Ecology

Wesche, K. et al. The Palaearctic steppe biome: A new synthesis. Biodivers. Conserv. 25, 2197–2231 (2016).
Google Scholar 
Hurka, H. et al. The Eurasian steppe belt: Status quo, origin and evolutionary history. Turczaninowia 22, 5–71 (2019).
Google Scholar 
Willner, W. et al. Formalized classification of semi-dry grasslands in central and eastern Europe. Preslia 91, 25–49 (2019).
Google Scholar 
Willis, K. J. & McElwain, J. C. The Evolution Of Plants (Oxford University Press, Oxford, 2002).Suc, J.-P. et al. Reconstruction of Mediterranean flora, vegetation and climate for the last 23 million years based on an extensive pollen dataset. Ecol. Mediterr. 44, 53–85 (2018).
Google Scholar 
Strani, F. Impact of Early and Middle Pleistocene major climatic events on the palaeoecology of Southern European ungulates. Hist. Biol. 33, 2260–2275 (2021).
Google Scholar 
Chytrý, M. et al. A modern analogue of the Pleistocene steppe-tundra ecosystem in southern Siberia. Boreas 48, 36–56 (2019).
Google Scholar 
Manafzadeh, S., Staedler, Y. M. & Conti, E. Visions of the past and dreams of the future in the orient: The Irano–Turanian region from classical botany to evolutionary studies. Biol. Rev. 92, 1365–1388 (2017).PubMed 

Google Scholar 
Seregin, A. P., Anačkov, G. & Friesen, N. Molecular and morphological revision of the Allium saxatile group (Amaryllidaceae): Geographical isolation as the driving force of underestimated speciation. Bot. J. Linn. Soc. 178, 67–101 (2015).
Google Scholar 
Friesen, N. et al. Dated phylogenies and historical biogeography of Dontostemon and Clausia (Brassicaceae) mirror the palaeogeographical history of the Eurasian steppe. J. Biogeogr. 43, 738–749 (2016).
Google Scholar 
Seidl, A. et al. Phylogeny and biogeography of the Pleistocene Holarctic steppe and semi-desert goosefoot plant. Krascheninnikovia ceratoides. Flora 262, 151504 (2020).
Google Scholar 
Seidl, A. et al. The phylogeographic history of Krascheninnikovia reflects the development of dry steppes and semi-deserts in Eurasia. Sci. Rep. 11, 6645 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Žerdoner Čalasan, A., Seregin, A. P., Hurka, H., Hofford, N. P. & Neuffer, B. The Eurasian steppe belt in time and space: Phylogeny and historical biogeography of the false flax (Camelina Crantz, Camelineae, Brassicaceae). Flora 260, 151477 (2019).
Google Scholar 
Žerdoner Čalasan, A. et al. Pleistocene dynamics of the Eurasian steppe as a driving force of evolution: Phylogenetic history of the genus Capsella (Brassicaceae). Ecol. Evol. 11, 12697–12713 (2021).PubMed 
PubMed Central 

Google Scholar 
Žerdoner Čalasan, A., German, D. A., Hurka, H. & Neuffer, B. A story from the Miocene: Clock-dated phylogeny of Sisymbrium L. (Sisymbrieae, Brassicaceae). Ecol. Evol. 11, 2573–2595 (2021).PubMed 
PubMed Central 

Google Scholar 
Zaveská, E. et al. Multiple auto- and allopolyploidisations marked the Pleistocene history of the widespread Eurasian steppe plant Astragalus onobrychis (Fabaceae). Mol. Phylogenet. Evol. 139, 106572 (2019).PubMed 

Google Scholar 
Franzke, A. et al. Molecular signals for Late Tertiary/Early Quaternary range splits of an Eurasian steppe plant: Clausia aprica (Brassicaceae). Mol. Ecol. 13, 2789–2795 (2004).CAS 
PubMed 

Google Scholar 
Friesen, N. et al. Evolutionary history of the Eurasian steppe plant Schivereckia podolica (Brassicaceae) and its close relatives. Flora 268, 151602 (2020).
Google Scholar 
Walter, H. & Straka, H. Arealkunde (Floristisch-Historische Geobotanik), second edition (Ulmer, 1970).Zimmermann, W. 50c. Familie Ranunculáceae in Gustav Hegi, Illustrierte Flora Von Mitteleuropa, Band III, Teil 3, second edition (eds. Rechinger, K. H. & Damboldt, J.) 53–341 (Parey, 1965–1974).Meusel, H., Jäger, E. & Weinert, E. Vergleichende Chorologie Der Zentraleuropäischen Flora, Band I (Gustav Fischer, 1965).Hoffmann, M. H. Ecogeographical differentiation patterns in Adonis sect. Consiligo (Ranunculaceae). Plant Syst. Evol. 211, 43–56 (1998).
Google Scholar 
Willner, W. et al. A higher-level classification of the Pannonian and western Pontic steppe grasslands (Central and Eastern Europe). Appl. Veg. Sci. 20, 143–158 (2017).PubMed 

Google Scholar 
Lange, D. Conservation and Sustainable Use of Adonis vernalis, a Medicinal Plant in International Trade (Landwirtschaftsverlag, Münster, 2000).Denisow, B., Wrzesień, M. & Cwener, A. Pollination and floral biology of Adonis vernalis L. (Ranunculaceae)—A case study of threatened species. Acta Soc. Bot. Pol. 83, 29–37 (2014).
Google Scholar 
Mitrenina, E. Y. et al. Karyotype and genome size in Adonis vernalis and Adonis volgensis. Turczaninowia 25, 5–15 (2022).
Google Scholar 
Zhai, W. et al. Chloroplast genomic data provide new and robust insights into the phylogeny and evolution of the Ranunculaceae. Mol. Phylogenet. Evol. 135, 12–21 (2019).CAS 
PubMed 

Google Scholar 
Bobrov, E. G. Genus 540. ADONIS L. In Flora Of The U.S.S.R., Volume VII, Ranales And Rhoeadales (ed. Komarov, V. L.) pp. 403–411 (Academy of Sciences of the U.S.S.R., 1970).Pisareva, V. V. et al. Changes in the landscape and climate of Eastern Europe in the Early Pleistocene. Stratigr. Geol. Correl. 27, 475–497 (2019).ADS 

Google Scholar 
Vislobokova, I., Tesakov, A. Early And Middle Pleistocene Of Northern Eurasia. In Encyclopedia Of Quaternary Science, Vol 4, 2nd edn (ed. Elias, S. A.) pp. 605–614 (Elsevier, Amsterdam, 2013).Hirsch, H. et al. High genetic diversity declines towards the geographic range periphery of Adonis vernalis, a Eurasian dry grassland plant. Plant Biol. 17, 1233–1241 (2015).CAS 
PubMed 

Google Scholar 
Kirschner, P. et al. Long-term isolation of European steppe outposts boosts the biome’s conservation value. Nat. Commun. 11, 1968 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Willner, W. et al. Long-term continuity of steppe grasslands in eastern Central Europe: Evidence from species distribution patterns and chloroplast haplotypes. J. Biogeogr. 48, 3104–3117 (2021).
Google Scholar 
Molnár, Z., Biró, M., Bartha, S. & Fekete, G. Past trends, present state and future prospects of Hungarian forest-steppes. In Eurasian Steppes. Ecological Problems And Livelihoods In A Changing World (eds Werger, M. J. A. & van Staalduinen, M. A.) pp. 209–252 (Springer, Dordrecht, 2012).
Google Scholar 
Liedtke, H. Die Nordischen Vereisungen in Mitteleuropa, 2nd edn. (Zentralausschuß für deutsche Landeskunde, Trier, 1981).Sizikova, A. O. & Zykina, V. S. The dynamics of the Late Pleistocene loess formation, Lozhok section, Ob loess Plateau, SW Siberia. Quat. Int. 365, 4–14 (2015).
Google Scholar 
Zykina, V. S. & Zykin, V. S. Pleistocene warming stages in Southern West Siberia: Soils, environment, and climate evolution. Quat. Int. 106–107, 233–243 (2003).
Google Scholar 
Shumilovskikh, L., Sannikov, P., Efimik, E., Shestakov, I. & Mingalev, V. V. Long-term ecology and conservation of the Kungur forest-steppe (pre-Urals, Russia): Case study Spasskaya Gora. Biodivers. Conserv. 30, 4061–4087 (2021).
Google Scholar 
Markova, A. & Puzachenko, A. Vertebrate records/Late Pleistocene of Northern Asia. In Encyclopedia of Quaternary Science Vol. 4 (ed. Elias, S. A.) 3158–3175 (Elsevier, Amsterdam, 2007).Gómez, C. & Espadaler, X. An update of the world survey of myrmecochorous dispersal distances. Ecography 36, 1193–1201 (2013).
Google Scholar 
Albert, A. et al. Seed dispersal by ungulates as an ecological filter: A trait-based meta-analysis. Oikos 124, 1109–1120 (2015).
Google Scholar 
Albert, A., Mårell, A., Picard, M. & Baltzinger, C. Using basic plant traits to predict ungulate seed dispersal potential. Ecography 38, 440–449 (2015).
Google Scholar 
Popescu, S.-M. et al. Late Quaternary vegetation and climate of SE Europe–NW Asia according to pollen records in three offshore cores from the Black and Marmara seas. Paleobiodivers. Paleoenviron. 101, 197–212 (2021).
Google Scholar 
Markova, A. K., Simakova, A. N. & Puzachenko, A. Y. Ecosystems of Eastern Europe at the time of maximum cooling of the Valdai glaciation (24–18 kyr BP) inferred from data on plant communities and mammal assemblages. Quat. Int. 201, 53–59 (2009).
Google Scholar 
Kajtoch, Ł et al. Phylogeographic patterns of steppe species in Eastern Central Europe: A review and the implications for conservation. Biodivers. Conserv. 25, 2309–2339 (2016).
Google Scholar 
Kropf, M., Bardy, K., Höhn, M. & Plenk, K. Phylogeographical structure and genetic diversity of Adonis vernalis L. (Ranunculaceae) across and beyond the Pannonian region. Flora 262, 151497 (2020).
Google Scholar 
Plenk, K., Bardy, K., Höhn, M., Thiv, M. & Kropf, M. No obvious genetic erosion, but evident relict status at the westernmost range edge of the Pontic-Pannonian steppe plant Linum flavum L. (Linaceae) in Central Europe. Ecol. Evol. 7, 6527–6539 (2017).PubMed 
PubMed Central 

Google Scholar 
Magyari, E. K. et al. Late Pleniglacial vegetation in eastern-central Europe: Are there modern analogues in Siberia? Quat. Sci. Rev. 95, 60–79 (2014).ADS 

Google Scholar 
Doležel, J., Greilhuber, J. & Suda, J. Estimation of nuclear DNA content in plants using flow cytometry. Nat. Protoc. 2, 2233–2244 (2007).PubMed 

Google Scholar 
Shaw, J., Lickey, E. B., Schilling, E. E. & Small, R. L. Comparison of whole chloroplast genome sequences to choose noncoding regions for phylogenetic studies in angiosperms: The tortoise and the hare III. Am. J. Bot. 94, 275–288 (2007).CAS 
PubMed 

Google Scholar 
Shaw, J. et al. The tortoise and the hare II: Relative utility of 21 noncoding chloroplast DNA sequences for phylogenetic analysis. Am. J. Bot. 92, 142–166 (2005).CAS 
PubMed 

Google Scholar 
Heckenhauer, J., Barfuss, M. H. J. & Samuel, R. Universal multiplexable matK primers for DNA barcoding of angiosperms. Appl. Plant Sci. 4, 1500137 (2016).
Google Scholar 
White, T. J., Bruns, T., Lee, S. & Taylor, J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: A Guide to Methods and Applications (eds Innis, M. A. et al.) pp. 315–322 (Academic Press, Cambridge, 1990).
Google Scholar 
Simmons, M. P. & Ochoterena, H. Gaps as characters in sequence-based phylogenetic analyses. Syst. Biol. 49, 369–381 (2000).CAS 
PubMed 

Google Scholar 
Müller, K. SeqState: Primer design and sequence statistics for phylogenetic DNA datasets. Appl. Bioinformat. 4, 65–69 (2005).
Google Scholar 
Swofford, D. L. PAUP*. Software. https://paup.phylosolutions.com/.Kozlov, A., Darriba, D., Flouri, T., Morel, B. & A.,. Stamatakis RAxML-NG: A fast, scalable, and user-friendly tool for maximum likelihood phylogenetic inference. Bioinformatics 35, 4453–4455 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bouckaert, R. et al. BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Heled, J. & Drummond, A. J. Calibrated tree priors for relaxed phylogenetics and divergence time estimation. Syst. Biol. 61, 138–149 (2012).PubMed 

Google Scholar 
Drummond, A. J., Ho, S. Y. W., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4, e88 (2006).PubMed 
PubMed Central 

Google Scholar 
Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rambaut, A. FigTree. Software. http://tree.bio.ed.ac.uk/software/figtree/.Wendler, N. et al. Unlocking the secondary gene-pool of barley with next-generation sequencing. Plant Biotechnol. J. 12, 1122–1131 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Eaton, D. A. R. & Overcast, I. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics 36, 2592–2594 (2020).CAS 
PubMed 

Google Scholar 
Weiß, C. L., Pais, M., Cano, L. M., Kamoun, S. & Burbano, H. A. nQuire: A statistical framework for ploidy estimation using next generation sequencing. BMC Bioinform. 19, 122 (2018).
Google Scholar 
Corrêa dos Santos, R. A., Goldman, G. H. & Riaño-Pachón, D. M. ploidyNGS: Visually exploring ploidy with next generation sequencing data. Bioinformatics 33, 2575–2576 (2017).
Google Scholar 
Frichot, E. & François, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Google Scholar 
R Core Team. R. A language and environment for statistical computing. Software. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.Gruber, B., Unmack, P. J., Berry, O. F. & Georges, A. dartR: An R package to facilitate analysis of SNP data generated from reduced representation genome sequencing. Mol. Ecol. Resour. 18, 691–699 (2018).PubMed 

Google Scholar 
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Minh, B. Q. et al. IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Guindon, S. et al. New algorithms and methods to estimate maximum-likelihood phylogenies: Assessing the performance of PhyML 3.0. Syst. Biol. 59, 307–321 (2010).CAS 
PubMed 

Google Scholar 
Minh, B. Q., Nguyen, M. A. T. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Huson, D. H. & Bryant, D. Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23, 254–267 (2005).PubMed 

Google Scholar 
Kalinowski, S. T. HP-RARE 1.0: A computer program for performing rarefaction on measures of allelic richness. Mol. Ecol. Notes 5, 187–189 (2005).CAS 

Google Scholar  More

Alvares, F. et al. Old Wolrd Canis spp. with taxonomic ambiguity: Workshop conclusions and recommendations Vairao, Portugal, 28th–30th May 2019. Canid News (Online Edition) (2019).Jackson, S. M. et al. Taxonomy of the dingo: It’s an ancient dog. Aust. Zool. 41, 347–357 (2021).
Google Scholar 
Stephens, D., Wilton, A. N., Fleming, P. J. S. & Berry, O. Death by sex in an Australian icon: A continent-wide survey reveals extensive hybridization between dingoes and domestic dogs. Mol. Ecol. 24, 5643–5656 (2015).CAS 
PubMed 

Google Scholar 
Cairns, K. M., Shannon, L. M., Koler-Matznick, J., Ballard, J. W. O. & Boyko, A. R. Elucidating biogeographical patterns in Australian native canids using genome wide SNPs. PLoS ONE 13, e0198754 (2018).PubMed 
PubMed Central 

Google Scholar 
Fleming, P. J. S., Ballard, G. & Cutter, N. There is no Dingo dilemma: legislation facilitates culling, containment and conservation of Dingoes in New South Wales. Aust. Zool. 41, 408–416 (2021).
Google Scholar 
Corbett, L. K. The Dingo in Australia and Asia. Second edn, (JB Books Australia, 2001).Newsome, T. M. et al. Making a new dog?. Bioscience 67, 374–381 (2017).
Google Scholar 
Wang, G.-D. et al. Out of southern East Asia: the natural history of domestic dogs across the world. Cell Res. 26, 21–33 (2016).PubMed 

Google Scholar 
Smith, B. The Dingo Debate: Origins, Behaviour and Conservation. (CSIRO Publishing, 2015).Jackson, S. M. et al. The dogma of dingoes-taxonomic status of the dingo: A reply to Smith et al. Zootaxa 4564, 198–212 (2019).
Google Scholar 
Zhang, S. J. et al. Genomic regions under selection in the feralization of the dingoes. Nat. Commun. 11, 671 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Balme, J. & O’Connor, S. Dingoes and Aboriginal social organization in Holocene Australia. J. Archaeol. Sci. Rep. 7, 775–781 (2016).
Google Scholar 
Cairns, K. M. What is a dingo – origins, hybridisation and identity. Aust. Zool. 41(3), 322–337 (2021).
Google Scholar 
Allen, B. L. & West, P. Influence of dingoes on sheep distribution in Australia. Aust. Vet. J. 91, 261–267 (2013).CAS 
PubMed 

Google Scholar 
Fleming, P. J. S. in Carnivores of Australia: Past, Present and Future (eds A.S. Glen & C.R. Dickman) Ch. 6, 105–149 (CSIRO Publishing, 2014).Stephens, D. The molecular ecology of Australian wild dogs: hybridisation, gene flow and genetic structure at multiple geographic scales, The University of Western Australia, (2011).Cairns, K. M., Nesbitt, B. J., Laffan, S. W., Letnic, M. & Crowther, M. S. Geographic hot spots of dingo genetic ancestry in southeastern Australia despite hybridisation with domestic dogs. Conserv. Genet. 21, 77–90 (2020).CAS 

Google Scholar 
Wilton, A. N., Steward, D. J. & Zafiris, K. Microsatellite variation in the Australian dingo. J. Hered. 90, 108–111 (1999).CAS 
PubMed 

Google Scholar 
Allendorf, F. W., Leary, R. F., Spruell, P. & Wenburg, J. K. The problems with hybrids: Setting conservation guidelines. Trends Ecol. Evol. 16, 613–622 (2001).
Google Scholar 
Atkinson, J. An account of the state of agriculture & grazing in New South Wales. (J. Cross, 1826).Massy, C. The Australian Merino: The Story of a Nation (Revised and updated). xxii,1262 (Random House Australia, 2007).Cairns, K. M., Brown, S. K., Sacks, B. N. & Ballard, J. W. O. Conservation implications for dingoes from the maternal and paternal genome: Multiple populations, dog introgression, and demography. Ecol. Evol. 7, 9787–9807 (2017).PubMed 
PubMed Central 

Google Scholar 
Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: A program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Driscoll, C., Yamaguchi, N., O’Brien, S. J. & Macdonald, D. W. A suite of genetic markers useful in assessing wildcat (Felis silvestris ssp.)-domestic cat (Felis silvestris catus) admixture. J. Hered. 102(1), S87–S90 (2011).PubMed 
PubMed Central 

Google Scholar 
Bohling, J. H. & Waits, L. P. Factors influencing red wolf–coyote hybridization in eastern North Carolina USA. Biol. Conserv. 184, 108–116 (2015).
Google Scholar 
Fleming, P., Corbett, L., Harden, R. & Thomson, P. in Managing the Impacts of Dingoes and Other Wild Dogs. (Bureau of Rural Sciences, Canberra, 2001).Van Veldhuisen, R. Pipe dreams: A history of water supply in the Wimmera-Mallee (Wimmera Mallee Water, 2001).Newsome, A. The distribution of red kangaroos, Megaleia rufa (Desmarest), about sources of persistent food and water in central Australia. Aust. J. Zool. 13, 289–300 (1965).
Google Scholar 
James, C. D., Landsberg, J. & Morton, S. R. Provision of watering points in the Australian arid zone: A review of effects on biota. J. Arid Environ. 41, 87–121 (1999).ADS 

Google Scholar 
Robinson, J. A., Brown, C., Kim, B. Y., Lohmueller, K. E. & Wayne, R. K. Purging of strongly deleterious mutations explains long-term persistence and absence of inbreeding depression in island foxes. Curr. Biol. 28, 3487-3494.e3484 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Benazzo, A. et al. Survival and divergence in a small group: The extraordinary genomic history of the endangered Apennine brown bear stragglers. PNAS 114, E9589–E9597 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mattucci, F. et al. Genomic approaches to identify hybrids and estimate admixture times in European wildcat populations. Sci. Rep. 9, 11612 (2019).ADS 
PubMed 
PubMed Central 

Google Scholar 
Thomson, P. C., Rose, K. & Kok, N. E. The behavioural ecology of dingoes in north-western Australia. VI. Temporary extra-terrestrial movements and dispersal. Wildl. Res. 19, 585–595 (1992).
Google Scholar 
Newsome, T. M., Ballard, G.-A., Dickman, C. R., Fleming, P. J. S. & van de Ven, R. Home range, activity and sociality of a top predator, the dingo: A test of the Resource Dispersion Hypothesis. Ecography 36, 914–925 (2013).
Google Scholar 
Giglio, R. M., Rocke, T. E., Osorio, J. E. & Latch, E. K. Characterizing patterns of genomic variation in the threatened Utah prairie dog: Implications for conservation and management. Evol. Appl. 14, 1036–1051 (2021).PubMed 

Google Scholar 
Conroy, G. C. et al. Conservation concerns associated with low genetic diversity for K’gari–Fraser Island dingoes. Sci. Rep. 11, 9503 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frankham, R. Do island populations have less genetic variation than mainland populations?. Heredity 78, 311–327 (1997).PubMed 

Google Scholar 
Funk, W. C. et al. Adaptive divergence despite strong genetic drift: genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (Urocyon littoralis). Mol. Ecol. 25, 2176–2194 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Behrendorff, L. Best-practice dingo management: six lessons from K’gari (Fraser Island). Aust. Zool. 41, 521–533 (2021).
Google Scholar 
van Eeden, L. M., Smith, B. P., Crowther, M. S., Dickman, C. R. & Newsome, T. M. ‘The dingo menace’: An historic survey on graziers’ management of an Australian carnivore. Pac. Conserv. Biol. 25, 245–256 (2019).
Google Scholar 
Whiting, S. D., Long, J. L., Hadden, K. M., Lauder, A. D. K. & Koch, A. U. Insights into size, seasonality and biology of a nesting population of the Olive Ridley turtle in northern Australia. Wildl. Res. 34, 200–210 (2007).
Google Scholar 
Banks, S. C., Hoyle, S. D., Horsup, A., Sunnucks, P. & Taylor, A. C. Demographic monitoring of an entire species (the northern hairy-nosed wombat, Lasiorhinus krefftii) by genetic analysis of non-invasively collected material. Anim. Conserv. 6, 101–107 (2003).
Google Scholar 
Parker, H. G. et al. Genomic analyses reveal the influence of geographic origin, migration, and hybridization on modern dog breed development. Cell Rep. 19, 697–708 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Thackway, R. & Cresswell, I. An Interim Biogeographic Regionalisation for Australia: A Framework for Setting Priorities in the National Reserves System Cooperative Program. Version 4, (Australian Nature Conservation Agency, Reserve Systems Unit, 1995).Bureau of Meteorology & CSIRO. (Bureau of Meteorology, CSIRO and Farmlink, http://www.bom.gov.au/climate/climate-guides/guides/01-Mallee-VIC-Climate-Guide.pdf, 2019).Rowan, J. N. & Downes, R. G. in Soil Conservation Authority of Victoria (ed Brookes, A.C.) 1–55 (Govt. Printer, Melbourne, 1963).Longmire, J. L., Maltbie, M. & Baker, R. J. Use of “lysis buffer” in DNA isolation and its implications for museum collections. Occas. Pap. Mus. Tex. Tech. Univ. 163, 1–3 (1997).
Google Scholar 
Tatler, J., Prowse, T. A. A., Roshier, D. A., Cairns, K. M. & Cassey, P. Phenotypic variation and promiscuity in a wild population of pure dingoes (Canis dingo). J. Zool. Syst. Evol. Res. 59, 311–322 (2020).
Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Francis, R. M. pophelper: An R package and web app to analyse and visualize population structure. Mol. Ecol. Resour. 17, 27–32 (2017).CAS 
PubMed 

Google Scholar 
Wang, J. The computer program structure for assigning individuals to populations: Easy to use but easier to misuse. Mol. Ecol. Resour. 17, 981–990 (2017).CAS 
PubMed 

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

Google Scholar 
Earl, D. A. & von Holdt, B. M. Structure harvester: A website and program for visualizing structure output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).
Google Scholar 
Verity, R. & Nichols, R. A. Estimating the number of subpopulations (K) in structured populations. Genetics 203, 1827–1839 (2016).PubMed 
PubMed Central 

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

Google Scholar 
Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 

Google Scholar 
Keenan, K. et al. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar 
Peakall, R. & Smouse, P. E. genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).
Google Scholar 
Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28, 2537–2539 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lynch, M. & Ritland, K. Estimation of pairwise relatedness with molecular markers. Genetics 152, 1753–1766 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370 (1984).CAS 
PubMed 

Google Scholar 
Jost, L. GST and its relatives do not measure differentiation. Mol. Ecol. 17, 4015–4026 (2008).PubMed 

Google Scholar 
Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
PubMed 

Google Scholar 
Shirk, A. J. & Cushman, S. A. sGD: Software for estimating spatially explicit indices of genetic diversity. Mol. Ecol. Resour. 11, 922–934 (2011).CAS 
PubMed 

Google Scholar 
Schnute, J., Boers, N., Haigh, R. & Couture-Beil, A. Introduction to PBSmapping. (2016). More

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

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

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

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

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

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