. Testing water fluxes and storage from two hydrology configurations within the ORCHIDEE land surface model across US semi-arid sites. Hydrol. Earth Syst. Sci. 24, 5203–5230 (2020).Article 
    CAS 

    Google Scholar 
    Burrell, A., Evans, J., De & Kauwe, M. Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification. Nat. Commun. 11, 3853 (2020).Article 
    CAS 

    Google Scholar 
    De Kauwe, M. G., Medlyn, B. E. & Tissue, D. T. To what extent can rising [CO2] ameliorate plant drought stress? N. Phytol. 231, 2118–2124 (2021).Article 

    Google Scholar 
    Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).Article 
    CAS 

    Google Scholar 
    Bernacchi, C. J. & VanLoocke, A. Terrestrial ecosystems in a changing environment: a dominant role for water. Annu. Rev. Plant Biol. 66, 599–622 (2015).Article 
    CAS 

    Google Scholar 
    Roderick, M. L., Greve, P. & Farquhar, G. D. On the assessment of aridity with changes in atmospheric CO2. Water Resour. Res. 51, 5450–5463 (2015).Article 
    CAS 

    Google Scholar 
    Anderegg, W. R., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).Article 
    CAS 

    Google Scholar 
    Zhou, S. et al. Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity. Proc. Natl Acad. Sci. USA 116, 18848–18853 (2019).Article 
    CAS 

    Google Scholar 
    Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Change 3, 52–58 (2013).Article 

    Google Scholar 
    Abdelmoaty, H. M., Papalexiou, S. M., Rajulapati, C. R. & AghaKouchak, A. Biases beyond the mean in CMIP6 extreme precipitation: a global investigation. Earth’s Future 9, e2021EF002196 (2021).Article 

    Google Scholar 
    Dunkerley, D. L. Light and low-intensity rainfalls: a review of their classification, occurrence, and importance in landsurface, ecological and environmental processes. Earth Sci. Rev. 214, 103529 (2021).Article 

    Google Scholar 
    Zhu, Y. & Yang, S. Interdecadal and interannual evolution characteristics of the global surface precipitation anomaly shown by CMIP5 and CMIP6 models. Int. J. Climatol. 41, E1100–E1118 (2021).Article 

    Google Scholar 
    Cuthbert, M. O. et al. Observed controls on resilience of groundwater to climate variability in sub-Saharan Africa. Nature 572, 230–234 (2019).Article 
    CAS 

    Google Scholar 
    Miguez-Macho, G. & Fan, Y. Spatiotemporal origin of soil water taken up by vegetation. Nature 598, 624–628 (2021).Article 

    Google Scholar 
    Potapov, P. et al. Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century. Nat. Food 3, 19–28 (2022).Article 

    Google Scholar 
    Trabucco, A. & Zomer, R. Global aridity index and potential evapotranspiration (ET0) climate database v.2. Figshare https://doi.org/10.6084/m9.figshare.7504448.v4 (2019).Paschalis, A., Fatichi, S., Katul, G. G. & Ivanov, V. Y. Cross-scale impact of climate temporal variability on ecosystem water and carbon fluxes. J. Geophys. Res. Biogeosci. 120, 1716–1740 (2015).Article 

    Google Scholar  More

  • in

    A large-scale dataset reveals taxonomic and functional specificities of wild bee communities in urban habitats of Western Europe

    Here we assessed how species and functional diversity components of wild bee assemblages responded to increasing urbanization levels, using a large dataset encompassing recent surveys gathering 838 sampling sites located in natural, semi-natural and urban habitats of France, Belgium and Switzerland.We found a weak, but significant negative effect of the proportion of impervious surfaces in a 500 m radius around each site on local species richness of bee communities. Thus, sites with high soil sealing tended to host less species than those with low soil sealing. However, this trend was not observed when using human population density as an urbanization metric: sites with denser human populations hosted on average the same number of species as less densely populated sites.Concerning taxonomic homogenization of communities, we did not record any effects of urbanization, both in terms of impervious surfaces or human population density.Analyses of occurrence rates of bee functional traits revealed significant differences between poorly and highly urbanized communities, for both urbanization metrics. With higher human population density, probabilities of occurrence of above-ground nesters, generalist and small species increased, and a higher probability of occurrence of above-ground nesters, generalists and social bees were recorded in areas with high soil sealing.Therefore, we found overall consistent results linking urbanization and wild bees taxonomic as well as functional trait diversity, even though analyses stemmed from a combination of many independent studies covering a broad range of anthropized and natural aeras from western Europe. This further highlights the greater generalizability of those ecological trends throughout European temperate biomes compared to other studies typically focusing on a single city and its immediate vicinity.Two complementary metrics of urbanization intensityTo quantify urbanization, we used two variables: soil sealing12,16,19,36 in a 500 m radius, and the mean human population density, also in a 500 m radius, the latter variable being used only recently to assess pollinator responses to urban environments37,38. These two variables return different but complementary information concerning urban environments. Indeed, if soil sealing gives an idea as to how human activities impact land use, human population density helps distinguish between very dense urban areas and very impervious areas with lower densities of buildings. High human population density areas are usually associated with high levels of soil sealing, but the contrary is not true. Similarly, areas with low soil sealing are usually associated with low human population densities, but again, the opposite is not always true. Therefore, we found it informative to consider both variables when analyzing the response of wild bee assemblages to urbanization.Note that some specific habitat types, for example business districts, are exceptions to the rule. These places are indeed very densely urbanized, but with very low population density. However, no inventories have been carried out in these places, and thus will not be a problem for our study.Response of bee community species richness to urbanizationOne of our goals was to position this study in the context of the contrasting findings on pollinator communities and urbanization. Whereas no consistent trend is reported in literature15, our large dataset reveals that high soil sealing is detrimental to wild bee species richness. This offers a unified view of a trend that has been unequally evidenced from studies focusing on a single or few cities only. High proportions of soil sealing reduce the availability of nesting sites for ground-nesting bee species. This may in turn lower the species diversity of local assemblages, by filtering out ground-nesting bees, leaving mainly cavity-nesting bees. Furthermore, high levels of soil sealing can lead to depletion of floral resources, of extreme importance for bees, especially in highly disturbed environments such as cities39,40. Note that several previous studies report the opposite, with high local species richness of wild bees in urbanized habitats. However, these positive effects are often associated with intermediate levels of urbanization15,16, where private gardens and other green spaces may supply abundant floral resources, in conjunction with intermediate levels of soil sealing16,17,18,19,20,24.On the contrary, there was no significant relationship between local species richness and human population density. Recently, two recent studies have used this metric to analyze how urbanization impacts local diversity of bee, hoverfly37 or butterfly38 assemblages, and both studies report negative impacts of human population density. However, high levels of human population density do not necessarily correlate with low availability of floral resources or nesting sites for pollinating insects. Several studies show that densely-populated urban environments may be adequate habitats for pollinating insects, due to alternative management practices of urban green space41 and the year-round availability of ornamental flowers42,43. Here, the absence of a clear effect of human population density on local bee species richness masks a change in the species composition of the communities, as shown by the increasing proportion of cavity nesters, compared with ground nesters. Indeed, despite the lower availability of nesting resources for ground-nesters, cavity-nesters take over in high-density areas, where more concrete structures and buildings are present15, thus they may compensate for the loss of ground-nesting bee species.Wild bee community homogenization and urbanizationWe did not observe any relationship between mean pairwise β-diversity and the two metrics of urbanization. This result contrasts with those of Banaszak-Cibicka and Żmihorski (2020)44 who found more homogeneous wild bee communities in urban environments compared to non-urban ones. Similar results have been reported for bees, with homogenization of urban pollinator communities compared to rural ones28,45. Biotic homogenization in urban environments has also been reported for other taxa, for example birds46.In our study, when considering urbanization levels, either in terms of soil sealing or human population density, urban wild bee communities are not more or less taxonomically homogeneous than non-urban ones. It is important to note that this result does not imply that urban and non-urban wild bee communities are similar, but that the homogenization of wild bee communities is constant throughout the urbanization gradient. In other words, urban communities are as dissimilar as non-urban ones. Here, the β diversity values are quite high (ranging from 0.68 to 0.96), emphasizing that even urban areas have quite dissimilar communities when compared to each other. This high level of dissimilarity among wild bee communities in urban environments can be explained by the large range of biogeographical regions encompassed in our dataset (Fig. 5), as each of these regions harbors a specific wild bee fauna34.Local factors in cities might also explain these high levels of dissimilarity. We know for example that green space connectivity has effects on species richness, with more wild bee species and abundance in cities with more connected green spaces47. Another local explanation might come from contrasting green space management practices among cities. Not all cities have the same policies, and urban green space management is crucial to the establishment and sustainability of diverse pollinator communities14,15,48. Thus, we expect more dissimilar wild bee communities among cities with differing green space layout and management.Figure 5Grouped sampling sites (n = 532) in France, Belgium and Switzerland, with the biogeographical regions. In total, 238 sites belong to the Continental region, 178 to the Atlantic, 106 to de Mediterranean and 10 to the Alpine. This figure was generated using QGIS software, v3.10.13 (https://www.qgis.org/).Full size imageFunctional responses of bee communities to urbanizationSeveral studies have already shown trends on how urban areas filter wild bee communities based on their functional traits (see30 and49 for reviews). However, as for taxonomic diversity, it is often difficult to identify clear variation patterns50. Using our large dataset, we could identify typical wild bee functional traits that are favored in urban environments, thus informing on the average functional profiles of wild bee species that may thrive in cities. We found urban wild bees in general to be typically above-ground nesters and generalists, while different trends were established for their body size and sociality, depending on the considered urbanization metric (Fig. 6).Figure 6Summary picture of an urban bee community, compared to a non-urban one. This figure was generated using Inkscape v1.2 (https://inkscape.org/).Full size imageNesting habitsAbove-ground nesting species were more frequent with increasing urbanization than below-ground nesting ones, and this result was recorded with both urbanization metrics.This result is consistent with what was previously reported in the literature16,49,51,52. Indeed, cities, with high proportions of impervious surfaces and buildings, offer fewer nesting habitats to ground-nesting species15, nesting sites becoming a limiting factor39. On the other hand, above-ground nesters can do well in cities with the presence of man-made structures, depending on their ability to use them and on their availability53.The presence of green areas in cities can help ground-nesting bee species by offering more nesting opportunities and resources17. Several studies highlight the importance of parks and gardens in supporting bee biodiversity in cities12,18,31,54, which otherwise are constraining environments due to soil sealing.DietGeneralist species were more frequent in more urbanized sites than specialist ones, and this was recorded for both urbanization metrics.This is in accordance with what was previously found in the literature32,50,51,52,54,55, as specialist bee species depend on the presence of their host plants to complete their life-cycle, which are often scarce due to the rarefaction of native flowering resources. As one can find many exotic flowers in cities, especially in residential gardens and urban parks56, we expect to detect less oligolectic bee species in densely urbanized habitats57.Notwithstanding, Banaszak-Cibicka et al. (2018)20 found more oligolectic species in urban parks of Poznań (Poland) compared to a national park. Thus, urban areas are not always depleted of specialist species, and well-managed parks with preserved native floral resources can obviously support specialist wild bee species in cities58.Additionally, it is important to emphasize that the presence of an exotic plant species may concomitantly support an associated specialist bee species. In Poland, for instance, the spread of Bryonia dioica in urban environments also brought the Andrena florea wild bee species, specialized on this plant59.Body sizeWe recorded contrasting effects of the two urbanization metrics on wild bee body size: small species were more frequent in relation to higher human population density compared to large species, but we found no difference with the proportion of impervious surfaces. Contrasting impacts of urbanization on bee body size are also reported in the literature, with some studies finding little to no effect32,50, and some finding that urbanization often favors smaller bee species12,30,60. Bee body size is of particular importance because it is related to the foraging range of individuals61,62. In fragmented habitats, such as dense urban environments, distances between suitable nesting and feeding habitats may select for smaller species that can remain on small green spaces and rarely need to commute across several green spaces. Furthermore, small bees may be favored given that they need fewer floral resources than large bees, even though large bees can fly further62.This might also explain the difference in the response of bee body size to the two urbanization metric results. In densely populated cities, it is harder to fly between suitable habitats, even for larger bees, as higher buildings and structures may act as barriers to their movement. Indeed, it has been recently shown that the 3D structure of cities impacts wild bee community composition63. Thus, being able to fly further might no longer be an advantage, and larger bees, requiring more floral resources than smaller ones, might be selected against. On the contrary, very impervious areas do not always host high building density (for example, as in the case of parking lots), thus making it easier for large wild bees to fly between bare soil areas.Densely populated areas might also exhibit warmer temperatures due to the urban heat island effect, and this could, in turn, result in the selection of smaller individuals, as we know that in cities, higher temperature results in smaller body sizes64.SocialityWe also recorded contrasting effects of the two urbanization metrics on sociality: social species were more frequent in relation to higher proportion of impervious surface compared to solitary ones, but no effect was recorded with human population density. This is in agreement with a recent literature review that reports on no consensus concerning the response of this trait to urbanization30.However, some urban habitats are shown to host more social species than rural habitats20,32, which may be linked to better reproductive success in cities compared to rural habitats such as agricultural environments65, an explanation that is consistent with our results on the soil sealing—sociality relationship.Conclusion, limits & future directionsOverall, our findings suggest that urban environment filters wild bee communities based on their functional traits. Our results also underscore different impacts of urbanization metrics on local species diversity, with a significant negative impact of soil sealing. On the contrary, both soil sealing and human population densities create strong functional filtering of trait assemblages.These results are particularly relevant since they arise from a range of independent studies, thus providing a general view on the wild bee communities in urban environments from western Europe. Since this study covers different biogeographical zones, it further underlines its applicability to other temperate countries. We therefore expect similar patterns to shape wild bee communities in urbanized areas from other temperate regions, but further confirmatory studies would be welcome.Our study also delivers a clear message concerning wild bee communities in urban environments. Urban environments cannot compare with non-urban ones in terms of species richness and trait diversities of bee communities. However, simple management practices of urban green spaces, such as differentiated management, or simply low management66, may help in maintaining this diversity. Indeed, not all green spaces are equally valuable in supporting wild bees, and pollinator assemblages in general49. For example, it has been shown that pollinator richness was positively influenced by green space size, but also by management measures such as mowing67. Increasing the quantity of floral resources and their spatio-temporal availability and diversity40,68 could also help conserving pollinator communities and pollination function in cities69, as long as these resources are native or attractive to pollinators.We can then hypothesize that changes in managing practices could help increase functional diversity of bees in cities, with specialist and ground-nesting species being found more frequently in these low-managed urban areas.Finally, if managing urban green space is of great importance to protect biodiversity in cities, it is crucial to involve all stakeholders, especially residents70 to achieve efficient and socially-accepted measures.In the future, it will be important to consider intra-city landscape variation, and see how urban characteristics might influence taxonomic and trait diversity. This will surely allow us to better understand the dynamics shaping wild bee communities in urban environments. More

  • in

    First report of glyphosate-resistant downy brome (Bromus tectorum L.) in Canada

    Powles, S. B. Evolved glyphosate-resistant weeds around the world: Lessons to be learnt. Pest Manage. Sci. 64, 360–365 (2008).CAS 

    Google Scholar 
    Bradshaw, L. D., Padgette, S. R., Kimball, S. L. & Wells, B. J. Perspectives on glyphosate resistance. Weed Technol. 11, 189–198 (1997).CAS 

    Google Scholar 
    Duke, S. O. & Powles, S. B. Glyphosate: A once-in-a-century herbicide. Pest Manage. Sci. 64, 319–325 (2008).CAS 

    Google Scholar 
    Baek, Y., Bobadilla, L. K., Giacomini, D. A., Montgomery, J. S., Murphy, B. P. & Tranel, P. J. Evolution of glyphosate-resistant weeds In Reviews of Environmental Contamination and Toxicology Volume 225 (ed. Knaak, J. B.) 93–128 (Cham, CH: Springer Nature Switzerland AG 2021).Heap, I. The international herbicide-resistant weed database www.weedscience.org (2022).Beckie, H. J. et al. A decade of herbicide-resistant crops in Canada. Can. J. Plant Sci. 61, 1243–1264 (2006).
    Google Scholar 
    Geddes, C. M. Glyphosate overreliance threatens no-till agriculture: Is kochia a canary in the coal mine? In Proceedings of the 2019 ASA-CSSA-SSSA International Annual Meeting https://scisoc.confex.com/scisoc/2019am/meetingapp.cgi/Paper/121120 (San Antonio, TX: ASA-CSSA-SSSA, 2019).Alberta Environment and Parks. Overview of 2018 pesticide sales in Alberta https://open.alberta.ca/publications/9781460148167 (Government of Alberta ISBN 978-1-4601-4816-7, 2020).Statistics Canada. Table 32-10-0359-01: Estimated areas, yield, production, average farm price and total farm value of principal field crops, in metric and imperial units https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3210035901 (2022).Brunharo, C. A. C. G. et al. Western United States and Canada perspective: Are herbicide-resistant crops the solution to herbicide-resistant weeds? Weed Sci. 66, 272–286 (2022).
    Google Scholar 
    Canadian Grain Commission. Grain varieties by acreage insured https://www.grainscanada.gc.ca/en/grain-research/statistics/varieties-by-acreage/ (2022).Statistics Canada. Table 32-10-0408-01: Tillage and seeding practices, Census of Agriculture, 2021 and 2016 https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3210040801 (2022).Upadhyaya, M. K., McIlvride, D. & Turkington, R. The biology of Canadian weeds: 75. Bromus tectorum L.. Can. J. Plant Sci. 66, 689–709 (1986).
    Google Scholar 
    Hedrick, D. W. History of cheatgrass – present geographical range and importance of cheatgrass in management of rangelands. In Cheatgrass Symposium. 13–16 (Portland, OR: US Dep. Int., Bur. Land Manage., 1965).Mack, R. N. Invasion of Bromus tectorum L. into western North America: An ecological chronicle. Agro-Ecosystems 7, 145–165 (1981).
    Google Scholar 
    Mitich, L. W. Downy brome, Bromus tectorum L. Weed Technol. 13, 664–668 (1999).
    Google Scholar 
    Morrow, L. A. & Stahlman, P. W. The history and distribution of downy brome (Bromus tectorum) in North America. Weed Sci. 32, 2–6 (1984).
    Google Scholar 
    Pellant, M. & Hall, C. Distribution of two exotic grasses on public lands in the Great Basin: status in 1992. In Proceedings–Ecology and Management of Annual Rangelands. (eds. Monsen, S. B. & Kitchen, S. G.) 109–112 (Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Research Station, General Technical Report INT-GTR-313, 1994).Leeson, J. Y., Hall, L. M. Neeser, C., Tidemann, B., & Harker, K. N. Alberta survey of annual crops in 2017. (Saskatoon, SK: Agriculture and Agri-Food Canada Weed Survey Series Publ. 19–1, 2019).Douglas, B., Thomas, A. & Derksen, D. Downy brome (Bromus tectorum) invasion into southwestern Saskatchewan. Can. J. Plant Sci. 70, 1143–1151 (1990).
    Google Scholar 
    Miller, Z. J., Menalled, F. D. & Burrows, M. Winter annual grassy weeds increase over-winter mortality in autumn-sown wheat. Weed Res. 53, 102–109 (2013).
    Google Scholar 
    Rydrych, D. J. & Muzik, T. K. Downy brome competition and control in dryland wheat. Agron. J. 60, 279–280 (1968).
    Google Scholar 
    Stahlman, P. W. & Miller, S. D. Downy brome (Bromus tectorum) interference and economic thresholds in winter wheat (Triticum aestivum). Weed Sci. 38, 224–228 (1990).
    Google Scholar 
    Blackshaw, R. E. Downy brome (Bromus tectorum) density and relative time of emergence affects interference in winter wheat (Triticum aestivum). Weed Sci. 41, 551–556 (1993).
    Google Scholar 
    Johnson, E. N. et al. Pyroxasulfone is effective for management of Bromus spp. in winter wheat in Western Canada. Weed Technol. 32, 739–748 (2018).
    Google Scholar 
    Kumar, V., Jha, P. & Jhala, A. J. Using pyroxasulfone for downy brome (Bromus tectorum L.) control in winter wheat. Am. J. Plant Sci. 8, 2367–2378 (2017).CAS 

    Google Scholar 
    Ostlie, M. H. & Howatt, K. A. Downy brome (Bromus tectorum) competition and control in no-till spring wheat. Weed Technol. 27, 502–508 (2013).CAS 

    Google Scholar 
    Steward, G. & Hull, A. C. Cheatgrass (Bromus tectorum L.) – An ecological intruder in southern Idaho. Ecology 30, 57–74 (1949).
    Google Scholar 
    Hulbert, L. C. Ecological studies of Bromus tectorum and other annual brome grasses. Ecol. Monogr. 25, 181–213 (1955).
    Google Scholar 
    Young, J. A. & Evans, R. A. Population dynamics after wildfires in sagebrush grasslands. J. Range. Manag. 31, 283–289 (1978).
    Google Scholar 
    Mack, R. N. & Pyke, D. A. The demography of Bromus tectorum: Variation in time and space. J. Ecol. 71, 69–93 (1983).
    Google Scholar 
    Pyke, A. P. & Novak, S. J. Cheatgrass demography–establishment attributes, recruitment, ecotypes and genetic variability. In Proceedings–Ecology and Management of Annual Rangelands. (eds. Monsen, S. B. & Kitchen, S. G.) 12–21 (Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Research Station, General Technical Report INT-GTR-313, 1994).Burnside, O. C., Wilson, R. G., Weisberg, S. & Hubbard, K. G. Seed longevity of 41 weed species buried 17 years in eastern and western Nebraska. Weed Sci. 44, 74–86 (1996).CAS 

    Google Scholar 
    Smith, D. C., Meyer, S. E. & Anderson, V. J. Factors affecting Bromus tectorum seed bank carryover in western Utah. Rangel. Ecol. Manag. 61, 430–436 (2008).
    Google Scholar 
    Wicks, G. A. Survival of downy brome (Bromus tectorum) seed in four environments. Weed Sci. 45, 225–228 (1997).CAS 

    Google Scholar 
    Rydrych, D. J. Competition between winter wheat and downy brome. Weed Sci. 22, 211–214 (1974).
    Google Scholar 
    Sebastian, D. J., Nissen, S. J., Sebastian, J. R. & Beck, K. G. Seed bank depletion: The key to long-term downy brome (Bromus tectorum L.) management. Rangel. Ecol. Manage. 70, 477–483 (2017).
    Google Scholar 
    Asthana, P., Zuger, R. J., Brew-Appiah, R., Sanguinet, K. & Burke, I. EPSPS gene amplification confers glyphosate resistance in Bromus tectorum (Downy brome). In Proceedings of the 2020 Weed Science Society of America (WSSA)–Western Society of Weed Science Joint Meeting. 58 (Maui, HI: WSSA, 2020).Zuger, R. J. & Burke, I. C. Testing in Washington identifies widespread postemergence herbicide resistance in annual grasses. Crops Soils Mag. 53, 13–19 (2020).
    Google Scholar 
    Davies, L. R., Hull, R., Moss, S. & Neve, P. The first cases of evolving glyphosate resistance in UK poverty brome (Bromus sterilis) populations. Weed Sci. 67, 41–47 (2019).
    Google Scholar 
    Malone, J. M., Morran, S., Shirley, N., Boutsalis, P. & Preston, C. EPSPS gene amplification in glyphosate-resistant Bromus diandrus. Pest Manage. Sci. 72, 81–88 (2016).CAS 

    Google Scholar 
    Vázquez-García, J. G. et al. Glyphosate resistance confirmation and field management of red brome (Bromus rubens L.) in perennial crops grown in southern Spain. Agronomy 11, 535 (2021).
    Google Scholar 
    Park, K. W. & Mallory-Smith, C. A. Physiological and molecular basis for ALS inhibitor resistance in Bromus tectorum biotypes. Weed Res. 44, 71–77 (2004).CAS 

    Google Scholar 
    Kumar, V. & Jha, P. First report of Ser653Asn mutation endowing high-level resistance to imazamox in downy brome (Bromus tectorum L.). Pest Manag. Sci. 73, 2585–2591 (2017).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Baerson, G. T. et al. Glyphosate resistant goosegrass. Identification of a mutation in the target enzyme 5-enolpyruvylshikimate-3-phosphate synthase. Plant Physiol. 129, 1265–1274 (2002).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gaines, T. A. et al. Mechanism of resistance of evolved glyphosate-resistant palmer amaranth (Amaranthus palmeri). J. Agric. Food Chem. 59, 5886–5889 (2011).CAS 
    PubMed 

    Google Scholar 
    Jugulam, M. et al. Tandem amplification of a chromosomal segment harboring 5-enolpyruvylshikimate-3-phosphate synthase locus confers glyphosate resistance in Kochia scoparia. Plant Physiol. 166, 1200–1207 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Metier, E. P., Lehnhoff, E. A., Mangold, J., Rinella, M. J. & Rew, L. J. Control of downy brome (Bromus tectorum) and Japanese brome (Bromus japonicus) using glyphosate and four graminicides: Effects of herbicide rate, plant size, species, and accession. Weed Technol. 34, 284–291 (2020).
    Google Scholar 
    Reddy, S., Stahlman, P. & Geier, P. Downy brome (Bromus tectorum L.) and broadleaf weed control in winter wheat with acetolactate synthase-inhibiting herbicides. Agronomy 3, 340–348 (2013).CAS 

    Google Scholar 
    Blackshaw, R. E. Differential competitive ability of winter wheat cultivars against downy brome. Agron. J. 86, 649–654 (1994).
    Google Scholar 
    Blackshaw, R. E. Rotation affects downy brome (Bromus tectorum) in winter wheat (Triticum aestivum). Weed Technol. 8, 728–732 (1994).
    Google Scholar 
    Wicks, G. A. Integrated systems for control and management of downy brome (Bromus tectorum) in cropland. Weed Sci. 32, 26–31 (1984).CAS 

    Google Scholar 
    Anderson, R. L. Timing of nitrogen application affects downy brome (Bromus tectorum) growth in winter wheat. Weed Technol. 5, 582–585 (1991).
    Google Scholar 
    Blackshaw, R. E., Larney, F. J., Lindwall, C. W., Watson, P. R. & Derksen, D. A. Tillage intensity and crop rotation affect weed community dynamics in a winter wheat cropping system. Can. J. Plant Sci. 81, 805–813 (2001).
    Google Scholar 
    Evans, R. A. & Young, J. A. Microsite requirements of downy brome (Bromus tectorum) infestation and control on sagebrush rangelands. Weed Sci. 32, 13–17 (1984).
    Google Scholar 
    QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project https://qgis.org/en/site/ (2022).Sheldrake, T. Jr. & Boodley, J. W. Plant growing in light-weight artificial mixes. Acta Hortic. 4, 155–157 (1966).
    Google Scholar 
    Canadian Weed Science Society – Société Canadienne de Malherbologie (CWSS-SCM). Description of 0–100 rating scale for herbicide efficacy and phytotoxicity https://weedscience.ca/cwss_scm-rating-scale/ (2018).Littell, R. C., Milken, G. A., Stroup, W. W., Wolfinger, R. R. & Schabenberger, O. SAS for mixed models 2nd edn. (SAS Institute Inc., 2006).
    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. (Vienna, Austria: R Foundation for Statistical Computing, 2019).Ritz, C., Baty, F., Streibig, F. C. & Gerhard, D. Dose-response analysis using R. PLoS ONE 10, e0146021 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    Seefeldt, S. S., Jensen, J. E. & Fuerst, E. P. Log-logistic analysis of herbicide dose-response relationships. Weed Technol. 9, 218–227 (1995).
    Google Scholar  More