Philippa Kaur

1.IPCC, Working Group I Contribution to the IPCC Fifth Assessment Report, Climate Change 2013: The Physical Science Basis, AR5. 2013.2.IPCC, Working Group I Report ‘The Physical Science Basis,’ PCC Fourth Assessment Report. 2007.3.IPCC, “IPCC Fourth Assessment Report (AR4),” IPCC, 1, 976, 2007.4.Guerra, C. A. et al. Blind spots in global soil biodiversity and ecosystem function research. Nature Communications 11, 1–13 (2020).Article 
CAS 

Google Scholar 
5.Nagy, K., Ábrahám, Á., Keymer, J. E. & Galajda, P. Application of microfluidics in experimental ecology: the importance of being spatial. Front. Microbiol. 9, 496 (2018).6.Tecon, R. & Or, D. Biophysical processes supporting the diversity of microbial life in soil. FEMS Microbiol. Rev. 41, 599–623 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive earth’s biogeochemical cycles. Science. https://doi.org/10.1126/science.1153213 (2008).8.Hobbie, J. E. & Hobbie, E. A. Microbes in nature are limited by carbon and energy: the starving-survival lifestyle in soil and consequences for estimating microbial rates. Front. Microbiol. 4, 1–11 (2013). no. NOV.Article 

Google Scholar 
9.Hill, P. W., Farrar, J. F. & Jones, D. L. Decoupling of microbial glucose uptake and mineralization in soil. Soil Biol. Biochem. https://doi.org/10.1016/j.soilbio.2007.09.008 (2008).10.IPCC. Climate change 2007: the physical science basis. 2007, https://doi.org/10.1260/095830507781076194.11.Baveye, P. C. et al. Emergent properties of microbial activity in heterogeneous soil microenvironments: different research approaches are slowly converging, yet major challenges remain. Front. Microbiol. https://doi.org/10.3389/fmicb.2018.01929 (2018).12.Bruand, A. & Cousin, I. Variation of textural porosity of a clay‐loam soil during compaction. Eur. J. Soil Sci. https://doi.org/10.1111/j.1365-2389.1995.tb01334.x (1995).13.Cnudde, V. & Boone, M. N. High-resolution X-ray computed tomography in geosciences: a review of the current technology and applications. Earth-Science Rev. https://doi.org/10.1016/j.earscirev.2013.04.003 (2013).14.Pagliai, M., Vignozzi, N. & Pellegrini, S. Soil structure and the effect of management practices. https://doi.org/10.1016/j.still.2004.07.002 (2004).15.Pires, L. F., Bacchi, O. O. S., Reichardt, K. & Timm, L. C. Application of γ-ray computed tomography to analysis of soil structure before density evaluations. Appl. Radiat. Isot. https://doi.org/10.1016/j.apradiso.2005.03.019 (2005).16.Larsbo, M., Koestel, J., Kätterer, T. & Jarvis, N. Preferential transport in macropores is reduced by soil organic carbon. Vadose Zone J. https://doi.org/10.2136/vzj2016.03.0021 (2016).17.Ananyeva, K., Wang, W., Smucker, A. J. M., Rivers, M. L. & Kravchenko, A. N. Can intra-aggregate pore structures affect the aggregate’s effectiveness in protecting carbon? Soil Biol. Biochem. https://doi.org/10.1016/j.soilbio.2012.10.019 (2013).18.Toosi, E. R., Kravchenko, A. N., Mao, J., Quigley, M. Y. & Rivers, M. L. Effects of management and pore characteristics on organic matter composition of macroaggregates: evidence from characterization of organic matter and imaging. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.12411 (2017).19.Katuwal, S. et al. Linking air and water transport in intact soils to macropore characteristics inferred from X-ray computed tomography. Geoderma. https://doi.org/10.1016/j.geoderma.2014.08.006 (2015).20.Negassa, W. C. et al. Properties of soil pore space regulate pathways of plant residue decomposition and community structure of associated bacteria. PLoS ONE https://doi.org/10.1371/journal.pone.0123999 (2015).21.Rabot, E., Wiesmeier, M., Schlüter, S. & Vogel, H. J. Soil structure as an indicator of soil functions: a review. Geoderma. https://doi.org/10.1016/j.geoderma.2017.11.009 (2018).22.Pronk, G. J. et al. Interaction of minerals, organic matter, and microorganisms during biogeochemical interface formation as shown by a series of artificial soil experiments. Biol. Fertil. Soils. https://doi.org/10.1007/s00374-016-1161-1 (2017).23.Downie, H. et al. Transparent Soil for Imaging the Rhizosphere. PLoS ONE. https://doi.org/10.1371/journal.pone.0044276 (2012).24.Aleklett, K. et al. Build your own soil: exploring microfluidics to create microbial habitat structures. ISME J. 12, 312–319 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Beebe, D. J., Mensing, G. A. & Walker, G. M. Physics and Applications of Microfluidics in Biology. Annu. Rev. Biomed. Eng. https://doi.org/10.1146/annurev.bioeng.4.112601.125916 (2002).26.Ahmed, T., Shimizu, T. S. & Stocker, R. Microfluidics for bacterial chemotaxis. Integr. Biol. https://doi.org/10.1039/c0ib00049c (2010).27.Ahmed, T. & Stocker, R. Experimental verification of the behavioral foundation of bacterial transport parameters using microfluidics. Biophys. J. https://doi.org/10.1529/biophysj.108.134510 (2008).28.Mao, H., Cremer, P. S. & Manson, M. D. A sensitive, versatile microfluidic assay for bacterial chemotaxis. Proc. Natl Acad. Sci. https://doi.org/10.1073/pnas.0931258100 (2003).29.Saragosti, J. et al. Directional persistence of chemotactic bacteria in a traveling concentration wave. Proc. Natl Acad. Sci. https://doi.org/10.1073/pnas.1101996108 (2011).30.Deng, J. et al. Synergistic effects of soil microstructure and bacterial EPS on drying rate in emulated soil micromodels. Soil Biol. Biochem. 83, 116–124 (2015).CAS 
Article 

Google Scholar 
31.Rubinstein, R. L., Kadilak, A. L., Cousens, V. C., Gage, D. J. & Shor, L. M. Protist-facilitated particle transport using emulated soil micromodels. Environ. Sci. Technol. 49, 1384–1391 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Stanley, C. E. et al. Probing bacterial-fungal interactions at the single cell level. Integr. Biol. 6, 935–945 (2014).CAS 
Article 

Google Scholar 
33.Aleklett, K., Ohlsson, P., Bengtsson, M. & Hammer, E. C. Fungal foraging behaviour and hyphal space exploration in micro-structured Soil Chips. ISME J. https://doi.org/10.1038/s41396-020-00886-7 (2021).34.Mafla-Endara, P. M. et al. Microfluidic chips provide visual access to in situ soil ecology. Commun. Biol. 4, 889 (2021).35.Falconer, R., Houston, A., Otten, W. & Baveye, P. Emergent behavior of soil fungal dynamics: influence of soil architecture and water distribution. Soil Sci. 177, 111–119 (2012).CAS 
Article 

Google Scholar 
36.Duffy, K. J. & Ford, R. M. Turn angle and run time distributions characterize swimming behavior for Pseudomonas putida. J. Bacteriol. 179, 1428–1430 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Rashid, S. et al. Adjustment in tumbling rates improves bacterial chemotaxis on obstacle-laden terrains. Proc. Natl Acad Sci USA 116, 11770–11775 (2019).38.Duffy, K. J., Cummings, P. T. & Ford, R. M. Random walk calculations for bacterial migration in porous media. Biophys. J. 68, 800–806 (1995).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Shum, H. & Gaffney, E. A. Hydrodynamic analysis of flagellated bacteria swimming in corners of rectangular channels. Phys. Rev. E 92, 1–11 (2015).Article 
CAS 

Google Scholar 
40.Guadayol, Ò., Thornton, K. L. & Humphries, S. Cell morphology governs directional control in swimming bacteria. Sci. Rep. https://doi.org/10.1038/s41598-017-01565-y (2017).41.Essig, A. et al. Copsin, a novel peptide-based fungal antibiotic interfering with the peptidoglycan synthesis. J. Biol. Chem. 289, 34953–34964 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
42.Dixon, E. F. & Hall, R. A. Noisy neighbourhoods: quorum sensing in fungal-polymicrobial infections. Cell. Microbiol. 17, 1431–1441 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
43.Banitz, T. et al. Assessing biodegradation benefits from dispersal networks. Ecol. Model. 222, 2552–2560 (2011).Article 

Google Scholar 
44.Furuno, S. et al. Fungal mycelia allow chemotactic dispersal of polycyclic aromatic hydrocarbon-degrading bacteria in water-unsaturated systems. Environ. Microbiol. 12, 1391–1398 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Kohlmeier, S. et al. Taking the fungal highway: mobilization of pollutant-degrading bacteria by fungi. Environ. Sci. Technol. 39, 4640–4646 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Held, M., Kaspar, O., Edwards, C. & Nicolau, D. V. Intracellular mechanisms of fungal space searching in microenvironments. Proc. Natl Acad. Sci. USA 116, 13543–13552 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
47.Soufan, R. et al. Pore-scale monitoring of the effect of microarchitecture on fungal growth in a two-dimensional soil-like micromodel. Front. Environ. Sci. https://doi.org/10.3389/fenvs.2018.00068 (2018).48.Hanson, K. L. et al. Fungi use efficient algorithms for the exploration of microfluidic networks. Small 2, 1212–1220 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Pajor, R., Falconer, R., Hapca, S. & Otten, W. Modelling and quantifying the effect of heterogeneity in soil physical conditions on fungal growth. Biogeosciences 7, 3731–3740 (2010).Article 

Google Scholar 
50.Varma, A., Abbott, L., Werner, D. & Hampp, R. Plant Surface Microbiology (Springer, 2008)..51.Lew, R. R. How does a hypha grow? The biophysics of pressurized growth in fungi. Nat. Rev. Microbiol. 9, 509–518 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Tayagui, A., Sun, Y., Collings, D. A., Garrill, A. & Nock, V. An elastomeric micropillar platform for the study of protrusive forces in hyphal invasion. Lab a Chip 17, 3643–3653 (2017).CAS 
Article 

Google Scholar 
53.Bardgett, R. D. & McAlister, E. The measurement of soil fungal:bacterial biomass ratios as an indicator of ecosystem self-regulation in temperate meadow grasslands. Biol. Fertil. Soils 29, 282–290 (1999).Article 

Google Scholar 
54.De Deyn, G. B., Cornelissen, J. H. C. & Bardgett, R. D. Plant functional traits and soil carbon sequestration in contrasting biomes. Ecol. Lett. 11, 516–531 (2008).PubMed 
Article 

Google Scholar 
55.Kuijper, L. D. J., Berg, M. P., Morriën, E., Kooi, B. W. & Verhoef, H. A. Global change effects on a mechanistic decomposer food web model. Glob. Change Biol. 11, 249–265 (2005).Article 

Google Scholar 
56.Falconer, R. E. et al. Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation. PLoS ONE 10, e0123774 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
57.Deveau, A. et al. Bacterial-fungal interactions: ecology, mechanisms and challenges. FEMS Microbiol. Rev. 42, 335–352 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Postma, J. & van Veen, J.A. Habitable pore space and survival of Rhizobium leguminosarum biovar trifolii introduced into soil. Microb. Ecol. 19, 149–161 (1990).59.Grundmann, G. L. Spatial scales of soil bacterial diversity—the size of a clone. FEMS Microbiol. Ecol. 48, 119–127 (2004).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Kuzyakov, Y. & Blagodatskaya, E. Microbial hotspots and hot moments in soil: concept & review. Soil Biol. Biochem. 83, 184–199 (2015).CAS 
Article 

Google Scholar 
61.Kim, D. S. & Fogler, H. S. Biomass evolution in porous media and its effects on permeability under starvation conditions. Biotechnol. Bioeng. 69, 47–56 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Dupin, H. J. & McCarty, P. L. Mesoscale and microscale observations of biological growth in a silicon pore imaging element. Environ. Sci. Technol. 33, 1230–1236 (1999).CAS 
Article 

Google Scholar 
63.Aufrecht, J. A. et al. Pore-scale hydrodynamics influence the spatial evolution of bacterial biofilms in a microfluidic porous network. PLoS ONE 14, 1–17 (2019).Article 
CAS 

Google Scholar 
64.Vervoort, R. W. & Cattle, S. R. Linking hydraulic conductivity and tortuosity parameters to pore space geometry and pore-size distribution. J. Hydrol. 272, 36–49 (2003).Article 

Google Scholar 
65.Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol. Biochem. 34, 139–162 (2002).Article 

Google Scholar 
66.Hoffman, M. T. & Arnold, A. E. Diverse bacteria inhabit living hyphae of phylogenetically diverse fungal endophytes. Appl. Environ. Microbiol. 76, 4063–4075 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
68.Mcdonald, J. C., Duffy, D. C., Anderson, J. R. & Chiu, D. T. Review general fabrication of microfluidic systems in poly (dimethylsiloxane). Electrophoresis 21, 27–40 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
69.Cánovas, D., Cases, I. & De Lorenzo, V. Heavy metal tolerance and metal homeostasis in Pseudomonas putida as revealed by complete genome analysis. Environ. Microbiol. 5, 1242–1256 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Mooney, A., Ward, P. G. & O’Connor, K. E. Microbial degradation of styrene: biochemistry, molecular genetics, and perspectives for biotechnological applications. Appl. Microbiol. Biotechnol. 72, 1–10 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Ward, P. G., Goff, M., Donner, M., Kaminsky, W. & O’Connor, K. E. A two step chemo-biotechnological conversion of polystyrene to a biodegradable thermoplastic. Environ. Sci. Technol. 40, 2433–2437 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Gomes, N. C. M., Kosheleva, I. A., Abraham, W. R. & Smalla, K. Effects of the inoculant strain Pseudomonas putida KT2442 (pNF142) and of naphthalene contamination on the soil bacterial community. FEMS Microbiol. Ecol. 54, 21–33 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Smith, M. C. M. Molecular biological methods for bacillus. FEBS Lett. https://doi.org/10.1016/0014-5793(91)80059-c. (1991).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Razavi, B. S., Zhang, X., Bilyera, N., Guber, A. & Zarebanadkouki, M. Soil zymography: simple and reliable? Review of current knowledge and optimization of the method. Rhizosphere 11, 100161 (2019). no. June.Article 

Google Scholar 
75.Nicodème, M., Grill, J. P., Humbert, G. & Gaillard, J. L. Extracellular protease activity of different Pseudomonas strains: dependence of proteolytic activity on culture conditions. J. Appl. Microbiol. https://doi.org/10.1111/j.1365-2672.2005.02634.x (2005).76.Güll, I., Alves, P. M., Gabor, F. & Wirth, M. Viability of the human adenocarcinoma cell line Caco-2: influence of cryoprotectant, freezing rate, and storage temperature. Scientia Pharmaceutica https://doi.org/10.3797/scipharm.0810-07 (2009).77.Burns, C. et al. Efficient GFP expression in the mushrooms Agaricus bisporus and Coprinus cinereus requires introns. Fungal Genetics Biol. https://doi.org/10.1016/j.fgb.2004.11.005 (2005).78.Stajich, J. E. et al. Insights into evolution of multicellular fungi from the assembled chromosomes of the mushroom Coprinopsis cinerea (Coprinus cinereus). Proc. Natl Acad. Sci. USA 107, 11889–11894 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods. https://doi.org/10.1038/nmeth.2019 (2012).80.Kneen, M. A. & Annegarn, H. J. Algorithm for fitting XRF, SEM and PIXE X-ray spectra backgrounds. Nucl. Instrum. Methods Phys. Res. Section B https://doi.org/10.1016/0168-583X(95)00908-6 (1996).81.Team, R. C. R: a language and environment for statistical computing. Vienna, Austria, 2019.82.Dunn, O. J. Multiple comparisons among means. J. Am. Stat. Assoc. https://doi.org/10.2307/2282330 (1961).83.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 1979.84.C. et al. Arellano-Caicedo, “Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation 2nd part,” Dryad, Dataset. 2021.85.C. et al. Arellano-Caicedo, “Habitat geometry in artificial microstructure affects bacterial and fungal growth, interactions, and substrate degradation. Part 1,” Dryad, Dataset, 2021. More

1.Tscharntke, T., Klein, A. M., Kruess, A., Steffan-Dewenter, I. & Thies, C. Landscape perspectives on agricultural intensification and biodiversity–ecosystem service management. Ecol. Lett. 8, 857–874 (2005).Article 

Google Scholar 
2.Van Zanten, B. T. et al. European agricultural landscapes, common agricultural policy and ecosystem services: A review. Agron. Sustain. Dev. 34, 309–325 (2014).Article 

Google Scholar 
3.Jongman, R. H. Homogenisation and fragmentation of the European landscape: Ecological consequences and solutions. Landsc. Urban Plan. 58, 211–221 (2002).Article 

Google Scholar 
4.Stoate, C. et al. Ecological impacts of arable intensification in Europe. J. Environ. Manag. 63, 337–365 (2001).CAS 
Article 

Google Scholar 
5.Storkey, J., Meyer, S., Still, K. S. & Leuschner, C. The impact of agricultural intensification and land-use change on the European arable flora. Proc. R. Soc. B 279, 1421–1429 (2012).CAS 
PubMed 
Article 

Google Scholar 
6.Donald, P. F., Sanderson, F. J., Burfield, I. J. & Van Bommel, F. P. J. Further evidence of continent-wide impacts of agricultural intensification on European farmland birds, 1990–2000. Agric. Ecosyst. Environ. 116, 189–196 (2006).Article 

Google Scholar 
7.Benton, T. G., Vickery, J. A. & Wilson, J. D. Farmland biodiversity: Is habitat heterogeneity the key?. Trends Ecol. Evol. 18, 182–188 (2003).Article 

Google Scholar 
8.Traba, J. & Morales, M. B. The decline of farmland birds in Spain is strongly associated to the loss of fallowland. Sci. Rep. 9, 1–6 (2019).Article 
CAS 

Google Scholar 
9.Mcmahon, B. J., Giralt, D., Raurell, M., Brotons, L. & Bota, G. Identifying set-aside features for bird conservation and management in northeast Iberian pseudo-steppes. Bird Study 57, 289–300 (2010).Article 

Google Scholar 
10.Tarjuelo, R. et al. Living in seasonally dynamic farmland: The role of natural and semi-natural habitats in the movements and habitat selection of a declining bird. Biol. Conserv. 251, 108794 (2020).Article 

Google Scholar 
11.Donázar, J. A., Naveso, M. A., Tella, J. L. & Campión, D. Extensive grazing and raptors in Spain. 117–149. in Farming and Birds in Europe: The Common Agricultural Policy and Its Implications for Bird Conservation (Pain, D. J. & Pienkowski, M. W. eds.). (Academic Press, 1997).12.Santos, T. & Suárez, F. Biogeography and population trends of the Iberian steppe birds. in Ecology and Conservation of Steppe-Land Birds (Bota, G., Morales, M. B., Mañosa, S. & Camprodon, J. eds.). (Lynx Edicions, 2005).13.Tarjuelo, R., Margalida, A. & Mougeot, F. Changing the fallow paradigm: A win–win strategy for the post-2020 Common Agricultural Policy to halt farmland bird declines. J. Appl. Ecol. 57, 642–649 (2020).Article 

Google Scholar 
14.Wilson, J. D., Morris, A. J., Arroyo, B. E., Clark, S. C. & Bradbury, R. B. A review of the abundance and diversity of invertebrate and plant foods of granivorous birds in northern Europe in relation to agricultural change. Agric. Ecosyst. Environ. 75, 13–30 (1999).Article 

Google Scholar 
15.Benton, T. G., Bryant, D. M., Cole, L. & Crick, H. Q. Linking agricultural practice to insect and bird populations: A historical study over three decades. J. Appl. Ecol. 39, 673–687 (2002).Article 

Google Scholar 
16.Raven, P. H. & Wagner, D. L. Agricultural intensification and climate change are rapidly decreasing insect biodiversity. Proc. Natl. Acad. Sci. U.S.A. 118, 2 (2021).Article 
CAS 

Google Scholar 
17.Andreasen, C., Jensen, H. A. & Jensen, S. M. Decreasing diversity in the soil seed bank after 50 years in Danish arable fields. Agric. Ecosyst. Environ. 259, 61–71 (2018).Article 

Google Scholar 
18.Newton, I. The recent declines of farmland bird populations in Britain: An appraisal of causal factors and conservation actions. Ibis 146, 579–600 (2004).Article 

Google Scholar 
19.Burfield, I. J. The conservation status of steppic birds in Europe. 119–140. in Ecology and Conservation of Steppe-Land Birds (Bota, G., Morales, M. B., Mañosa, S. & Camprodon, J. eds.). (Lynx Edicions, 2005).20.Del Hoyo, J., Elliott, A., Sargatal, J. & Christie D. A. Handbook of the Birds of the World. (Lynx Edicions, 1992).21.Madroño, A., González, C. & Atienza, J. C. Libro Rojo de las Aves de España. (Dirección General para la Biodiversidad-SEO/BirdLife, 2004)22.Suárez, F., Hervás, I., Levassor, C. & Casado, M. A. La alimentación de la ganga ibérica y la ganga ortega. 215–229. in La Ganga Iberica (Pterocles alchata) y la Ganga Ortega (Pterocles orientalis) en España. Distribución, Abundancia, Biología y Conservación (Herranz, J. & Suárez, F. eds.). (Ministerio de Medio Ambiente, 1999).23.Jiguet, F. Arthropods in diet of Little Bustards Tetrax tetrax during the breeding season in western France. Bird Study 49, 105–109 (2002).Article 

Google Scholar 
24.Bravo, C., Ponce, C., Palacín, C. & Alonso, J. C. Diet of young Great Bustards Otis tarda in Spain: Sexual and seasonal differences. Bird Study 59, 243–251 (2012).Article 

Google Scholar 
25.Pompanon, F. et al. Who is eating what: Diet assessment using next generation sequencing. Mol. Ecol. 21, 1931–1950 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Shokralla, S., Spall, J. L., Gibson, J. F. & Hajibabaei, M. Next-generation sequencing technologies for environmental DNA research. Mol. Ecol. 21, 1794–1805 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Mougeot, F., Fernández-Tizón, M., Tarjuelo, R., Benítez-López, A. & Jiménez, J. La Ganga Ibérica y la Ganga Ortega en España, Población Reproductora en 2019 y Método de Censo. (SEO/BirdLife, 2021).28.Martin, T. E. Food as a limit on breeding birds: A life-history perspective. Annu. Rev. Ecol. Evol. Syst. 18, 453–487 (1987).Article 

Google Scholar 
29.Martín, C. A., Casas, F., Mougeot, F., García, J. T. & Viñuela, J. Positive interactions between vulnerable species in agrarian pseudo-steppes: Habitat use by pin-tailed sandgrouse depends on its association with the little bustard. Anim. Conserv. 13, 383–389 (2010).Article 

Google Scholar 
30.Bravo, C., Cuscó, F., Morales, M. & Mañosa, S. Diet composition of a declining steppe bird the Little Bustard (Tetrax tetrax) in relation to farming practices. Avian Conserv. Ecol. 12, 1 (2017).CAS 

Google Scholar 
31.Morse, J. G. & Hoddle, M. S. Invasion biology of thrips. Annu. Rev. Entomol. 51, 67–89 (2006).CAS 
PubMed 
Article 

Google Scholar 
32.Goldarazena, A. Orden Thysanoptera. Ide@-Sea 52, 1–20 (2015).
Google Scholar 
33.Ndang’ang’a, P. K., Njoroge, J. B. & Vickery, J. Quantifying the contribution of birds to the control of arthropod pests on kale, Brassica oleracea acephala, a key crop in East African highland farmland. Int. J. Pest Manag. 59, 211–216 (2013).Article 

Google Scholar 
34.Gunnarsson, B. Bird predation on spiders: Ecological mechanisms and evolutionary consequences. J. Arachnol. 35(509), 529 (2007).
Google Scholar 
35.Lee, J. H. et al. Anticancer activity of the antimicrobial peptide scolopendrasin VII derived from the centipede, Scolopendra subspinipes mutilans. J. Microbiol. Biotechnol. 25, 1275–1280 (2015).CAS 
PubMed 
Article 

Google Scholar 
36.Lima, D. B. et al. Antiparasitic effect of Dinoponera quadriceps giant ant venom. Toxicon 120, 128–132 (2016).CAS 
PubMed 
Article 

Google Scholar 
37.Whitman, D. W. et al. Antiparasitic properties of cantharidin and the blister beetle berberomeloe majalis (Coleoptera: meloidae). Toxins 11, 234 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
38.Bravo, C., Bautista, L. M., García-París, M., Blanco, G. & Alonso, J. C. Males of a strongly polygynous species consume more poisonous food than females. PLoS ONE 9, e111057 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
39.Bolívar, P. et al. Antiparasitic effects of plant species from the diet of great bustards. Preprint. https://doi.org/10.21203/rs.3.rs-122399/v1 (2020).Article 

Google Scholar 
40.Boyer, A. G. et al. Seasonal variation in top-down and bottom-up processes in a grassland arthropod community. Oecologia 136, 309–316 (2003).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Palacios, F., Garzón, J. & Castroviejo, J. L. alimentación de la avutarda (Otis tarda) en España, especialmente en primavera. Ardeola 21, 347–406 (1975).
Google Scholar 
42.Cabodevilla, X., Gómez-Moliner, B. J. & Madeira, M. J. Simultaneous analysis of the intestinal parasites and diet through eDNA metabarcoding. Preprint. https://doi.org/10.22541/au.158531783.33894277 (2020).Article 

Google Scholar 
43.García de la Morena, E. L., Bota, G., Mañosa, S. & Morales, M. B. El Sisón Común en España. II Censo Nacional (2016). (SEO/BirdLife, 2018).44.Cabodevilla, X., Aebischer, N. J., Mougeot, F., Morales, M. B. & Arroyo, B. Are population changes of endangered little bustards associated with releases of red-legged partridges for hunting? A large-scale study from central Spain. Eur. J. Wildl. Res. 66, 1–10 (2020).Article 

Google Scholar 
45.Cuscó, F., Cardador, L., Bota, G., Morales, M. B. & Mañosa, S. Inter-individual consistency in habitat selection patterns and spatial range constraints of female little bustards during the non-breeding season. BMC Ecol. 18, 1–12 (2018).Article 

Google Scholar 
46.González del Portillo, D., Arroyo, B., García Simón, G. & Morales, M. B. Can current farmland landscapes feed declining steppe birds? Evaluating arthropod abundance for the endangered little bustard (Tetrax tetrax) in cereal farmland during the chick‐rearing period: Variations between habitats and localities. Ecol. Evol. 11, 3219–3238 (2021).
47.Silva, J. P., Pinto, M. & Palmeirim, J. M. Managing landscapes for the little bustard Tetrax tetrax: Lessons from the study of winter habitat selection. Biol. Conserv. 117, 521–528 (2004).Article 

Google Scholar 
48.Pfiffner, L. & Luka, H. Overwintering of arthropods in soils of arable fields and adjacent semi-natural habitats. Agric. Ecosyst. Environ. 78, 215–222 (2000).Article 

Google Scholar 
49.Hendrickx, F. et al. How landscape structure, land-use intensity and habitat diversity affect components of total arthropod diversity in agricultural landscapes. J. Appl. Ecol. 44, 340–351 (2007).Article 

Google Scholar 
50.Tarjuelo, R., Morales, M. B., Arribas, L. & Traba, J. Abundance of weeds and seeds but not of arthropods differs between arable habitats in an extensive Mediterranean farming system. Ecol. Res. 34, 624–636 (2019).Article 

Google Scholar 
51.Green, R. E. The feeding ecology and survival of partridge chicks (Alectoris rufa and Perdix perdix) on arable farmland in East Anglia. J. Appl. Ecol. 1, 817–830 (1984).Article 

Google Scholar 
52.Palacín, C. La decadencia de la comunidad de aves de los cultivos cerealistas mediterráneos. in XV Congreso del Grupo Ibérico de Aguiluchos. https://xvcongresoaguiluchosgia.es/wp-content/uploads/2019/11/LA-DECADENCIA-DE-LA-COMUNIDAD-DE-AVES-DE-LOS-CULTIVOS-CEREALISTAS-MEDITERRÁNEOS-Carlos-Palac%C3%ADn.pdf (2019).53.Blanco-Aguiar, J. A., Virgós, E. & Villafuerte, R. Perdiz roja (Alectoris rufa). in Atlas de las Aves Reproductoras de España. 212–213 (2003).54.Rodríguez-Teijeiro, J. D., Puigcerver, M. & Gallego, S. Codorniz común. in Atlas de las Aves Reproductoras de España. 218–219 (2003).55.Andueza, A. et al. Evaluación del Impacto Económico y Social de la Caza en España. (Fundación Artemisan, 2018)56.Lane, S. J., Alonso, J. C., Alonso, J. A. & Naveso, M. A. Seasonal changes in diet and diet selection of great bustards (Otis tarda) in north-west Spain. J. Zool. 247, 201–214 (1999).Article 

Google Scholar 
57.QGIS Development Team. QGIS Geographic Information System. http://qgis.osgeo.org (Open Source Geospatial Foundation Project, 2018).58.Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).Article 

Google Scholar 
59.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
Article 

Google Scholar 
60.Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic. Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.McKnight, D. T. et al. Methods for normalizing microbiome data: An ecological perspective. Methods Ecol. Evol. 10, 389–400 (2019).Article 

Google Scholar 
62.Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28, 420–430 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
63.Piñol, J., Senar, M. A. & Symondson, W. O. The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol. Ecol. 28, 407–419 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
64.Russo, T. et al. All is fish that comes to the net: metabarcoding for rapid fisheries catch assessment. Ecol. Appl. 31, e02273 (2021).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
65.González-Teuber, M., Vilo, C., Guevara-Araya, M. J., Salgado-Luarte, C. & Gianoli, E. Leaf resistance traits influence endophytic fungi colonization and community composition in a South American temperate rainforest. J. Ecol. 108, 1019–1029 (2020).Article 
CAS 

Google Scholar 
66.Aliche, E. B., Talsma, W., Munnik, T. & Bouwmeester, H. J. Characterization of maize root microbiome in two different soils by minimizing plant DNA contamination in metabarcoding analysis. Biol. Fertil. Soils. 57, 731–737 (2021).CAS 
Article 

Google Scholar 
67.de Groot, G. A. et al. The aerobiome uncovered: Multi-marker metabarcoding reveals potential drivers of turn-over in the full microbial community in the air. Environ. Int. 154, 106551 (2021).PubMed 
Article 
PubMed Central 

Google Scholar 
68.Tordoni, E. et al. Integrated eDNA metabarcoding and morphological analyses assess spatio-temporal patterns of airborne fungal spores. Ecol. Indic. 121, 107032 (2021).Article 

Google Scholar 
69.R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/. (R Foundation for Statistical Computing, 2019).70.Russell, V. L. Least-squares means: The R Package lsmeans. J. Stat. Softw. 69, 1–33 (2016).
Google Scholar 
71.Oksanen, J. et al. Vegan: Community Ecology Package. R Package Version 2.0 (2013). More

1.Mitchell, M. S. & Powell, R. A. A mechanistic home range model for optimal use of spatially distributed resources. Ecol. Model. 177, 209–232 (2004).Article 

Google Scholar 
2.Horne, J. S., Garton, E. O. & Rachlow, J. L. A synoptic model of animal space use: Simultaneous estimation of home range, habitat selection, and inter/intra–specific relationships. Ecol. Model. 214, 338–348 (2008).Article 

Google Scholar 
3.Nathan, R. An emerging movement ecology paradigm. Proc. Natl. Acad. Sci. USA 105, 19050–19051 (2008).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Fretwell, S. D. & Lucas, H. L. J. On territorial behavior and other factors influencing habitat distribution in birds. Part 1. Theoretical development. Acta. Biotheor. 19, 16–36 (1969).Article 

Google Scholar 
5.Powell, R. A. Animal home ranges and territories and home range estimators. Res. Tech. Animal Ecol. Controversies Consequences. 1, 476 (2000).
Google Scholar 
6.Parker, G. A. & Smith, J. M. Optimality theory in evolutionary biology. Nature 348, 27 (1990).ADS 
Article 

Google Scholar 
7.Hiller, T. L., Belant, J. L. & Beringer, J. Sexual size dimorphism mediates effects of spatial resource variability on American black bear space use. J. Zool. 296, 200–207 (2015).Article 

Google Scholar 
8.Mitchell, M. S. & Powell, R. A. Optimal use of resources structures home ranges and spatial distribution of black bears. Anim. Behav. 74, 219–230 (2007).Article 

Google Scholar 
9.McLoughlin, P. D. & Ferguson, S. H. A hierarchical pattern of limiting factors helps explain variation in home range size. Ecoscience 7, 123–130 (2000).Article 

Google Scholar 
10.Johnson, D. D., Kays, R., Blackwell, P. G. & Macdonald, D. W. Does the resource dispersion hypothesis explain group living?. Trends Ecol. Evol. 17, 563–570 (2002).Article 

Google Scholar 
11.Macdonald, D. W. The ecology of carnivore social behavior. Nature 301, 379–384 (1983).ADS 
Article 

Google Scholar 
12.Macdonald, D. W. & Johnson, D. D. P. Patchwork planet: The resource dispersion hypothesis, society, and the ecology of life. J. Zool. 295, 75–107 (2015).Article 

Google Scholar 
13.Lukacs, P. M. et al. Factors influencing elk recruitment across ecotypes in the Western United States. J. Wildl. Manag. 82, 698–710 (2018).Article 

Google Scholar 
14.Mangipane, L. S. et al. Influences of landscape heterogeneity on home-range sizes of brown bears. Mamm. Biol. 88, 1–7 (2018).Article 

Google Scholar 
15.McClintic, L. F., Taylor, J. D., Jones, J. C., Singleton, R. D. & Wang, G. Effects of spatiotemporal resource heterogeneity on home range size of American beaver. J. Zool. 293, 134–141 (2014).Article 

Google Scholar 
16.Harestad, A. S. & Bunnel, F. L. Home range and body weight—A reevaluation. Ecology 60, 389–402 (1979).Article 

Google Scholar 
17.Knick, S. T. Ecology of bobcats relative to exploitation and a prey decline in southeastern Idaho. Wildl. Monogr. 108, 3–42 (1990).
Google Scholar 
18.Kelt, D. A. & Van Vuren, D. H. The ecology and macroecology of mammalian home range area. Am. Nat. 157, 637–645 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.McNab, B. K. Bioenergetics and the determination of home range size. Am. Nat. 97, 133–140 (1963).Article 

Google Scholar 
20.Dahle, B., Støen, O. G. & Swenson, J. E. Factors influencing home-range size in subadult brown bears. J. Mammal. 87, 859–865 (2006).Article 

Google Scholar 
21.Dahle, B. & Swenson, J. E. Home ranges in adult Scandinavian brown bears (Ursus arctos): Effect of mass, sex, reproductive category, population density and habitat type. J. Zool. 260, 329–335 (2003).Article 

Google Scholar 
22.Lafferty, D. J. R., Loman, Z. G., White, K. S., Morzillo, A. T. & Belant, J. L. Moose (Alces alces) hunters subsidize the scavenger community in Alaska. Polar Biol. 39, 639–647 (2016).Article 

Google Scholar 
23.Van Manen, F. T. et al. Primarily resident grizzly bears respond to late-season elk harvest. Ursus 2019, 1–15 (2019).Article 

Google Scholar 
24.Taylor, M.K. Density-dependent population regulation of black, brown and polar bears. in 9th International Conference on Bear Research and Management. International Bear Association, Missoula (1994).25.Swenson, J. E., Dahle, B. & Sandegren, F. Intraspecific predation in Scandinavian brown bears older than cubs-of-the-year. Ursus 12, 81–91 (2001).
Google Scholar 
26.Hilderbrand, G. V. et al. Body size and lean mass of brown bears across and within four diverse ecosystems. J. Zool. 305, 53–62 (2018).Article 

Google Scholar 
27.Hilderbrand, G. V. et al. The importance of meat, particularly salmon, to body size, population productivity, and conservation of North American brown bears. Can. J. Zool. 77, 132–138 (1999).Article 

Google Scholar 
28.Belant, J. L., Kielland, K., Follmann, E. H. & Adams, L. G. Interspecific resource partitioning in sympatric ursids. Ecol. Appl. 16, 2333–2343 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
29.Belant, J. L., Griffith, B., Zhang, Y., Follmann, E. H. & Adams, L. G. Population-level resource selection by sympatric brown and American black bears in Alaska. Polar Biol. 33, 31–40 (2010).Article 

Google Scholar 
30.Munro, R. H. M., Nielsen, S. E., Price, M. H., Stenhouse, G. B. & Boyce, M. S. Seasonal and diel patterns of grizzly bear diet and activity in west-central Alberta. J. Mammal. 87, 1112–1121 (2006).Article 

Google Scholar 
31.US Fish and Wildlife Service. Izembek National Wildlife Refuge Land Exchange/Road Corridor, Draft Environmental Impact Statement (US Fish and Wildlife Service, 2018).
Google Scholar 
32.Svoboda, N. J. & Crye, J. R. Roosevelt Elk Management Report and Plan, Game Management Unit 8: Report Period 1 July 2013–30 June 2018, and Plan Period 1 July 2018–30 June 2023 (Alaska Department of Fish and Game, 2020).
Google Scholar 
33.Van Daele, M. B. et al. Salmon consumption by Kodiak brown bears (Ursus arctos middendorffi) with ecosystem management implications. Can. J. Zool. 91, 164–174 (2013).Article 

Google Scholar 
34.Barnes, V. The influence of salmon availability on movements and range of brown bears on Southwest Kodiak Island. Bears Biol. Manag. 8, 305–313 (1990).
Google Scholar 
35.Deacy, W., Leacock, W., Armstrong, J. B. & Stanford, J. A. Kodiak brown bears surf the salmon red wave: Direct evidence from GPS collared individuals. Ecology 97, 1091–1098 (2016).PubMed 
Article 

Google Scholar 
36.Van Daele, L. J., Barnes, V. G. & Belant, J. L. Ecological flexibility of brown bears on Kodiak Island, Alaska. Ursus 23, 21–29 (2012).Article 

Google Scholar 
37.Stirling, I., Spencer, C. & Andriashek, D. Immobilization of polar bears (Ursus maritimus) with Telazol® in the Canadian Arctic. J. Wildl. Dis. 25, 159–168 (1989).CAS 
PubMed 
Article 

Google Scholar 
38.Woolf, A., Hays, H. R., Allen, W. B. & Swart, J. Immobilization of wild ungulates with etorphine HC1. J. Zoo Animal Med. 4, 16–19 (1973).Article 

Google Scholar 
39.Meuleman, T., Port, J. D., Stanley, T. H., Williard, K. F. & Kimball, J. Immobilization of elk and moose with carfentanil. J. Wildl. Manag. 48, 258–262 (1984).Article 

Google Scholar 
40.Lance, W.R. & Kenny, D.E. Thiafentanil oxalate (A3080) in nondomestic ungulate species. in Fowler’s Zoo and Wild Animal Medicine (ed. Miller and Fowler) 589–595 (W.B. Saunders, 2012).41.Garshelis, D. L. & McLaughlin, C. R. Review and evaluation of breakaway devices for bear radiocollars. Ursus 10, 459–465 (1998).
Google Scholar 
42.Calvert, W. & Ramsay, M. A. Evaluation of age determination of polar bears by counts of cementum growth layer groups. Ursus 10, 449–453 (1998).
Google Scholar 
43.Thiemann, G. W. et al. Effects of chemical immobilization on the movement rates of free-ranging polar bears. J. Mammal. 94, 386–397 (2013).Article 

Google Scholar 
44.Noonan, M. J. et al. A comprehensive analysis of autocorrelation and bias in home range estimation. Ecol. Monogr. 89, e01344 (2019).Article 

Google Scholar 
45.Bishop, A., Brown, C., Rehberg, M., Torres, L. & Horning, M. Juvenile Steller sea lion (Eumetopias jubatus) utilization distributions in the Gulf of Alaska. Mov. Ecol. 6, 1–15 (2018).Article 

Google Scholar 
46.Long, R. A., Muir, J. D., Rachlow, J. L. & Kie, J. G. A comparison of two modeling approaches for evaluating wildlife-habitat relationships. J. Wildl. Manag. 73, 294–302 (2009).Article 

Google Scholar 
47.Fleming, M. D. & Spencer, P. A vegetative cover map for the Kodiak Archipelago Alaska (USGS, Alaska Science Center, Anchorage, 2004).
Google Scholar 
48.Brodeur, V., Ouellet, J. P., Courtois, R. & Fortin, D. Habitat selection by black bears in an intensively logged boreal forest. Can. J. Zool. 86, 1307–1316 (2008).Article 

Google Scholar 
49.Hesselbarth, M. H., Sciaini, M., With, K. A., Wiegand, K. & Nowosad, J. landscapemetrics: An open-source R tool to calculate landscape metrics. Ecography 42, 1648–1657 (2019).Article 

Google Scholar 
50.Shannon, C. E. & Weaver, W. The Mathematical Theory of Communication (University of Illinois Press, 1963).MATH 

Google Scholar 
51.Smith, T. S. & Partridge, S. T. Dynamics of intertidal foraging by coastal brown bears in southwestern Alaska. J. Wildl. Manag. 68, 233–240 (2004).Article 

Google Scholar 
52.Zager, P. & Beecham, J. The role of American black bears and brown bears as predators on ungulates in North America. Ursus 17, 95–108 (2006).Article 

Google Scholar 
53.Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).Article 

Google Scholar 
54.Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).Article 

Google Scholar 
55.Morris, L. R., Proffitt, K. M., Asher, V. & Blackburn, J. K. Elk resource selection and implications for anthrax management in Montana. J. Wildl. Manag. 80, 235–244 (2016).Article 

Google Scholar 
56.Pontius, R. G. & Parmentier, B. Recommendations for using the relative operating characteristic (ROC). Landsc. Ecol. 29, 367–382 (2014).Article 

Google Scholar 
57.Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).Article 

Google Scholar 
58.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information–Theoretic Approach 2nd edn. (Springer, 2002).MATH 

Google Scholar 
59.R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.60.Lewis, T. M. & Lafferty, D. J. Brown bears and wolves scavenge humpback whale carcass in Alaska. Ursus 25, 8–13 (2014).Article 

Google Scholar 
61.Paralikidis, N. P., Papageorgiou, N. K., Kontsiotis, V. J. & Tsiompanoudis, A. C. The dietary habits of the brown bear (Ursus arctos) in western Greece. Mamm. Biol. 75, 29–35 (2010).Article 

Google Scholar 
62.Sandell, M. The mating tactics and spacing patterns of solitary carnivores In Carnivore Behavior, Ecology, and Evolution (ed. Gittleman J.L.) 164–182 (Springer, 1989).63.Hilderbrand, G. V., Jenkins, S. G., Schwartz, C. C., Hanley, T. A. & Robbins, C. T. Effect of seasonal differences in dietary meat intake on changes in body mass and composition in wild and captive brown bears. Can. J. Zool. 77, 1623–1630 (1999).Article 

Google Scholar 
64.Milakovic, B. & Parker, K. L. Quantifying carnivory by grizzly bears in a multi-ungulate system. J. Wildl. Manag. 77, 39–47 (2013).Article 

Google Scholar 
65.Nieminen, M. The impact of large carnivores on the mortality of semi-domesticated reindeer (Rangifer tarandus tarandus L.) calves in Kainuu, southeastern reindeer herding region of Finland. Rangifer. 30, 79–88 (2010).Article 

Google Scholar 
66.Mumma, M. A. et al. Intrinsic traits of woodland caribou Rangifer tarandus caribou calves depredated by black bears Ursus americanus and coyotes Canis latrans. Wildl. Biol. 2019, 1–9 (2019).Article 

Google Scholar 
67.Svoboda, N. J., Belant, J. L., Beyer, D. E., Duquette, J. F. & Lederle, P. E. Carnivore space use shifts in response to seasonal resource availability. Ecosphere. 10, e02817 (2019).Article 

Google Scholar 
68.Ruth, T. K. et al. Large-carnivore response to recreational big-game hunting along the Yellowstone National Park and Absaroka-Beartooth Wilderness boundary. Wildl. Soc. Bull. 31, 1150–1161 (2003).
Google Scholar 
69.Haroldson, M. A., Schwartz, C. C., Cherry, S. & Moody, D. S. Possible effects of elk harvest on fall distribution of grizzly bears in the Greater Yellowstone Ecosystem. J. Wildl. Manag. 68, 129–137 (2004).Article 

Google Scholar 
70.Bastille-Rousseau, G., Fortin, D., Dussault, C., Courtois, R. & Ouellet, J. P. Foraging strategies by omnivores: are black bears actively searching for ungulate neonates or are they simply opportunistic predators?. Ecography 34, 588–596 (2011).Article 

Google Scholar 
71.Gehr, B. et al. Evidence for nonconsumptive effects from a large predator in an ungulate prey?. Behav. Ecol. 29, 724–735 (2018).Article 

Google Scholar 
72.Hebblewhite, M., Merrill, E. H. & McDonald, T. L. Spatial decomposition of predation risk using resource selection functions: An example in a wolf–elk predator–prey system. Oikos 111, 101–111 (2005).Article 

Google Scholar 
73.Nielsen, S. E., Boyce, M. S. & Stenhouse, G. B. Grizzly bears and forestry: I. Selection of clearcuts by grizzly bears in west-central Alberta, Canada. For. Ecol. Manag. 199, 51–65 (2004).Article 

Google Scholar 
74.McLellan, B. N. Relationships between human industrial activity and grizzly bears. Bears Biol. Manag. 8, 57–64 (1990).
Google Scholar 
75.Sigman, M. Impacts of Clearcut Logging on the Fish and Wildlife Resources of Southeast Alaska Vol. 85 (Alaska Department of Fish and Game, 1985).
Google Scholar 
76.Linnell, J. D., Swenson, J. E., Andersen, R. & Barnes, B. How vulnerable are denning bears to disturbance?. Wildl. Soc. Bull. 28, 400–413 (2000).
Google Scholar 
77.McLellan, B. N. & Shackleton, D. M. Grizzly bears and resource-extraction industries: Effects of roads on behaviour, habitat use and demography. J. Appl. Ecol. 25, 451–460 (1988).Article 

Google Scholar 
78.Nielsen, S. E., Munro, R. H. M., Bainbridge, E. L., Stenhouse, G. B. & Boyce, M. S. Grizzly bears and forestry: II. Distribution of grizzly bear foods in clearcuts of west-central Alberta, Canada. For. Ecol. Manag. 199, 67–82 (2004).Article 

Google Scholar 
79.Nielsen, S. E., Stenhouse, G. B., Beyer, H. L., Huettmann, F. & Boyce, M. S. Can natural disturbance-based forestry rescue a declining population of grizzly bears?. Biol. Cons. 141, 2193–2207 (2008).Article 

Google Scholar 
80.Frank, S. C. et al. A “clearcut” case? Brown bear selection of coarse woody debris and carpenter ants on clearcuts. For. Ecol. Manage. 348, 164–173 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
81.Hertel, A. G. et al. Bears and berries: Species-specific selective foraging on a patchily distributed food resource in a human-altered landscape. Behav. Ecol. Sociobiol. 70, 831–842 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
82.Valeix, M., Loveridge, A. J. & Macdonald, D. W. Influence of prey dispersion on territory and group size of African lions: A test of the resource dispersion hypothesis. Ecology 93, 2490–2496 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
83.Robbins, C. T. et al. Optimizing protein intake as a foraging strategy to maximize mass gain in an omnivore. Oikos 116, 1675–1682 (2007).Article 

Google Scholar 
84.Ben-David, M., Titus, K. & Beier, L. R. Consumption of salmon by Alaskan brown bears: A trade-off between nutritional requirements and the risk of infanticide?. Oecologia 138, 465–474 (2004).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
85.Smith, T. R. & Pelton, M. R. Home ranges and movements of black bears in bottomland hardwood forest in Arkansas. Int. Conf. Bear Res. Manag. 8, 213–218 (1990).
Google Scholar 
86.Welch, C. A., Keay, J., Kendall, K. C. & Robbins, C. T. Constraints on frugivory by bears. Ecology 78, 1105–1119 (1997).Article 

Google Scholar 
87.Gantchoff, M., Wang, G., Beyer, D. & Belant, J. Scale-dependent home range optimality for a solitary omnivore. Ecol. Evol. 8, 12271–12282 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
88.Tao, Y., Börger, L. & Hastings, A. Dynamic range size analysis of territorial animals: An optimality approach. Am. Nat. 188, 460–474 (2016).PubMed 
Article 
PubMed Central 

Google Scholar  More

More detailed methods can be found in the supplementary material. Data from this experiment on the characterisation of the microbial community and its response to climate change has been previously published in Scanes et al.12, therefore, the present study focussed on the interaction of metabolic processes with the microbiome. We examined the links between climate change, metabolism, genotype and microbiome of the Sydney rock oyster, Saccostrea glomerata20. Nine oyster aquacultural breeding lineages (labelled as genotype-lines A–I) of S. glomerata, which are known to differ in their resilience to climate change12 were exposed to ambient and elevated temperature and PCO2 treatments. All seawater used in acclimation and experimental exposure was collected from Little Beach, Port Stephens (152°9′30.00″E, 32°42′43.03″S), filtered through canister filters to a nominal 5 µm, and stored onsite in 38,000 L polyethylene tanks as a stock of filtered seawater.Approximately 72 individual S. glomerata, from each of the nine families (A-I) were collected from intertidal leases in Cromarty Bay, Port Stephens (152° 4′0.69″E, 32°43′19.69″S). Oysters were held on private leases so a collection permit was not required. Oysters were collected in September 2019 for experiments, meaning all oysters were 22 months old when experiments began. Oysters were placed into a 2000 L fibreglass tank and maintained at 24 °C, a salinity of 35 ppt and ambient PCO2 (pH 8.18) for two weeks to acclimate to laboratory conditions. Following acclimation, oysters from each genotype-line were divided among twelve 750 L polyethylene tanks filled with 400 L of filtered seawater (5 µm) at a density of 54 oysters per tank, with each genotype-line represented by six replicate individuals. Treatments consisted of orthogonal combinations of two PCO2 concentrations (ambient [400 µatm]; elevated [1000 µatm]) and two temperature treatments (24 and 28 °C). Each combination was replicated across three tanks. Treatments were selected to represent temperatures and PCO2 concentrations predicted for 2080–2100 by the IPCC27 and reflect measured changes in estuary temperatures reported from south eastern Australia20.Once oysters were transferred to experimental tanks, the PCO2 level and temperature were steadily increased in elevated exposure tanks over one week until the experimental treatment level was reached. The elevated CO2 level was maintained using a pH negative feedback system (Aqua Medic, Aqacenta Pty Ltd, Kingsgrove, NSW, Australia; accuracy ± 0.01 pH units) bubbling food grade CO2 (BOC Australia) through a mixing chamber and into each tank, previously described in18. These PCO2 levels corresponded to a mean ambient pHNBS of (8.18 ± 0.01) and at elevated CO2 levels a mean pHNBS of (7.84 ± 0.01). Temperature was increased and then maintained using 1000 W aquarium heaters in each tank. Oysters were then exposed to their respective treatments for a further four weeks. Oysters were checked daily for mortality; no dead oysters were found in any tanks during the four-week exposure period.Haemolymph sampling for DNA extractionFollowing exposure to experimental conditions, haemolymph was taken from two replicate oysters, from each genotype-line, from each tank for microbial analysis following the methods previously described in Scanes et al.,12. This amounted to six individuals from each genotype-line, in each treatment. Each oyster was opened using an autoclave sterilised shucking knife, ensuring that the pericardial cavity was not ruptured. Excess fluid was tipped off the tissue surface and 200–300 µL of haemolymph was extracted from the pericardial cavity using a new sterile 1 mL needled syringe (Terumo Co.). Samples from two oysters were transferred to two new pre-labelled DNA/RNA free 1 mL tubes (Eppendorf Co.) and immediately frozen at − 80 °C where they were stored until DNA extraction.We used 16 s rRNA amplicon sequencing to characterise the bacterial microbiome of S. glomerata haemolymph following the methods previously described in Scanes et al.12. DNA was extracted from 216 oyster haemolymph samples (9 genotype-lines × 4 treatments × 3 replicate tanks × 2 replicate oysters per tank) using the Qiagen DNeasy Blood and Tissue Kit (Qiagen Australia, Chadstone, VIC), according to the manufacturer’s instructions. The bacterial microbiome of the oyster haemolymph was characterised with 16S rRNA amplicon sequencing, using the 341F (CCTACGGGNGGCWGCAG) and 805R (GACTACHVGGGTATCTAATCC) primer pair28 targeting the V3-V4 variable regions of the 16S rRNA gene with the following cycling conditions: 95 °C for 3 min, 25 cycles of 95 °C for 30 s, 55 °C for 30 s and 72 °C for 30 s, and a final extension at 72 °C for 5 min. Amplicons were sequenced on the Illumina Miseq platform (2 × 300 bp) following the manufacturer’s guidelines at the Ramaciotti Centre for Genomics, University of New South Wales. Raw data files in FASTQ format were deposited in NCBI Sequence Read Archive (SRA) under Bioproject number PRJNA663356.Sequence analysisRaw demultiplexed data was processed using the Quantitative Insights into Microbial Ecology (QIIME 2 version 2019.1.0) pipeline. Briefly, paired-end sequences were imported (qiime tools import), trimmed and denoised using DADA2 (version 2019.1.0). Sequences were identified at the single nucleotide threshold (Amplicon Sequence Variants; ASV) and taxonomy was assigned using the classify-sklearn QIIME 2 feature classifier against the Silva v138 database29. Sequences identified as chloroplasts or mitochondria were also removed. Cleaned data were then rarefied at 6,500 counts per sample.Physiological analysisWe measured physiological variables relating to oyster haemolymph metabolic function. These were: extracellular pH (pHe), extracellular CO2 concentrations (PCO2e) and the whole oyster metabolic rate (MR) measured as a standardised rate of oxygen consumption. Physiological measurements were taken from two oysters from each genotype-line in each tank (methods followed that of Parker et al.16,30 and Scanes et al.18). Oysters were immediately opened without rupturing the pericardial cavity. Haemolymph samples were drawn from the interstitial fluid filling the pericardial cavity chamber of an opened oyster using a sealed 1 mL needled syringe. A 0.2 mL sample was drawn carefully to avoid aeration of the haemolymph. Half of the sample was then immediately transferred to an Eppendorf tube where pHe of the sample was measured at 20 °C using a micro pH probe (Metrohm 827 biotrode). The remaining haemolymph was transferred to a gas analyser (CIBA Corning 965) to determine total CO2 (CCO2). The micro pH probe was calibrated prior to use with NBS standards at the acclimation temperature and the gas analyser was calibrated using manufacturer guidelines. Two oysters were sampled per genotype-line in each replicate tank. Partial pressure of CO2 in haemolymph (PCO2e) was calculated from the CCO2 using the modified Henderson-Hasselbalch equation according to Heisler31,32. Metabolic rate (MR) was determined using a closed respiratory system as previously described in Parker et al.16 and Scanes et al.18. Briefly, MR was measured in two oysters per genotype-line, per tank by placing oysters in a closed 500 mL glass chamber containing filtered seawater (5 µm) set at the correct treatment conditions. Oxygen concentrations were then measured within the chamber using a fibre optic dipping probe (PreSens dipping probe DP-PSt3, AS1 Ltd, Regensburg, Germany) and recorded (15 s intervals) until the oxygen concentration had been reduced by 20%, the time taken to reduce oxygen by 20% was recorded. Oysters were removed from the chambers, opened and the tissue was dried at 70 °C for 72 h. Tissue was then weighed on an electronic balance (± 0.001 g), and MR was calculated using Eq. (1):$$MR = frac{{left[ {V_{r} times Delta {text{C}}_{W} O_{2} } right]}}{{Delta t times {text{bw}}}}$$
(1)

where MR is oxygen consumption normalised to 1 g of dry tissue mass (mg O2 g−1 dry tissue mass h−1), Vr is the volume of the respiratory chamber minus the volume of the oyster (L), ΔCWO2 is the change in water oxygen concentration measured (mg O2L−1), Δt is the measuring time (h), bw is the dry tissue mass (g). Equation is modified from Parker et al.16.Data analysisIt was not possible to measure all variables in each oyster, but rather three individuals were needed to fulfil one replicate set of measurements. PCO2e and pHe could be measured in the same individual however, MR and the microbiome were measured in separate individuals. This meant that measurements were taken from 6 oysters per genotype-line, per replicate tank (each measurement replicated twice). To align physiological data with microbiome data we took a conservative approach where data from PCO2e and pHe, MR and the microbiome were randomly matched to individuals from the same genotype-line and replicate tank. This gave us the best approximation and is conservative because it increased variability compared to taking all measurements from the same individual. ANOVA was used to determine the significant (n = 210; P  More

1.Sponheimer, M. Isotopic evidence of early hominin diets. Proc. Natl. Acad. Sci. USA 110, 10513–10518 (2013).CAS 
PubMed Central 
Article 
ADS 
PubMed 

Google Scholar 
2.Fleagle, J. G. et al. (eds) Out of Africa I: The first hominin colonization of Eurasia. Vertebrate Paleobiology and Paleoanthropology (Springer, 2010).
Google Scholar 
3.Norton, C. J. & Braun, D. R. (eds) Asian Paleoanthropology: From Africa to China and Beyond. Vertebrate Paleobiology and Paleoanthropology (Springer, 2010).
Google Scholar 
4.Bettis, E. A. III. et al. Way out of Africa: Early Pleistocene paleoenvironments inhabited by Homo erectus in Sangiran, Java. J. Hum. Evol. 56, 11–24 (2009).PubMed 
Article 

Google Scholar 
5.Ciochon, R. L. Divorcing hominins from the Stegodon-Ailuropoda Fauna: New views on the antiquity of hominins in Asia. In Out of Africa I: The First Hominin Colonization of Eurasia (eds Fleagle, J. G. et al.) 111–126 (Springer, 2010).Chapter 

Google Scholar 
6.Sémah, A.-M., Sémah, F., Djubiantono, T. & Brasseur, B. Landscapes and hominids’ environments: Changes between the Lower and the early Middle Pleistocene in Java (Indonesia). Quat. Int. 223, 451–454 (2010).Article 

Google Scholar 
7.Janssen, R. et al. Tooth enamel stable isotopes of Holocene and Pleistocene fossil fauna reveal glacial and interglacial paleoenvironments of hominins in Indonesia. Quat. Sci. Rev. 144, 145–154 (2016).Article 
ADS 

Google Scholar 
8.Rizal, Y. et al. Last appearance of Homo erectus at Ngandong, Java, 117,000–108,000 years ago. Nature 577, 381–385 (2020).CAS 
PubMed 
Article 

Google Scholar 
9.Chen, F. et al. A late Middle Pleistocene Denisovan mandible from the Tibetan Plateau. Nature 569, 409–412 (2019).CAS 
PubMed 
Article 
ADS 

Google Scholar 
10.Sutikna, T. et al. Revised stratigraphy and chronology for Homo floresiensis at Liang Bua in Indonesia. Nature 532, 366–369 (2016).CAS 
PubMed 
Article 
ADS 

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

Google Scholar 
12.De Vos, J. Reconsideration of Pleistocene cave faunas from South China and their relation to the faunas from Java. Cour. Forsch. Inst. Senckenberg 69, 259–266 (1984).
Google Scholar 
13.Schwartz, J. H., Long, V. T., Cuong, N. L., Kha, L. T. & Tattersall, I. A diverse hominoid fauna from the late Middle Pleistocene breccia cave of Tham Kuyen, Socialist Republic of Vietnam. Anthrop. Pap. Am. Mus. Nat. Hist. 74, 1–11 (1994).
Google Scholar 
14.Schwartz, J. H., Long, V. T., Cuong, N. L., Kha, L. T. & Tattersall, I. A review of the Pleistocene hominoid fauna of the Socialist Republic of Vietnam. Anthrop. Pap. Am. Mus. Nat. Hist. 76, 1–24 (1995).
Google Scholar 
15.Reyes-Centeno, H. Out of Africa and into Asia: Fossil and genetic evidence on modern origins and dispersal. Quat. Int. 416, 249–262 (2016).Article 

Google Scholar 
16.Bae, C. J., Douka, K. & Petraglia, M. D. On the origin of modern humans: Asian perspectives. Science 358, 9067 (2017).Article 
CAS 

Google Scholar 
17.Dennell, R., Martinón-Torres, M., Bermúdez de Castro, J.-M. & Xing, G. A demographic history of Late Pleistocene China. Quat. Int. 559, 4–13 (2020).Article 

Google Scholar 
18.Westaway, K. E. et al. An early modern human presence in Sumatra 73000–63000 years ago. Nature 548, 322–325 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
19.Bacon, A.-M. et al. Late Pleistocene mammalian assemblages of Southeast Asia: New dating, mortality profiles and evolution of the predator-prey relationships in an environmental context. Palaeogeogr. Palaeoclimatol. Palaeoecol. 422, 101–127 (2015).Article 

Google Scholar 
20.Bourgon, N. et al. Zinc isotopes in Late Pleistocene fossil teeth from a Southeast Asian cave setting preserve paleodietary information. Proc. Natl. Acad. Sci. USA 117, 4675–4681 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Bacon, A.-M. et al. A rhinocerotid-dominated megafauna at the MIS6-5 transition: The late Middle Pleistocene Coc Muoi assemblage, Lang Son province, Vietnam. Quat. Sci. Rev. 186, 123–141 (2018).Article 
ADS 

Google Scholar 
22.Bacon, A.-M. et al. Nam Lot (MIS 5) and Duoi U’Oi (MIS 4) Southeast Asian sites revisited: Zooarchaeological and isotopic evidences. Palaeogeogr. Palaeoclimatol. Palaeoecol. 512, 132–144 (2018).Article 

Google Scholar 
23.Suraprasit, K., Jongauttchariyakul, S., Yamee, C., Pothichaiya, C. & Bocherens, H. New fossil and isotope evidence for the Pleistocene zoogeogeographic transition and hypothesized savanna corridor in peninsular Thailand. Quat. Sci. Rev. 221, 105861 (2019).Article 

Google Scholar 
24.Sun, F. et al. Paleoecology of Pleistocene mammals and paleoclimatic change in South China: Evidence from stable carbon and oxygen isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 524, 1–12 (2019).Article 

Google Scholar 
25.Demeter, F. et al. Anatomically modern human in Southeast Asia (Laos) by 46 ka. Proc. Natl. Acad. Sci. USA 109, 14375–14380 (2012).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
26.Shackelford, L. et al. Additional evidence for early modern human morphological diversity in Southeast Asia at Tam Pà Ling, Laos. Quat. Int. 466, 93–106 (2018).Article 

Google Scholar 
27.Petraglia, M. D., Breeze, P. S. & Groucutt, H. S. Blue Arabia: Examining colonisation and dispersal models. In Geological setting, Palaeoenvironment and Archaeology of the Red Sea (eds Rasul, N. M. A. & Stewart, I. C. F.) 675–683 (Springer International Publishing, 2019).Chapter 

Google Scholar 
28.Cappellini, E. et al. Early Pleistocene enamel proteome from Dmanisi resolves Stephanorhinus phylogeny. Nature 574, 103–107 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
29.Welker, F. et al. Enamel proteome shows that Gigantopithecus was an early diverging pongine. Nature 576, 262–265 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
30.Welker, F. et al. The dental proteome of Homo antecessor. Nature 580, 235–238 (2020).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
31.Wang, W. et al. Sequence of mammalian fossils, including hominoid teeth, from the Bubing Basin caves, South China. J. Hum. Evol. 52, 370–379 (2007).PubMed 
Article 

Google Scholar 
32.Rink, W. J., Wei, W., Bekken, D. & Jones, H. L. Geochronology of Ailuropoda-Stegodon fauna and Gigantopithecus in Guangxi Province, Southern China. Quat. Res. 69, 377–387 (2008).CAS 
Article 

Google Scholar 
33.Norton, C. J., Jin, C., Wang, Y. & Zhang, Y. Rethinking the ¨Palearctic-Oriental biogeographic boundary in Quaternary China. In Asian Paleoanthropology: From Africa to China and Beyond (eds Norton, C. J. & Braun, D. R.) 81–100 (Vertebrate Paleobiology and Paleoanthropology, 2010).
Google Scholar 
34.Turvey, S. T., Tong, H., Stuart, A. J. & Lister, A. M. Holocene survival of Late Pleistocene megafauna in China: A critical review of the evidence. Quat. Sci. Rev. 76, 156–166 (2013).Article 
ADS 

Google Scholar 
35.Ma, J. et al. Isotopic evidence of foraging ecology of Asian elephant (Elephas maximus) in South China during the Late Pleistocene. Quat. Int. 443, 160–167 (2017).Article 

Google Scholar 
36.Owen-Smith, R. N. Megaherbivores. The Influence of Very Large Body Size on Ecology (Cambridge University Press, 1988).Book 

Google Scholar 
37.Louys, J. & Meijaard, E. Palaeoecology of Southeast Asian megafauna-bearing sites from the Pleistocene and a review of environmental changes in the region. J. Biogeography 37, 1432–1449 (2010).
Google Scholar 
38.Graham, R. W. Diversity and community structure of the late Pleistocene mammal fauna of North America. Acta Zool. Fenn. 170, 181–192 (1985).
Google Scholar 
39.Graham, R. W. Spatial response of mammals to late quaternary environmental fluctuations. Science 272, 1601–1606 (1996).CAS 
PubMed 
Article 
ADS 
PubMed Central 

Google Scholar 
40.Price, G. J. Fossil bandicoots (Marsupiala, Peramelidae) and environmental change during the Pleistocene on the Darling Downs, Southern Queensland, Australia. J. Syst. Palaeontol. 2, 347–356 (2004).Article 

Google Scholar 
41.Stewart, J. R. The progressive effect of the individualistic response of species to Quaternary climate change: An analysis of British mammalian faunas. Quat. Sci. Rev. 27, 2499–2508 (2008).Article 
ADS 

Google Scholar 
42.Faith, J. T., Rowan, J. & Du, A. Early hominins evolved within non-analog ecosystems. Proc. Natl. Acad. Sci. USA 116, 21478–21483 (2019).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
43.Zeitoun, V., Chinnawut, W., Debruyne, R., Frère, S. & Auetrakulvit, P. A sustainable review of the Middle Pleistocene benchmark sites including the Ailuropoda-Stegodon faunal complex: The Proboscidean point of view. Quat. Int. 416, 12–26 (2010).Article 

Google Scholar 
44.Jablonski, D. & Sepkoski, J. J. Jr. Paleobiology, community ecology and scales of ecological patterns. Ecology 77, 1367–1378 (1996).CAS 
PubMed 
Article 

Google Scholar 
45.Graham, R. W. Quaternary mammal communities: Relevance of the individualistic response and non-analogue faunas. In Paleobiogeography: Generating New Insights Into the Coevolution of the Earth and Its Biota (eds Lieberman, B. S. & Stigall, A. L.) 141–157 (Paleontological Society Papers, 2005).
Google Scholar 
46.Stewart, J. R. The evolutionary consequence of the individualistic response to climate change. J. Evol. Biol. 22, 2363–2375 (2009).CAS 
PubMed 
Article 

Google Scholar 
47.Hofreiter, M. & Stewart, J. Ecological change, range fluctuations and population dynamics during the Pleistocene. Curr. Biol. 19, R584–R594 (2009).CAS 
PubMed 
Article 

Google Scholar 
48.Tougard, C. & Montuire, S. Pleistocene paleoenvironmental reconstructions and mammalian evolution in South-East Asia: Focus on fossil faunas from Thailand. Quat. Sci. Rev. 25, 126–141 (2006).Article 
ADS 

Google Scholar 
49.Zeitoun, V. et al. Dating, stratigraphy and taphonomy of the Pleistocene site of Ban Fa Suai II (Northern Thailand): Contributions to the study of paleobiodiversity in Southeast Asia. Ann. Paléontol. 105, 275–285 (2019).Article 

Google Scholar 
50.Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).Article 

Google Scholar 
51.Bennett, K. D. & Provan, J. What do we mean by refugia? Quat. Sci. Rev. 27, 2449–2455 (2008).Article 
ADS 

Google Scholar 
52.Leonard, J. A., Wayne, R. K. & Cooper, A. Population genetics of Ice Age brown bears. Proc. Natl. Acad. Sci. USA 97, 1651–1654 (2000).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
53.Leonard, J. A. et al. Megafaunal extinctions and the disappearance of a specialized wolf ectomorph. Curr. Biol. 17, 1146–1150 (2007).CAS 
PubMed 
Article 

Google Scholar 
54.Barnes, I., Matheus, P., Shapiro, B., Jensen, D. & Cooper, A. Dynamics of Pleistocene population extinctions in Beringian brown bears. Science 295, 2267–2270 (2002).CAS 
PubMed 
Article 
ADS 

Google Scholar 
55.Hofreiter, M. et al. Lack of phylogeography in European mammals before the last glaciation. Proc. Natl. Acad. Sci. USA 35, 12963–12968 (2004).Article 
ADS 

Google Scholar 
56.Shapiro, B. et al. Rise and Fall of the Beringian Steppe Bison. Science 306, 1561–1565 (2004).CAS 
PubMed 
Article 
ADS 

Google Scholar 
57.Rohland, N. et al. The population history of extant and extinct hyenas. Mol. Biol. Evol. 22, 2435–2443 (2005).CAS 
PubMed 
Article 

Google Scholar 
58.Gilbert, M. T. P. et al. Intraspecific phylogenetic analysis of Siberian woolly mammoths using complete mitochondrial genomes. Proc. Natl. Acad. Sci. USA 105, 8327–8332 (2008).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
59.Orlando, L. et al. Revising the recent evolutionary history of equids using ancient DNA. Proc. Natl. Acad. Sci. USA 106, 21754–21759 (2009).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
60.Campos, P. F. et al. Ancient DNA analyses exclude humans as the driving force behind late Pleistocene musk ox (Ovibos moschatus) population dynamics. Proc. Natl. Acad. Sci. USA 107, 5675–5680 (2010).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
61.Campos, P. F. et al. Ancient DNA sequences point to a large loss of mitochondrial genetic diversity in the saiga antelope (Saiga tatarica) since the Pleistocene. Mol. Ecol. 19, 4863–4875 (2010).CAS 
PubMed 
Article 

Google Scholar 
62.Lorenzen, E. D. et al. Species-specific responses of Late Quaternary megafauna to climate and humans. Nature 479, 359–365 (2011).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
63.Loog, L. et al. Ancient DNA suggests modern wolves trace their origin to a Late Pleistocene expansion from Beringia. Mol. Ecol. 29, 1596–1610 (2019).Article 

Google Scholar 
64.Lord, E. et al. Pre-extinction demographic stability and genomic signatures of adaptation in the woolly rhinoceros. Curr. Biol. 30, 3871–3879 (2020).CAS 
PubMed 
Article 

Google Scholar 
65.Lister, A. M. The impact of Quaternary Ice Ages on mammalian evolution. Phil. Trans. R. Soc. Lond. B 359, 221–241 (2004).Article 

Google Scholar 
66.Barnosky, A. D. Effects of Quaternary climatic change on speciation in mammals. J. Mammal. Evol. 12, 247–264 (2005).Article 

Google Scholar 
67.Stewart, J. R., Lister, A. M., Barnes, I. & Dalén, L. Refugia revisited: Individualistic responses of species in space and time. Proc. R. Soc. B 277, 661–671 (2010).PubMed 
Article 

Google Scholar 
68.Pushkina, D., Bocherens, H., Chaimanee, Y. & Jeager, J.-J. Stable carbon isotope reconstructions of diet and paleoenvironment from the late Middle Pleistocene Snake cave in northeastern Thailand. Naturwissenschaften 97, 299–309 (2010).CAS 
PubMed 
Article 
ADS 

Google Scholar 
69.Suraprasit, K., Bocherens, H., Chaimanee, Y., Panha, S. & Jeager, J.-J. Late Middle Pleistocene ecology and climate in Northeastern Thailand inferred from the stable isotope analysis of Khok Sung herbivore tooth enamel and the land mammal cenogram. Quat. Sci. Rev. 193, 24–42 (2018).Article 
ADS 

Google Scholar 
70.Suraprasit, K. et al. Long-term isotope evidence on the diet and habitat breadth of Pleistocene to Holocene caprines in Thailand: Implications for the extirpation and conservation of Himalayan gorals. Front. Ecol. Evol. 8, 1–16 (2020).Article 

Google Scholar 
71.Bocherens, H. et al. Flexibility of diet and habitat in Pleistocene South Asian mammals: Implications for the fate of the giant fossil ape Gigantopithecus. Quat. Int. 434, 148–155 (2017).Article 

Google Scholar 
72.Stacklyn, S. et al. Carbon and oxygen isotopic evidence for diets, environments and niche differentiation of early Pleistocene pandas and associated mammals in South China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468, 351–361 (2017).Article 

Google Scholar 
73.Ma, J., Wang, Y., Jin, C., Hu, Y. & Bocherens, H. Ecological flexibility and differential survival of Pleistocene Stegodon orientalis and Elephas maximus in mainland southeast Asia revealed by stable isotope (C, O) analysis. Quat. Sci. Rev. 212, 33–44 (2019).Article 
ADS 

Google Scholar 
74.Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Biol. 40, 503–537 (1989).CAS 
Article 

Google Scholar 
75.van der Merwe, N. J. & Medina, E. The canopy effect, carbon isotope ratios and foodwebs in Amazonia. J. Archaeol. Sci. 18, 249–259 (1991).Article 

Google Scholar 
76.Zazzo, A. et al. Herbivore paleodiet and paleoenvironmental changes in Chad during the Pliocene using stable isotope ratios of tooth enamel carbonate. Paleobiology 26, 294–309 (2000).Article 

Google Scholar 
77.Dansgaard, W. Stable isotopes in precipitation. Tellus 16, 436–468 (1964).Article 
ADS 

Google Scholar 
78.Longinelli, A. Oxygen isotopes in mammal bone phosphate: A new tool for paleohydrological and paleoclimatological research? Geochim. Cosmochim. Acta 48, 385–390 (1984).CAS 
Article 
ADS 

Google Scholar 
79.Luz, B., Kolodny, Y. & Horowitz, M. Fractionation of oxygen isotopes between mammalian bone-phosphate and environmental drinking water. Geochim. Cosmochim. Acta 48, 1689–1693 (1984).CAS 
Article 
ADS 

Google Scholar 
80.Fricke, H. C., Clyde, W. C. & O’Neil, J. R. Intra-tooth variations in δ 18O (PO4) of mammalian tooth enamel as a record of seasonal variations in continental climate variables. Geochim. Cosmochim. Acta 62, 1839–1850 (1998).CAS 
Article 
ADS 

Google Scholar 
81.Fricke, H. C., Clyde, W. C., O’Neil, J. R. & Gingerich, P. D. Evidence for rapid climate change in North America during the latest Paleocene thermal maximum: Oxygen isotope compositions of biogenic phosphate from the Bighorn Basin (Wyoming). Earth Planet. Sci. Lett. 160, 193–208 (1998).CAS 
Article 
ADS 

Google Scholar 
82.Kohn, M. J., Schoeninger, M. J. & Valley, J. W. Herbivore tooth oxygen isotope compositions: Effects of diet and physiology. Geochim. Cosmochim. Acta 60, 3889–3896 (1996).CAS 
Article 
ADS 

Google Scholar 
83.Bryant, J. D. & Froelich, P. N. A model of oxygen isotope fractionation in body water of large mammals. Geochim. Cosmochim. Acta 59, 4523–4537 (1995).CAS 
Article 
ADS 

Google Scholar 
84.Kohn, M. J. & Cerling, T. E. Stable isotope compositions of biological apatite. Rev. Mineral. Geochem. 48, 455–488 (2002).CAS 
Article 

Google Scholar 
85.Zheng, Z. & Lei, Z.-Q. A 400,000 years record of vegetational and climatic changes from a volcanic basin, Leizhou Peninsula, southern China. Palaeogeogr. Palaeoclimatol. Palaeoecol. 145, 339–362 (1999).Article 

Google Scholar 
86.Li, S.-P. et al. Pleistocene vegetation in Guangxi, south China, based on palynological data from seven karst caves. Grana 59, 94–106 (2020).Article 

Google Scholar 
87.Wang, Y. et al. Millenial- and orbital-scale changes in the East Asian monsoon over the past 224,000 years. Nature 451, 1090–1093 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
88.Chen, H. et al. A penultimate glacial monsoon record from Hulu Cave and two-phase glacial terminations. Geology 34, 217–220 (2006).Article 
ADS 
CAS 

Google Scholar 
89.Kelly, M. J. et al. High resolution characterization of the Asian Monsoon between 146,000 and 99,000 years B.P. from Dongge Cave, China and global correlation of events surrounding Termination II. Palaeogeogr. Palaeoclimatol. Palaeoecol. 236, 20–38 (2006).Article 

Google Scholar 
90.Milano, S. et al. Environmental conditions framing the first evidence of modern humans at Tam Pà Ling, Laos: A stable isotope record from terrestrial gastropod carbonates. Palaeogeogr. Palaeoclimatol. Palaeoecol. 511, 352–363 (2018).Article 

Google Scholar 
91.Bird, M. I., Taylor, D. & Hunt, C. Palaeoenvironments of insular southeast Asia during the last glacial period: A savanna corridor in Sundaland? Quat. Sci. Rev. 24, 228–242 (2005).Article 

Google Scholar 
92.Marwick, B. & Gagan, M. K. Late Pleistocene monsoon variability in northwest Thailand: An oxygen isotope sequence from the bivalve Margaritanopsis laosensis excavated in Mae Hong Son province. Quat. Sci. Rev. 30, 3088–3098 (2011).Article 
ADS 

Google Scholar 
93.Geist, V. On the relationship of social evolution and ecology in ungulates. Am. Zool. 14, 205–220 (1974).Article 

Google Scholar 
94.Bacon, A.-M. et al. Testing the savannah corridor hypothesis during MIS2: The Boh Dambang hyena site in southern Cambodia. Quat. Int. 464, 417–439 (2018).Article 

Google Scholar 
95.Cannon, C. H., Robert, J., Morley, R. J. & Bush, A. B. G. The current refugial rainforests of Sundaland are unrepresentative of their biogeographic past and highly vulnerable to disturbances. Proc. Natl. Acad. Sci. USA 106, 11188–11193 (2009).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
96.Yuan, D. et al. Timing, duration, and transitions of the Last Interglacial Asian monsoon. Science 304, 575–578 (2004).CAS 
PubMed 
Article 
ADS 

Google Scholar 
97.Hublin, J.-J. How old are the oldest Homo sapiens in Far East Asia? Proc. Natl. Acad. Sci. USA 118, e2101173118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
98.Boivin, N., Fuller, D. Q., Dennell, R., Allaby, R. & Petraglia, M. D. Human dispersal across diverse environments of Asia during the Upper Pleistocene. Quat. Int. 300, 32–47 (2013).Article 

Google Scholar 
99.Perera, N. et al. People of the ancient rainforest: Late Pleistocene foragers at the Batadomba-Iena rockshelter, Sri Lanka. J. Hum. Evol. 61, 254–269 (2011).PubMed 
Article 

Google Scholar 
100.Roberts, P., Boivin, N., Lee-Thorp, J., Petraglia, M. & Stock, J. Tropical forests and the genus Homo. Evol. Anthropol. 25, 306–317 (2016).PubMed 
Article 

Google Scholar 
101.Roberts, P. & Petraglia, M. D. Pleistocene rainforests: Barriers or attractive environments for early human foragers? World Archaeol. 47, 718–739 (2015).Article 

Google Scholar 
102.Wedage, O. et al. Specialized rainforest hunting by Homo sapiens ~45,000 years ago. Nat. Commun. 10, 739 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
103.Barker, G. et al. The “human revolution” in lowland tropical Southeast Asia: The antiquity and behavior of anatomically modern humans at Niah cave (Sarawak, Borneo). J. Hum. Evol. 52, 243–261 (2007).PubMed 
Article 

Google Scholar 
104.Piper, P. J. & Rabett, R. J. Hunting in a tropical rainforest: Evidence from the terminal Pleistocene at Lobang Hangus, Niah caves, Sarawak. Int. J. Osteoarchaeol. 19, 551–565 (2009).Article 

Google Scholar 
105.Mellars, P. Going East: New genetic and archaeological perspectives on the modern human colonization of Eurasia. Science 313, 796–800 (2006).CAS 
PubMed 
Article 
ADS 

Google Scholar 
106.Posth, C. et al. Pleistocene mitochondrial genomes suggest a single major dispersal of non-Africans and a Late Glacial populations turnover in Europe. Curr. Biol. 26, 827–833 (2016).CAS 
PubMed 
Article 

Google Scholar 
107.Roberts, P. & Stewart, B. A. Defining the ‘generalist specialist’ niche for Pleistocene Homo sapiens. Nat. Hum. Behav. 2, 542–550 (2018).PubMed 
Article 

Google Scholar 
108.Zachwieja, A. J. et al. Understanding Late Pleistocene human land preference using ecological niche models in an Australasian test case. Quat. Int. 563, 13–28 (2020).Article 

Google Scholar 
109.Shea, J. J. Homo sapiens is as Homo sapiens was: Behavioral variability versus “behavioral modernity” in Paleolithic archaeology. Curr. Anthropol. 52, 1–35 (2011).Article 

Google Scholar 
110.Sun, X.-F. et al. Ancient DNA and multimethod dating confirm the late arrival of anatomically modern humans in southern China. Proc. Natl. Acad. Sci. USA 118, e2019158118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
111.Martinón-Torres, M. et al. On the misidentification and unreliable context of the new “human teeth” from Fuyan Cave (China). Proc. Natl. Acad. Sci. USA 118, e2102961118 (2021).PubMed 
Article 
CAS 

Google Scholar 
112.Timmerman, A. & Friedrich, F. T. Late Pleistocene climate drivers of early human migration. Nature 538, 92–95 (2016).Article 
ADS 
CAS 

Google Scholar 
113.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 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
114.De Deckker, P. et al. Marine Isotope Stage 4 in Australasia: A full glacial culminating 65,000 years ago: Global connections and implications for human dispersal. Quat. Sci. Rev. 204, 187–207 (2019).Article 
ADS 

Google Scholar 
115.Clarkson, C. et al. Human occupation of northern Australia by 65,000 years ago. Nature 547, 306–310 (2017).CAS 
PubMed 
Article 
ADS 

Google Scholar 
116.O’Connell, J. F. et al. When did Homo sapiens first reach Southeast Asia and Sahul?. Proc. Natl. Acad. Sci. USA 115, 8482–8490 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
117.Brain, C. K. The Hunters and the Hunted? An Introduction to African Cave Taphonomy (The University of Chicago press, 1981).
Google Scholar 
118.Lucchini, V., Meijaard, E., Diong, C. H., Groves, C. P. & Randi, E. New phylogenetic perspectives among species of South-east Asian wild pig (Sus sp.) based on mtDNA sequences and morphometric data. J. Zool. Lond. 266, 25–35 (2006).Article 

Google Scholar 
119.Sponheimer, M. et al. Do “savanna” chimpanzees consume C4 resources? J. Hum. Evol. 51, 128–133 (2006).CAS 
PubMed 
Article 

Google Scholar 
120.Cerling, T. E. et al. Dietary changes of large herbivores in the Turkana Basin, Kenya from 4 to 1 Ma. Proc. Natl. Acad. Sci. USA 112, 11467–11472 (2015).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar 
121.Tejada-Lara, J. V. et al. Comparative isotope ecology of western Amazonian rainforest mammals. Proc. Natl. Acad. Sci. USA 117, 26263–26272 (2020).Article 
CAS 

Google Scholar 
122.Kohn, M. J. Carbon isotope compositions of terrestrial C3 Plants as Indicators of (Paleo)ecology and (Paleo)climate. Proc. Natl. Acad. Sci. USA 107, 19691–19695 (2010).CAS 
PubMed 
PubMed Central 
Article 
ADS 

Google Scholar  More

Study systemWe conducted our study in Caracoles Ranch, located in Doñana National Park (SW Spain 37° 04′ N, 6° 18′ W). The study area has a Mediterranean climate with mild winters and an average 50-year annual rainfall of 550–570 mm. Vegetation is dominated by annual grassland species, with no perennial species present. A subtle topographic gradient (slope 0.16%) is enough to generate vernal pools at the lower border of the ranch from winter (November–January) to spring (March–May), while upper parts do not get flooded except in exceptionally wet seasons. In our study, an extreme flooding event occurred during the growing season of 2018. A strong soil salinity–humidity gradient is structured along this topographic gradient.In September 2014, we established nine plots of 8.5 m × 8.5 m along a 1 km × 200 m area. Three of these nine plots were located in the upper part of the topographic gradient, three at the middle, and the last three at the lower part. The average distance between these three locations was 300 m and the average distance between plots within each location was 30 m (minimum distance 20 m). In addition, each plot was divided into 36 subplots of 1 m × 1 m with aisles of 0.5 m in between to allow access to subplots where measurements were taken (total of 324 subplots). This spatial design was established to parameterize population models including an intrinsic fecundity component and the effect of intra- and interspecific pairwise interactions. Specifically, the core of the observations involved measuring, for each focal individual, per germinant viable seed production as a function of the number and identity of neighbors within a radius of 7.5 cm including individuals of the same species. This radius is a standard distance used in previous studies to measure competitive interactions among annual plant species29,34, and has been validated to capture the outcome of competition interactions at larger scales (1 m²) under locally homogeneous environmental conditions35. From November 2014 to September 2019, we sampled 19 species present in the study area each year. We sampled one individual per subplot for widespread species and several individuals per subplot when species were rare (max. 324 individuals/species). This sampling design ensured that all species are balanced in terms of number of observations, and that we capture the full range of observed spatial interactions among species across the study area. Furthermore, we obtained independent estimates of seed survival and seed germination rates in 2016 (see17 for details on obtaining these rates). These 19 species belong to disparate taxonomic families and exhibit contrasted functional profiles along the growing season (Supplementary Table 1). The earliest species with small size and open flowers, such as C. fuscatum (Asteraceae), peak at beginning of the growing season (February), while late species with succulent leaves, such as S. soda (Amaranthaceae) and S. splendens (Amaranthaceae), grow during summer and peak at the end of the growing season (September-October). All these species represent up to 99% of plant cover in the study area.Estimating species interaction networks and intrinsic growth ratesWe estimated the effect of nearby individuals on individual fecundity via a Ricker model of population dynamics, which allowed us to estimate the strength of positive or negative interactions among pair of species, and therefore, to build a matrix of interactions among species. This approach has been previously applied to study annual plant systems under Mediterranean-type climates36, and it has also recently been shown to have several advantages compared to other formulations34. For example, this model implemented using a negative-binomial distribution for individual fecundities is more flexible in terms of modeling over-dispersion than a Poisson model, while maintaining predictions as positive integers. The model is of the form$${F}_{i,t}={lambda }_{i}{e}^{-({sum }^{}{alpha }_{i,j}{N}_{j,t})}$$
(1)
where ({lambda }_{i}) is the number of seeds produced by species i in the absence of interactions, ({alpha }_{i,j}) is the per capita effect of species j over species i (which can be positive or negative, thus allowing both competitive and facilitative effects), and ({N}_{j,t}) is the number of individuals of species j within 7.5 cm of the focal individual at timestep t. We fitted this model to the empirical data using Bayesian multilevel models with a negative-binomial distribution34. For model fitting, we used non-informative priors with MCMC settings of 5000 iterations (of which 2500 were warm-up) and 6 chains. The model was implemented using the brms R package37. The effect of changes in environmental conditions on species persistence can be phenomenologically evaluated by allowing models to vary in their estimates of species’ intrinsic growth rates and the reorganization of species interactions38. In our case, to evaluate the effect of environmental heterogeneity on species persistence (Question 1), we developed two complementary models. In both cases, we modeled the observed viable seed production per individual as a function of the identity and abundance of neighboring species. For the model assuming that plant species interact within a homogeneous environment across plots, we pooled together observations from the whole study area, and allowed the intercept and slope of the relationships to vary across years by including year as a random effect. Thus, the ({lambda }_{i}) and ({alpha }_{i,j}) values in Eq. 1 vary across years, but are homogeneous for the whole study area. We used the means from the obtained posterior distributions as estimates in the subsequent analyses. For the model that assumes that heterogeneous environments across space and time impact species population dynamics, we included an additional crossed random effect “plot”, thus obtaining spatially and temporally differentiated seed production in the absence of neighbors (({lambda }_{i})) and interaction coefficients (({alpha }_{i,j})). Importantly, our modeling approach does not evaluate the magnitude per se of the spatiotemporal variability in our system. It rather evaluates the response of plant species to changes in environmental conditions through their effects on vital rates and interaction coefficients (see39,40,41 for similar approaches). Likewise, this approach does not model the spatial dynamics of the community or spatially explicit mechanisms such as dispersal, but rather uses observed spatially explicit associations of individuals to infer their vital rates and interaction coefficients. In the following, we refer to the two developed models as “homogeneous parameterization” and “heterogeneous parameterization”, respectively (Fig. 1).The statistical methodology generates a posterior distribution of estimates for each parameter inferred, i.e., for each intrinsic fecundity rate (({lambda }_{i})) and interaction coefficient (({alpha }_{i,j})). These means, by definition, do not capture the full variability obtained with the statistical model, and may potentially be biased, especially for species pairs that have comparatively few observations. To ensure that our results were not biased by using the posterior mean as a fixed value in subsequent analyses, we replicated our analyses using random samples from the posterior distributions instead of the mean values. We generated 100 random draws from each parameterization and compared the obtained curves to the ones derived from the posterior means (Supplementary Note 1 and Supplementary Fig. 3).Finally, we assume that the study system presents a rich soil seed bank but we do not explicitly model its direct influence on driving the spatial pattern of species interactions or intrinsic vital rates: rather we use fixed field estimates of seed survival and germination rates in our modeling framework (see section “Analyzing species persistence”). This assumption implies that we cannot evaluate the contribution of a spatially or temporally varying seed bank to the shape of CARs and SARs, which remains an open question for future studies.Analyzing species persistenceTo analyze which species are predicted to persist and coexist with others in our system, we built communities based on the species’ spatial location. At the smallest spatial scale, given a community of S species observed in the field in a given plot and a given year, we calculated the persistence of each species within every community combination, from 2 species to S. Thus, we obtained for each species, plot, and year, two estimates of persistence, one from the homogeneous and another from the heterogeneous parameterization. To scale-up our predictions of species persistence at increasingly large areas, we aggregated species composition and persistence patterns from increasing numbers of plots. We consistently evaluated species persistence using a structuralist approach because prior work has shown it is compatible with the model used to estimate interaction coefficients (Eq. 1)14. Specifically, for a given community we first used the strength of sign of intra- and interspecific interactions to compute its feasibility domain (note that the structuralist approach can accommodate different signs in the interaction coefficients). Broadly speaking the feasibility domain is the structural analog of niche differences, and it represents the possible range of intrinsic species growth rates compatible with the persistence of individual species and of the entire community14. Indeed, the larger the feasibility domain, the larger the likelihood of species to persist. Yet, computing the feasibility domain does not tell us which species can persist. To obtain such information, we need to check whether the vector containing the observed differences in intrinsic growth rates between species fits within the limits of the feasibility domain. If so, then all species are predicted to coexist. If not, then one or more particular species is predicted to be excluded (see14 for a graphical representation).In order to quantify the feasibility of ecological communities, the intrinsic growth rates and interaction coefficients must be formulated according to a linear Lotka-Volterra model, or an equivalent formulation14. We transformed the parameters obtained from Eq. 1 to an equivalent Lotka-Volterra formulation with the following expression (Supplementary Note 2):$${r}_{i}={log}left(frac{1-(1-{g}_{i}){s}_{i}}{{g}_{i}}right)+{lambda }_{i}$$
(2)
where ({g}_{i}) is the seed germination rate of species i and ({s}_{i}) is its seed survival rate. Thus, we quantified the feasibility of our communities using the ri intrinsic growth rates from Eq. 2 and the ({alpha }_{i,j}) coefficients, which are not modified. For our main analyses, we used empirical estimates of seed survival and germination rates. We further explored the influence of these vital rates in the transformed intrinsic growth rates in Supplementary Note 2 (see also Supplementary Fig. 4 and Supplementary Table 4).The structuralist methodology further allowed us to dissect which specific configuration of species interactions is behind species persistence in our system (Question 2), among three possibilities: first, a given species may be able to persist by itself, and hinder the long-term persistence of neighboring species (category dominant). Second, pairs of species may be able to coexist through direct interactions (category coexistence of species pairs). The classic example of two-species coexistence is when the stabilizing effect of niche differences that arise because intraspecific competition exceeds interspecific competition overcome fitness differences41. Lastly, species may only be able to coexist in more complex communities (category multispecies coexistence)23, thanks to the effect of indirect interactions on increasing the feasible domain of the community14. A classic example of multispecies coexistence is a rock–paper–scissors configuration in which the three species coexist because no species is best at competing for all resources24,42. Because species may be predicted to persist under different configurations in a given community, we assigned their persistence category to the simplest community configuration. For instance, if we predicted that a three-species combination coexists as well as each of the three pairs separately, we assigned these species to the coexistence of pairs category26,43. Finally, if a species is not predicted to persist but it is observed in the system, we classify it as naturally transient, that is, it will tend to become locally extinct no matter what its surrounding community. In order to ascertain our classification of species as transient, we further analyzed whether these species shared ecological traits known to be common to transient species. In particular, a pervasive characteristic of transient species is their comparatively small population sizes. We explored the relationship between our classification as transient and species abundance through a logistic regression with logit link (supplementary Table 3).In addition to our main analyses, based on the structuralist approach, we explored the local stability44 of the observed communities, which evaluates their asymptotic response to infinitesimal perturbations, and thus provides a complementary view to the potential coexistence of the system (Supplementary Note 3).Species–area and coexistence–area relationshipsTo answer Question 3, we obtained standard SARs for each year, by calculating the average diversity observed when moving from 1 plot (72 m²) to 9 plots (650 m²) of our system. In the classification of SAR types proposed by Scheiner et al.45, the curves from our system are thus of type III-B, i.e., plots in a non-contiguous grid, with diversity values obtained using averages from all possible combinations of plots. Likewise, the yearly CARs were built taking the average number of coexisting species in each combination from 1 to 9 plots. In this case, a species was taken to persist in a given area if it was persisting alone or if it was part of at least one coexisting community within that area. We obtained CARs for the two parameterizations, i.e., assuming homogeneous interaction coefficients and individual fecundity throughout the study area, or explicitly including spatial variability in these terms. We fitted the CARs from Fig. 2 to power-law functions and obtained their associated parameters (Supplementary Table 2) using the mmSAR v1.0 package in R46. In the last step of the analyses, to evaluate the role of species identity in driving these empirical fits of CARs, we compared them to two complementary null models that reshuffle the strengths of per capita interactions between species pairs across the interaction matrix. In particular, as baseline we took the CARs from the homogeneous parameterization, in order to have a unique interaction strength value per species pair. In the first null model, and taking the inferred interaction matrix from a given plot and year, we redistributed the pairwise interaction coefficients randomly. That is, we fixed the number of species observed in a certain plot and year, as well as the structure of the interaction matrix, but randomized the magnitude of observed pairwise interactions (both intra and interspecific interactions) in that community. The second null model is similar, but keeping the diagonal coefficients of the interaction matrix, i.e., the intraspecific terms, fixed. While the first null model accounted for the effect of interspecific competitive responses, as well as self-limiting processes on driving CARs, the second null maintained self-limiting processes fixed by avoiding changes in the diagonal elements of our interaction matrices. We ran 100 replicates of each model for each year, and obtained the average CARs across replicates. All analyses were carried out in R v3.6.3, using packages tidyverse47 v.1.3.1 for data manipulation and visualization, and foreach48 v1.5.1 and doParallel49 v1.0.16 for parallelizing computationally intensive calculations.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

EDITORIAL
26 October 2021

The answer to the biodiversity crisis is not more debt

Funding pledges from China and other countries need to be given in grants — which must include research grants — and not as a reward for taking out loans.

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

The Alichur Valley in Tajikistan is among a number of ecologically sensitive areas that researchers say could be affected by China’s Belt and Road Initiative.Credit: Alamy

Funding for biodiversity is getting some attention at last.In September, nine philanthropic organizations, most of them in the United States, pledged a total of US$5 billion over a decade towards projects that will help to preserve the richness of Earth’s species.This month, Chinese President Xi Jinping announced the allocation of 1.5 billion yuan ($235 million) to the new Kunming Biodiversity Fund. This will have a goal of funding projects, such as protected areas, that will contribute to slowing down and eventually reversing the loss of species and ecosystems.More details are awaited from China, along with further information on a promise made by the European Union to double its funding for biodiversity. Contributions to the Kunming fund should be given as grants, not loans; they should have a research component; and they should be pooled and managed through international organizations. Moreover, the rules for access need to be transparent and fair to all applicants. These are important factors to emphasize, because there seems to be a trend towards providing environmental finance as loans — many of them to some of the world’s poorest countries, which are often already highly indebted.
The broken $100-billion promise of climate finance – and how to fix it
The pledges were timed to coincide with the first part of the China-hosted United Nations biodiversity conference, COP15, which ended on 24 October. Collectively, the sums, although not insignificant, will amount to little more than a 1–2% increase on the roughly $133 billion a year that the world currently spends on biodiversity. Well over half of this is spent by China, the EU, Japan and the United States.Spending on biodiversity needs to increase in all regions, according to a report by the UN Environment Programme, published in May (see go.nature.com/3ekaopk). For comparison, money earmarked for tackling climate change totalled $632 billion per year in 2019–20, according to a Nature analysis (Nature 598, 400–402; 2021).The reasons that finance for biodiversity is lower than that for its climate cousin include a relative dearth of finance in low- and middle-income countries and the fact that more than half of all climate funds take the form of loans. Both public and private investors know that in financing projects such as solar energy plants or batteries research and development, they will probably see a return on their investments. By contrast, protecting a watershed or a wetland is more of a public service — and so is more likely to be funded from taxation. Partly as a result, some 86% of biodiversity funding currently comes from public sources, in the form of grants.But that might be about to change. Researchers, corporations, bankers and policymakers have been exploring how to create financial investment products — from both private and public sources — in biodiversity, as well as how to better protect nature from the negative environmental impacts of big infrastructure projects. Most industrial sectors rely on biodiversity to some extent. Food producers, forestry, clothing manufacturers and hydropower, for example, would all struggle without healthy soils, pollinators or predictable water supplies. If nature continues to degrade, the world’s economic output will begin to suffer sooner or later.
Global climate action needs trusted finance data
One idea being studied is how to create an internationally agreed reporting system so that any entity — a bank, a government or a corporation — would need to publish data on whether its investments could lead to ecological damage. Such disclosures would probably prompt financiers to think twice before taking on investments that might be environmentally harmful. Earlier this year, an organization called Taskforce on Nature-related Financial Disclosures began work to develop such a system. It is co-chaired by Elizabeth Mrema, the executive secretary of the UN biodiversity convention secretariat, and is based in Montreal, Canada.Another idea under study is called Nature Performance Bonds (NPBs). According to this model, indebted countries would be eligible for more-favourable loan repayment terms if they could commit to spending the cash saved on environmental protection.Last month, a study commissioned by the China Council for International Cooperation on Environment and Development, an organization of policymakers that advises China’s government, recommended that China become a global leader in NPBs (see go.nature.com/3pekzk7). The study says that some 52 low- and middle-income countries owe China a combined total of more than $100 billion in loans. These include loans for projects that are part of China’s Belt and Road Initiative (BRI) to upgrade energy sources, roads, railways and airports, mainly in low- and middle-income countries. Many of China’s BRI investments are in ecologically sensitive areas.The terms of China’s $235-million biodiversity announcement have not yet been confirmed. But it would be wise if this funding were not linked to the debts of countries whose biodiversity is being affected by BRI projects. Otherwise it would seem that China’s main motivation is the greening of its own investments, when, as the host of COP15, it needs to think and act more globally, and work towards creating a fund by and for all nations.
Where climate cash is flowing and why it’s not enough
The Kunming Biodiversity Fund needs to be a stand-alone grant fund, ideally managed by a mechanism involving all countries, and with transparent rules of access. It also needs to have a dedicated research component — something that is not possible through loan finance. And other nations must contribute.The need for research funding is especially acute. There are often few funding opportunities from national research bodies for researchers in low- and middle-income countries that are rich in biodiversity. The UN’s official biodiversity funder, the Global Environment Facility, based in Washington DC, does not have a dedicated research facility. It does fund some science, but that is a part of a small-grants programme (see go.nature.com/3mgu8io) that is mainly focused on funding for conservation.It is clear that biodiversity will be getting more finance. But loan finance must not crowd out or replace grant funding. There is a precedent for this. It is already happening in climate finance, for which a much-delayed $100 billion pledged to be provided annually to low- and middle-income countries will be mainly in the form of loans.A step change in biodiversity finance is needed and the Kunming Biodiversity Fund will be a welcome move in the right direction. But it will be inequitable if most of the promised finance ends up committed to loans. Finding an answer to the biodiversity crisis should not mean the poorest countries having to take on yet more debt.

Nature 598, 539-540 (2021)
doi: https://doi.org/10.1038/d41586-021-02891-y

Related Articles

The broken $100-billion promise of climate finance – and how to fix it

Where climate cash is flowing and why it’s not enough

Global climate action needs trusted finance data

Growing support for valuing ecosystems will help conserve the planet

Subjects

Biodiversity

Climate change

Economics

Policy

Latest on:

Biodiversity

Illegal mining in the Amazon hits record high amid Indigenous protests
News 30 SEP 21

Fine-root traits in the global spectrum of plant form and function
Article 29 SEP 21

Pollinators contribute to the maintenance of flowering plant diversity
Article 08 SEP 21

Climate change

COP26: set a minimum global carbon price for emissions
Correspondence 26 OCT 21

How climate change will make the hottest tropical days even more extreme
Research Highlight 25 OCT 21

COP26 climate summit: A scientists’ guide to a momentous meeting
News Explainer 25 OCT 21

Economics

COP26: set a minimum global carbon price for emissions
Correspondence 26 OCT 21

Countries of the Indo-Gangetic Plain must unite against air pollution
Correspondence 19 OCT 21

The cost of changes in energy use in a warming world
News & Views 13 OCT 21

Jobs

Assistant Professor in Theoretical Neuroscience

Princeton University
Princeton, United States

Assistant, Associate, or Full Professor (Tenure Track Investigator)

Feinberg School of Medicine, NU
Chicago, IL, United States

Assistant Professor in Human Cognitive Neuroscience

Princeton University
Princeton, NJ, United States

Postdoctoral positions in functional and structural studies of ion channels

Georgetown University Medical Center (GUMC)
Washington, DC, United States More

1.Wang, X., White, S. C. & Guan, J. A new genus and species of sabertooth, Oriensmilus liupanensis (Barbourofelinae, Nimravidae, Carnivora), from the middle Miocene of China suggests barbourofelines are nimravids, not felids. J. Syst. Palaeontol. 18, 783–803 (2020).Article 

Google Scholar 
2.Barrett, P. Z., Hopkins, S. S. B. & Price, S. A. How many sabertooths? Reevaluating the number of carnivoran sabertooth lineages with total-evidence Bayesian techniques and a novel origin of the Miocene Nimravidae. J. Vertebr. Paleontol. 41, e1923523 (2021).Article 

Google Scholar 
3.Robles, J. M. et al. New craniodental remains of the barbourofelid Albanosmilus jourdani (Filhol, 1883) from the Miocene of the Valles-Penedes Basin (NE Iberian Peninsula) and the phylogeny of the Barbourofelini. J. Syst. Palaeontol. 11, 993–1022 (2013).Article 

Google Scholar 
4.Bryant, H. N. Nimravidae. In The terrestrial Eocene-Oligocene Transition in North America (eds. Prothero, D. R. & Emry, R. J.) 453–475 (Cambridge University Press, 1996).5.Peigné, S. Systematic review of European Nimravinae (Mammalia, Carnivora, Nimravidae) and the phylogenetic relationships of Palaeogene Nimravidae. Zool. Scr. 32, 199–229 (2003).Article 

Google Scholar 
6.Barrett, P. Z. Taxonomic and systematic revisions to the North American Nimravidae (Mammalia, Carnivora). PeerJ 4, e1658 (2016).Article 

Google Scholar 
7.Morlo, M., Peigné, S. & Nagel, D. A new species of Prosansanosmilus: Implications for the systematic relationships of the family Barbourofelidae new rank (Carnivora, Mammalia). Zool. J. Linn. Soc. 140, 43–61 (2004).Article 

Google Scholar 
8.Geraads, D. & Güleç, E. Relationships of Barbourofelis piveteaui (Ozansoy, 1965), a late miocene nimravid (Carnivora, Mammalia) from Central Turkey. J. Vertebr. Paleontol. 17, 370–375 (1997).Article 

Google Scholar 
9.Janis, C. M., Figueirido, B., Desantis, L. & Lautenschlager, S. An eye for a tooth: Thylacosmilus was not a marsupial ‘saber-tooth predator’. PeerJ 8, e9346 (2020).Article 

Google Scholar 
10.Slater, G. J. & Van Valkenburgh, B. Long in the tooth: Evolution of sabertooth cat cranial shape. Paleobiology 34, 403–419 (2008).Article 

Google Scholar 
11.Wallace, S. C. & Hulbert, R. C. A new machairodont from the Palmetto Fauna (Early Pliocene) of Florida, with comments on the origin of the Smilodontini (Mammalia, Carnivora, Felidae). PLoS One 8, e56173 (2013).ADS 
CAS 
Article 

Google Scholar 
12.Melchionna, M. et al. A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian sabre-tooth. Palaeontology 64, 573–584 (2021).Article 

Google Scholar 
13.Bowdich, T. E. An Analysis of the Natural Classifications of Mammalia, for the Use of Students and Travellers. (Smith, 1821).14.Cope, E. D. On the extinct cats of America. Am. Nat. 14, 833–858 (1880).Article 

Google Scholar 
15.Gervais, P. Zoologie et paléontologie générales. Nouvelles recherches sur les animaux vertébrés vivants et fossiles. 2. série. (A. Bertrand, 1876).16.Morea, F. M. On the Species of Hoplophoneus and Eusmilus (Carnivora, Felidae). Department of Geology (South Dakota School of Mines and Technology, 1975).17.Bryant, H. N. Delayed eruption of the deciduous upper canine in the sabertoothed carnivore Barbourofelis lovei (Carnivora, Nimravidae). J. Vertebr. Paleontol. 8, 298–306 (1988).Article 

Google Scholar 
18Radinsky, L. B. Evolution of skull shape in carnivores. 3. The origin and early radiation of the modern carnivore families. Paleobiology 8, 177–195 (1982).Article 

Google Scholar 
19.Jepsen, G. L. American eusmiloid sabre-tooth cats of the Oligocene epoch. Proc. Am. Philos. Soc. 72, 355–369 (1933).
Google Scholar 
20.Antón, M. Sabertooth. (Indiana University Press, 2013).21.Bryant, H. N. & Russell, A. P. Carnassial functioning in nimravid and felid sabertooths: Theoretical basis and robustness of inferences. In Functional Morphology in Vertebrate Paleontology (ed. Thomason, J.) 116–135 (Cambridge University Press, 1995).22.Van Valkenburgh, B. Skeletal and dental predictors of body mass in carnivores. In Body Size in Mammalian Paleobiology: Estimation and Biological Implications (eds. Damuth, J. & MacFadden, B. J.) 181–205 (Cambridge University Press, 1990). https://doi.org/10.1017/CBO9781107415324.004.23.Martin, L. D. Functional morphology and the evolution of cats. Trans. Nebraska Acad. Sci. 8, 141–154 (1980).
Google Scholar 
24.Morlo, M. New remains of Barbourofelidae (Mammalia, Carnivora) from the Miocene of Southern Germany: Implications for the history of barbourofelid migrations. Beitr. Paläontol. 30, 339–349 (2006).
Google Scholar 
25.Kingdon, J. The Kingdon Field Guide to African mammals 2nd edn. (Princeton University Press, 2015).
Google Scholar 
26.Carbone, C., Mace, G. M., Roberts, S. C. & Macdonald, D. W. Energetic constraints on the diet of terrestrial carnivores. Nature 402, 286–288 (1999).ADS 
CAS 
Article 

Google Scholar 
27.Carbone, C., Teacher, A. & Rowcliffe, J. M. The costs of carnivory. PLoS Biol. 5, 0363–0368 (2007).CAS 

Google Scholar 
28.Prothero, D. R. & Emry, R. J. The Chadronian, Orellan, and Whitneyan North American land mammal ages. In Late Cretaceous and Cenozoic Mammals of North America: Biostratigraphy and Geochronology (ed. Woodburne, M. O.) 156–168 (Columbia University Press, 2004).29.Boardman, G. S. Paleoecology of Nebraska’s Ungulates During the Eocene-Oligocene Climate Transition. Dissertations & Theses in Earth and Atmospheric Sciences (University of Nebraska-Lincoln, 2013).30.Christiansen, P. Phylogeny of the sabertoothed felids (Carnivora: Felidae: Machairodontinae). Cladistics 29, 543–559 (2013).Article 

Google Scholar 
31.Morales, J., Pickford, M., Salesa, M. & Soria, D. The systematic status of Kelba, Savage, 1965, Kenyalutra, Schmidt-Kittler, 1987 and Ndamathaia, Jacobs et al., 1987, (Viverridae, Mammalia) and a review of Early Miocene mongoose-like carnivores of Africa. Ann. Paléontol. 86, 243–251 (2000).Article 

Google Scholar 
32.Borths, M. R., Holroyd, P. A. & Seiffert, E. R. Hyainailourine and teratodontine cranial material from the late Eocene of Egypt and the application of parsimony and Bayesian methods to the phylogeny and biogeography of Hyaenodonta (Placentalia, Mammalia). PeerJ 4, e2639 (2016).Article 

Google Scholar 
33.Borths, M. R. & Stevens, N. J. Simbakubwa kutokaafrika, gen. et sp. Nov. (Hyainailourinae, Hyaenodonta, ‘Creodonta’, Mammalia), a gigantic carnivore from the earliest Miocene of Kenya. J. Vertebr. Paleontol. 39, 1–20 (2019).Article 

Google Scholar 
34.Tseng, Z. J., Takeuchi, G. T. & Wang, X. Discovery of the upper dentition of Barbourofelis whitfordi (Nimravidae, Carnivora) and an evaluation of the genus in California. J. Vertebr. Paleontol. 30, 244–254 (2010).Article 

Google Scholar 
35.Piras, P. et al. Evolution of the sabertooth mandible: A deadly ecomorphological specialization. Palaeogeogr. Palaeoclimatol. Palaeoecol. 496, 166–174 (2018).Article 

Google Scholar 
36.Tedford, R. H. et al. Mammalian biochronology of the Arikareean through Hempillian interval (late Oligocene through Early Pliocene epochs). In Late Cretaceous and Cenozoic Mammals of North America: Biostratigraphy and geochronology (ed. Woodburne, M. O.) 169–231 (Columbia University Press, 2004).37Bouckaert, R. et al. BEAST2: A software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).Article 

Google Scholar 
38.Fan, Y., Wu, R., Chen, M. H., Kuo, L. & Lewis, P. O. Choosing among partition models in Bayesian phylogenetics. Mol. Biol. Evol. 28, 523–532 (2011).CAS 
Article 

Google Scholar 
39Antón, M. et al. Implications of the mastoid anatomy of larger extant felids for the evolution and predatory behaviour of sabertoothed cats (Mammalia, Carnivora, Felidae). Zool. J. Linn. Soc. 140, 207–221 (2004).Article 

Google Scholar 
40Meachen-Samuels, J. A. & Van Valkenburgh, B. Radiographs reveal exceptional forelimb strength in the sabertooth cat, Smilodon fatalis. PLoS One 5, e11412 (2010).ADS 
Article 

Google Scholar 
41.Goswami, A., Smaers, J. B., Soligo, C. & Polly, P. D. The macroevolutionary consequences of phenotypic integration: From development to deep time. Philos. Trans. R. Soc. B 369, 20130254 (2014).CAS 
Article 

Google Scholar 
42.Stadler, T. Sampling-through-time in birth-death trees. J. Theor. Biol. 267, 396–404 (2010).ADS 
MathSciNet 
Article 

Google Scholar 
43.Didiera, G., Royer-Carenzib, M. & Laurinc, M. The reconstructed evolutionary process with the fossil record. J. Theor. Biol. 315, 26–37 (2012).ADS 
MathSciNet 
Article 

Google Scholar 
44.Stadler, T., Künert, D., Bonhoeffer, S. & Drummond, A. J. Birth-death skyline plot reveals temporal changes of epidemic spread in HIV and hepatitis C virus (HCV). Proc. Natl. Acad. Sci. U. S. A. 110, 228–233 (2013).ADS 
CAS 
Article 

Google Scholar 
45.Silvestro, D., Schnitzler, J., Liow, L. H., Antonelli, A. & Salamin, N. Bayesian estimation of speciation and extinction from incomplete fossil occurrence data. Syst. Biol. 63, 349–367 (2014).Article 

Google Scholar 
46.Wozencraft, W. C. Order Carnivora. In Mammal Species of the World. A Taxonomic and Geographic Reference (eds. Wilson, D. E. & Reeder, D. M.) 532–628 (Johns Hopkins University Press, 2005).47.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarisation in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67(5), 901–904(2018).CAS 
Article 

Google Scholar 
48.Morales, J. & Pickford, M. A new barbourofelid mandible (Carnivora, Mammalia) from the Early Miocene of Grillental-6, Sperrgebiet, Namibia. Commun. Geol. Surv. Namibia 18, 113–123 (2018).
Google Scholar 
49.Meade, A. & Pagel, M. BayesTraits V3.0.2. (2019). http://www.evolution.rdg.ac.uk/BayesTraitsV3.0.2/BayesTraitsV3.0.2.html. Accessed 9 February 2021.50.Paterson, R. S., Rybczynski, N., Kohno, N. & Maddin, H. C. A total evidence phylogenetic analysis of pinniped phylogeny and the possibility of parallel evolution within a monophyletic framework. Front. Ecol. Evol. 7, 1–16 (2020).Article 

Google Scholar 
51Goloboff, P. A. & Catalano, S. A. TNT version 1.5, including a full implementation of phylogenetic morphometrics. Cladistics 32, 221–238 (2016).Article 

Google Scholar 
52.Griffin, R. H. btw: Run BayesTraitsV3 from R. R package version 2.0. http://www.randigriffin.com/projects/btw.html (2018). Accessed 9 February 2021.53.Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223. https://doi.org/10.1111/j.2041-210X.2011.00169.x (2012).Article 

Google Scholar  More