Ecology

1.
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
2.
Campbell, G. S. & Norman, J. M. An Introduction to Environmental Biophysics (Springer Science & Business Media, 2012).

3.
Lambers, H., Chapin, F. S. & Pons, T. L. Plant Physiological Ecology (Springer New York, 2008).

4.
Ögren, E. & Evans, J. R. Photosynthetic light-response curves. Planta 189, 182–190 (1993).
Google Scholar 

5.
Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Environ. Resour. 39, 91–123 (2014).
Google Scholar 

6.
Walters, M. B. & Field, C. B. Photosynthetic light acclimation in two rainforest Piper species with different ecological amplitudes. Oecologia 72, 449–456 (1987).
CAS  PubMed  Google Scholar 

7.
Walters, R. G. & Horton, P. Acclimation of Arabidopsis thaliana to the light environment: changes in composition of the photosynthetic apparatus. Planta 195, 248–256 (1994).
CAS  Google Scholar 

8.
Bailey, S., Walters, R. G., Jansson, S. & Horton, P. Acclimation of Arabidopsis thaliana to the light environment: the existence of separate low light and high light responses. Planta 213, 794–801 (2001).
CAS  PubMed  Google Scholar 

9.
Sims, D. A. & Pearcy, R. W. Photosynthetic characteristics of a tropical forest understory herb, Alocasia macrorrhiza, and a related crop species, Colocasia esculenta grown in contrasting light environments. Oecologia 79, 53–59 (1989).
PubMed  Google Scholar 

10.
Poorter, H. et al. A meta-analysis of plant responses to light intensity for 70 traits ranging from molecules to whole plant performance. New Phytol. 223, 1073–1105 (2019).
CAS  PubMed  Google Scholar 

11.
Hikosaka, K. & Terashima, I. A model of the acclimation of photosynthesis in the leaves of C3 plants to sun and shade with respect to nitrogen use. Plant Cell Environ. 18, 605–618 (1995).
CAS  Google Scholar 

12.
Warren, C. R. & Adams, M. A. Distribution of N, Rubisco and photosynthesis in Pinus pinaster and acclimation to light. Plant Cell Environ. 24, 597–609 (2001).
CAS  Google Scholar 

13.
Laisk, A., Nedbal, L. & Govindjee (eds) Photosynthesis in silico (Springer Netherlands, 2009); https://doi.org/10.1007/978-1-4020-9237-4

14.
Baldocchi, D. ‘Breathing’ of the terrestrial biosphere: lessons learned from a global network of carbon dioxide flux measurement systems. Aust. J. Bot. 56, 1–26 (2008).
CAS  Google Scholar 

15.
Baldocchi, D. D. How eddy covariance flux measurements have contributed to our understanding of global change biology. Glob. Change Biol. 26, 242–260 (2020).
Google Scholar 

16.
Keenan, T. F. et al. Widespread inhibition of daytime ecosystem respiration. Nat. Ecol. Evol. 3, 407–415 (2019).
PubMed  PubMed Central  Google Scholar 

17.
Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).
PubMed  Google Scholar 

18.
Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).
CAS  PubMed  Google Scholar 

19.
Smith, N. G. et al. Global photosynthetic capacity is optimized to the environment. Ecol. Lett. 22, 506–517 (2019).
PubMed  PubMed Central  Google Scholar 

20.
Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).
CAS  PubMed  PubMed Central  Google Scholar 

21.
Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e38345 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

22.
Bauerle, W. L. et al. Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling. Proc. Natl Acad. Sci. USA 109, 8612–8617 (2012).
CAS  PubMed  Google Scholar 

23.
He, L. et al. Changes in the shadow: the shifting role of shaded leaves in global carbon and water cycles under climate change. Geophys. Res. Lett. 45, 5052–5061 (2018).
Google Scholar 

24.
Ögren, E. & Rosenqvist, E. On the significance of photoinhibition of photosynthesis in the field and its generality among species. Photosynth. Res. 33, 63–71 (1992).
PubMed  Google Scholar 

25.
Greer, D. H., Berry, J. A. & Björkman, O. Photoinhibition of photosynthesis in intact bean leaves: role of light and temperature, and requirement for chloroplast-protein synthesis during recovery. Planta 168, 253–260 (1986).
CAS  PubMed  Google Scholar 

26.
Öquist, G., Chow, W. S. & Anderson, J. M. Photoinhibition of photosynthesis represents a mechanism for the long-term regulation of photosystem II. Planta 186, 450–460 (1992).
PubMed  Google Scholar 

27.
Daniel, E. The temperature dependence of photoinhibition in leaves of Phaseolus vulgaris (L.). Influence of CO2 and O2 concentrations. Plant Sci. 124, 1–8 (1997).
CAS  Google Scholar 

28.
Tyystjärvi, E Photoinhibition of photosystem II. Int. Rev. Cell Mol. Biol. 300, 243–303 (2013).
PubMed  Google Scholar 

29.
Katul, G. G., Palmroth, S. & Oren, R. Leaf stomatal responses to vapour pressure deficit under current and CO2-enriched atmosphere explained by the economics of gas exchange. Plant Cell Environ. 32, 968–979 (2009).
CAS  PubMed  Google Scholar 

30.
Oren, R. et al. Survey and synthesis of intra- and interspecific variation in stomatal sensitivity to vapour pressure deficit. Plant Cell Environ. 22, 1515–1526 (1999).
Google Scholar 

31.
Laing, W. A. Temperature and light response curves for photosynthesis in kiwifruit (Actinidia chinensis) cv. Hayward. N. Zeal. J. Agric. Res. 28, 117–124 (1985).
Google Scholar 

32.
Leverenz, J. W. The effects of illumination sequence, CO2 concentration, temperature and acclimation on the convexity of the photosynthetic light response curve. Physiol. Plant 74, 332–341 (1988).
Google Scholar 

33.
Bazzaz, F. A. & Carlson, R. W. Photosynthetic acclimation to variability in the light environment of early and late successional plants. Oecologia 54, 313–316 (1982).
CAS  PubMed  Google Scholar 

34.
Valladares, F., Martinez-Ferri, E., Balaguer, L., Perez-Corona, E. & Manrique, E. Low leaf-level response to light and nutrients in Mediterranean evergreen oaks: a conservative resource-use strategy? New Phytol. 148, 79–91 (2000).
CAS  Google Scholar 

35.
Poorter, H. & Evans, J. R. Photosynthetic nitrogen-use efficiency of species that differ inherently in specific leaf area. Oecologia 116, 26–37 (1998).
PubMed  Google Scholar 

36.
Kattge, J., Knorr, W., Raddatz, T. & Wirth, C. Quantifying photosynthetic capacity and its relationship to leaf nitrogen content for global-scale terrestrial biosphere models. Glob. Change Biol. 15, 976–991 (2009).
Google Scholar 

37.
Murchie, E. H. & Horton, P. Acclimation of photosynthesis to irradiance and spectral quality in British plant species: chlorophyll content, photosynthetic capacity and habitat preference. Plant Cell Environ. 20, 438–448 (1997).
Google Scholar 

38.
Alter, P., Dreissen, A., Luo, F. L. & Matsubara, S. Acclimatory responses of Arabidopsis to fluctuating light environment: comparison of different sunfleck regimes and accessions. Photosynth. Res. 113, 221–237 (2012).
CAS  PubMed  PubMed Central  Google Scholar 

39.
Slattery, R. A., Walker, B. J., Weber, A. P. M. & Ort, D. R. The impacts of fluctuating light on crop performance. Plant Physiol. 176, 990–1003 (2018).
CAS  PubMed  Google Scholar 

40.
Murchie, E. H., Hubbart, S., Chen, Y., Peng, S. & Horton, P. Acclimation of rice photosynthesis to irradiance under field conditions. Plant Physiol. 130, 1999–2010 (2002).
CAS  PubMed  PubMed Central  Google Scholar 

41.
Thornton, P. E. & Zimmermann, N. E. An improved canopy integration scheme for a land surface model with prognostic canopy structure. J. Clim. 20, 3902–3923 (2007).
Google Scholar 

42.
Grant, R. F. et al. Interannual variation in net ecosystem productivity of Canadian forests as affected by regional weather patterns—a FLUXNET-Canada synthesis. Agric. Meteorol. 149, 2022–2039 (2009).
Google Scholar 

43.
Ryu, Y. et al. Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales. Glob. Biogeochem. Cycles 25, GB4017 (2011).
Google Scholar 

44.
Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).
PubMed  PubMed Central  Google Scholar 

45.
Luo, X., Croft, H., Chen, J. M., He, L. & Keenan, T. F. Improved estimates of global terrestrial photosynthesis using information on leaf chlorophyll content. Glob. Change Biol. 25, 2499–2514 (2019).
Google Scholar 

46.
Pastorello, G. Z. et al. A new data set to keep a sharper eye on land–air exchanges. Eos 98, https://doi.org/10.1029/2017EO071597 (2017).

47.
Lasslop, G. et al. Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: critical issues and global evaluation. Glob. Change Biol. 16, 187–208 (2010).
Google Scholar 

48.
Schaefer, K. et al. A model–data comparison of gross primary productivity: results from the North American Carbon Program site synthesis. J. Geophys. Res. Biogeosci. 117, G03010 (2012).
Google Scholar 

49.
Ricciuto, D. M. et al. NACP Site: Terrestrial Biosphere Model Output Data in Original Format (ORNL DAAC, 2013); https://doi.org/10.3334/ornldaac/1192

50.
Ricciuto, D. M. et al. NACP Site: Terrestrial Biosphere Model and Aggregated Flux Data in Standard Format (ORNL DAAC, 2013); https://doi.org/10.3334/ornldaac/1183

51.
Gonsamo, A. et al. Improved assessment of gross and net primary productivity of Canada’s landmass. J. Geophys. Res. Biogeosci. 118, 1546–1560 (2013).
Google Scholar 

52.
Wang, Q. et al. Simulation and scaling of temporal variation in gross primary production for coniferous and deciduous temperate forests. Glob. Change Biol. 10, 37–51 (2004).
Google Scholar 

53.
Chen, J., Liu, J., Cihlar, J. & Goulden, M. Daily canopy photosynthesis model through temporal and spatial scaling for remote sensing applications. Ecol. Model. 124, 99–119 (1999).
CAS  Google Scholar 

54.
Luo, X. et al. Comparison of big-leaf, two-big-leaf, and two-leaf upscaling schemes for evapotranspiration estimation using coupled carbon–water modeling. J. Geophys. Res. Biogeosci. 123, 207–225 (2018).
Google Scholar 

55.
Xu, L. & Baldocchi, D. D. Seasonal trends in photosynthetic parameters and stomatal conductance of blue oak (Quercus douglasii) under prolonged summer drought and high temperature. Tree Physiol. 23, 865–877 (2003).
PubMed  Google Scholar 

56.
Stocker, B. D. et al. Quantifying soil moisture impacts on light use efficiency across biomes. New Phytol. 218, 1430–1449 (2018).
PubMed  PubMed Central  Google Scholar 

57.
Fisher, J. B., Whittaker, R. J. & Malhi, Y. ET come home: potential evapotranspiration in geographical ecology. Glob. Ecol. Biogeogr. 20, 1–18 (2011).
Google Scholar 

58.
Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).
CAS  PubMed  Google Scholar 

59.
Medlyn, B. E. et al. Temperature response of parameters of a biochemically based model of photosynthesis. II. A review of experimental data. Plant Cell Environ. 25, 1167–1179 (2002).
CAS  Google Scholar  More

1.
Wall, D. H. et al. Soil Ecology And Ecosystem Services. p. 406 (Oxford University Press, 2012).
2.
Blouin, M. et al. A review of earthworm impact on soil function and ecosystem services. Eur. J. Soil Sci. 64, 161–182 (2013).
Google Scholar 

3.
Baveye, P. C., Baveye, J. & Gowdy, J. Soil ‘Ecosystem’ services and natural capital: critical appraisal of research on uncertain ground. Front. Environ. Sci. Eng. China 4, 1–49 (2016).
Google Scholar 

4.
Bardgett, R. D. & van der Putten, W. H. Belowground biodiversity and ecosystem functioning. Nature 515, 505–511 (2014).
ADS  CAS  PubMed  Google Scholar 

5.
Heemsbergen & Hal, V. Biodiversity effects on soil processes explained by interspecific functional dissimilarity biodiversity effects on soil processes explained by interspecific. Science 306, 8–10 (2004).
Google Scholar 

6.
Reich, P. B. et al. Impacts of biodiversity loss escalate through time as redundancy fades. Science 336, 589–592 (2012).
ADS  CAS  PubMed  Google Scholar 

7.
Schuldt, A. et al. Biodiversity across trophic levels drives multifunctionality in highly diverse forests. Nat. Commun. 9, 2989 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

8.
Risch, A. C. et al. Size-dependent loss of aboveground animals differentially affects grassland ecosystem coupling and functions. Nat. Commun. 9, 3684 (2018).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

9.
Soliveres, S. et al. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. Nature 536, 456–459 (2016).
ADS  CAS  PubMed  Google Scholar 

10.
Maestre, F. T. et al. Increasing aridity reduces soil microbial diversity and abundance in global drylands. Proc. Natl Acad. Sci. USA 112, 201516684 (2015).
Google Scholar 

11.
Manning, P. et al. Redefining ecosystem multifunctionality. Nat. Ecol. Evol. 2, 427–436 (2018).
PubMed  Google Scholar 

12.
Maestre, F. T. et al. Plant species richness and ecosystem multifunctionality in global drylands. Science 335, 214–218 (2012).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

13.
Song, D. et al. Large-scale patterns of distribution and diversity of terrestrial nematodes. Appl. Soil Ecol. 114, 161–169 (2017).
Google Scholar 

14.
Delgado-Baquerizo, M. et al. Palaeoclimate explains a unique proportion of the global variation in soil bacterial communities. Nat. Ecol. Evol. 1, 1339–1347 (2017).
PubMed  Google Scholar 

15.
Pärtel, M., Bennett, J. A. & Zobel, M. Macroecology of biodiversity: disentangling local and regional effects. New Phytol. 211, 404–410 (2016).
PubMed  Google Scholar 

16.
Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).
ADS  PubMed  PubMed Central  Google Scholar 

17.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
ADS  CAS  PubMed  Google Scholar 

18.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

19.
Grace, J. B. et al. Integrative modelling reveals mechanisms linking productivity and plant species richness. Nature 529, 390–393 (2016).
ADS  CAS  PubMed  Google Scholar 

20.
Liang, J. et al. Positive biodiversity-productivity relationship predominant in global forests. Science 354, aaf8957 (2016).

21.
Duffy, J. E., Godwin, C. M. & Cardinale, B. J. Biodiversity effects in the wild are common and as strong as key drivers of productivity. Nature 549, 261–264 (2017).
ADS  CAS  PubMed  Google Scholar 

22.
van der Plas, F. et al. Jack-of-all-trades effects drive biodiversity-ecosystem multifunctionality relationships in European forests. Nat. Commun. 7, 11109 (2016).
ADS  PubMed  PubMed Central  Google Scholar 

23.
van der Plas, F. et al. Continental mapping of forest ecosystem functions reveals a high but unrealised potential for forest multifunctionality. Ecol. Lett. 21, 31–42 (2018).
PubMed  Google Scholar 

24.
Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541 (2016).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

25.
Cameron, E. K. et al. Global gaps in soil biodiversity data. Nat. Ecol. Evol. 2, 1042–1043 (2018).
PubMed  PubMed Central  Google Scholar 

26.
Wetzel, F. T. et al. Unlocking biodiversity data: prioritization and filling the gaps in biodiversity observation data in Europe. Biol. Conserv. 221, 78–85 (2018).
Google Scholar 

27.
Amano, T. & Sutherland, W. J. Four barriers to the global understanding of biodiversity conservation: wealth, language, geographical location and security. Proc. Biol. Sci. 280, 20122649 (2013).
PubMed  PubMed Central  Google Scholar 

28.
Eisenhauer, N., Bonn, A. & Guerra, C. A. Recognizing the quiet extinction of invertebrates. Nat. Commun. 10, 50 (2019).

29.
Pereira, H. M., Navarro, L. M. & Martins, I. S. Global biodiversity change: the bad, the good, and the unknown. Annu. Rev. Environ. Resour. 37, 25–50 (2012).
Google Scholar 

30.
Paleari, S. Is the European Union protecting soil? A critical analysis of Community environmental policy and law. Land Use Policy 64, 163–173 (2017).
Google Scholar 

31.
Meyer, C., Weigelt, P. & Kreft, H. Multidimensional biases, gaps and uncertainties in global plant occurrence information. Ecol. Lett. 19, 992–1006 (2016).
PubMed  Google Scholar 

32.
Costello, M. J., Michener, W. K., Gahegan, M., Zhang, Z.-Q. & Bourne, P. E. Biodiversity data should be published, cited, and peer reviewed. Trends Ecol. Evol. 28, 454–461 (2013).
PubMed  Google Scholar 

33.
Bingham, H. C., Doudin, M. & Weatherdon, L. V. The biodiversity informatics landscape: elements, connections and opportunities. 3, e14059 (2017).

34.
Gibb, H. et al. A global database of ant species abundances. Ecology 98, 883–884 (2017).
PubMed  Google Scholar 

35.
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 551, 457–463 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

36.
Overmann, J., Abt, B. & Sikorski, J. Present and future of culturing bacteria. Annu. Rev. Microbiol. 71, 711–730 (2017).
CAS  PubMed  Google Scholar 

37.
de Groot, R. S., Alkemade, R., Braat, L., Hein, L. & Willemen, L. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272 (2010).
Google Scholar 

38.
Stewart, G. Meta-analysis in applied ecology. Biol. Lett. 6, 78–81 (2010).
PubMed  Google Scholar 

39.
Rillig, M. C. et al. Biodiversity research: data without theory—theory without data. Front. Ecol. Evol. 3, 20 (2015).
ADS  Google Scholar 

40.
Coleman, D. C., Callaham, M. A. & Crossley, D. A., Jr. Fundamentals of Soil Ecology. (Academic Press, 2017).

41.
Lavelle, P. & Spain, A. Soil Ecology. (Springer Science & Business Media, 2001).

42.
Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).
Google Scholar 

43.
Phillips, H. R. P. et al. 2019. Global distribution of earthworm diversity. Science https://doi.org/10.1126/science.aax4851 (2019).

44.
van den Hoogen J., et al. Soil nematode abundance and functional group composition at a global scale. Nature https://doi.org/10.1038/s41586-019-1418-6 (2019).

45.
Tedersoo, L. et al. Global diversity and geography of soil fungi. Science 346, 1256688 (2014).
PubMed  Google Scholar 

46.
Orgiazzi, A. et al. Global Soil Biodiversity Atlas (JRC and the Global Soil Biodiversity Initiative, 2016).

47.
Guenard, B., Weiser, M. D. & Gomez, K. The Global Ant Biodiversity Informatics (GABI) database: synthesizing data on the geographic distribution of ant species (Hymenoptera: Formicidae). Myrmecol. News 24, 83–89 (2017).

48.
Nielsen, U. N. et al. The enigma of soil animal species diversity revisited: the role of small-scale heterogeneity. PLoS ONE 5, e11567 (2010).
ADS  PubMed  PubMed Central  Google Scholar 

49.
Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Biol. 42, S3–S15 (2006).

50.
Evans, T. A., Dawes, T. Z., Ward, P. R. & Lo, N. Ants and termites increase crop yield in a dry climate. Nat. Commun. 2, 262–267 (2011).
ADS  PubMed  PubMed Central  Google Scholar 

51.
Eisenhauer, N., Bowker, M. A., Grace, J. B. & Powell, J. R. From patterns to causal understanding: Structural equation modeling (SEM) in soil ecology. Pedobiologia 58, 65–72 (2015).
Google Scholar 

52.
Isbell, F. et al. Biodiversity increases the resistance of ecosystem productivity to climate extremes. Nature 526, 574–577 (2015).
ADS  CAS  PubMed  Google Scholar 

53.
Craven, D. et al. Multiple facets of biodiversity drive the diversity-stability relationship. Nat. Ecol. Evol. 2, 1579–1587 (2018).
PubMed  Google Scholar 

54.
Fraser, L. H. et al. Plant ecology. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science 349, 302–305 (2015).
ADS  CAS  PubMed  Google Scholar 

55.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

56.
Borgman, C. L., Wallis, J. C. & Enyedy, N. Little science confronts the data deluge: habitat ecology, embedded sensor networks, and digital libraries. Int. J. Digit. Lib. 7, 17–30 (2007).
Google Scholar 

57.
Hampton, S. E. et al. Big data and the future of ecology. Front. Ecol. Environ. 11, 156–162 (2013).
Google Scholar 

58.
Pey, B. et al. Current use of and future needs for soil invertebrate functional traits in community ecology. Basic Appl. Ecol. 15, 194–206 (2014).
Google Scholar 

59.
Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Chang. Biol. 21, 973–985 (2015).
ADS  PubMed  Google Scholar 

60.
Blankinship, J. C., Niklaus, P. A. & Hungate, B. A. A meta-analysis of responses of soil biota to global change. Oecologia 165, 553–565 (2011).
ADS  PubMed  Google Scholar 

61.
Wheeler, Q. D., Raven, P. H. & Wilson, E. O. Taxonomy: impediment or expedient? Science 303, 285 (2004).
CAS  PubMed  Google Scholar 

62.
Delgado-Baquerizo, M. & Eldridge, D. J. Cross-biome drivers of soil bacterial alpha diversity on a worldwide scale. Ecosystems 22, 1–12 (2019).
Google Scholar 

63.
Hursh, A. et al. The sensitivity of soil respiration to soil temperature, moisture, and carbon supply at the global scale. Glob. Chang. Biol. 23, 2090–2103 (2017).
ADS  PubMed  Google Scholar 

64.
Wang, Q., Liu, S. & Tian, P. Carbon quality and soil microbial property control the latitudinal pattern in temperature sensitivity of soil microbial respiration across Chinese forest ecosystems. Glob. Chang. Biol. 24, 2841–2849 (2018).
ADS  PubMed  Google Scholar 

65.
Prăvălie, R. Drylands extent and environmental issues. A global approach. Earth-Sci. Rev. 161, 259–278 (2016).
ADS  Google Scholar 

66.
Delgado-baquerizo, M. et al. A global atlas of the dominant bacteria found in soil. Science 325, 320–325 (2018).
ADS  Google Scholar 

67.
Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. (2018).

68.
Cowan, D. A. et al. Microbiomics of Namib Desert habitats. Extremophiles 24, 17–29 (2020).
CAS  PubMed  Google Scholar 

69.
Rutgers, M. et al. Mapping earthworm communities in Europe. Appl. Soil Ecol. 97, 98–111 (2016).
Google Scholar 

70.
Delgado-Baquerizo, M. et al. Ecological drivers of soil microbial diversity and soil biological networks in the Southern Hemisphere. Ecology 99, 583–596 (2018).
PubMed  Google Scholar 

71.
Chen, S., Zou, J., Hu, Z., Chen, H. & Lu, Y. Global annual soil respiration in relation to climate, soil properties and vegetation characteristics: summary of available data. Agric. For. Meteorol. 198–199, 335–346 (2014).
ADS  Google Scholar 

72.
Zhang, D., Hui, D., Luo, Y. & Zhou, G. Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors. J. Plant Ecol. 1, 85–93 (2008).
Google Scholar 

73.
Kreft, H. & Jetz, W. Global patterns and determinants of vascular plant diversity. Proc. Natl Acad. Sci. USA 104, 5925–5930 (2007).
ADS  CAS  PubMed  Google Scholar 

74.
Cameron, E. et al. Global mismatches in aboveground and belowground biodiversity. Conserv. Biol. 33, 1187–1192 (2019)

75.
Scherber, C. et al. Bottom-up effects of plant diversity on multitrophic interactions in a biodiversity experiment. Nature 468, 553–556 (2010).
ADS  CAS  PubMed  Google Scholar 

76.
Eisenhauer, N. et al. Plant diversity effects on soil food webs are stronger than those of elevated CO2 and N deposition in a long-term grassland experiment. Proc. Natl Acad. Sci. USA 110, 6889–6894 (2013).
ADS  CAS  PubMed  Google Scholar 

77.
Lange, M. et al. Plant diversity increases soil microbial activity and soil carbon storage. Nat. Commun. 6, 6707 (2015).
ADS  CAS  PubMed  Google Scholar 

78.
Menegotto, A. & Rangel, T. F. Mapping knowledge gaps in marine diversity reveals a latitudinal gradient of missing species richness. Nat. Commun. 9, 4713 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

79.
Eisenhauer, N. et al. Priorities for research in soil ecology. Pedobiologia 63, 1–7 (2017).
PubMed  PubMed Central  Google Scholar 

80.
Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
PubMed  PubMed Central  Google Scholar 

81.
Dirzo, R. et al. Defaunation in the Anthropocene. Science 345, 401–406 (2014).
ADS  CAS  Google Scholar 

82.
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 489, 326–326 (2012).
ADS  CAS  Google Scholar 

83.
Titeux, N. et al. Biodiversity scenarios neglect future land-use changes. Glob. Chang. Biol. 22, 2505–2515 (2016).
ADS  PubMed  Google Scholar 

84.
Popp, A. et al. Land-use futures in the shared socio-economic pathways. Glob. Environ. Change 42, 331–345 (2017).
Google Scholar 

85.
Dai, A. Increasing drought under global warming in observations and models. Nat. Clim. Chang. 3, 52 (2012).
ADS  Google Scholar 

86.
Kharin, V. V., Zwiers, F. W., Zhang, X. & Hegerl, G. C. Changes in temperature and precipitation extremes in the IPCC ensemble of global coupled model simulations. J. Clim. 20, 1419–1444 (2007).
ADS  Google Scholar 

87.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

88.
Bronselaer, B. et al. Change in future climate due to Antarctic meltwater. Nature 564, 53–58 (2018).
ADS  CAS  PubMed  Google Scholar 

89.
Collins, M., Knutti, R., Arblaster, J., Dufresne, J. L. & Fichefet, T. Long-term climate change: projections, commitments and irreversibility. Chapter 12 (eds T. Stocker et al) 1029–1136 (Cambridge University Press, 2013).

90.
Delgado-Baquerizo, M., Eldridge, D. J., Hamonts, K. & Singh, B. K. Ant colonies promote the diversity of soil microbial communities. ISME J. https://doi.org/10.1038/s41396-018-0335-2 (2019).

91.
Wardle, D. A. et al. Ecological linkages between aboveground and belowground biota. Science 304, 1629–1633 (2004).
ADS  CAS  PubMed  Google Scholar 

92.
Thomson, S. A. et al. Taxonomy based on science is necessary for global conservation. PLoS Biol. 16, e2005075 (2018).
PubMed  PubMed Central  Google Scholar 

93.
Drew, L. W. Are we losing the science of taxonomy? Bioscience 61, 942–946 (2011).
Google Scholar 

94.
Paknia, O., Sh., H. R. & Koch, A. Lack of well-maintained natural history collections and taxonomists in megadiverse developing countries hampers global biodiversity exploration. Organ. Divers. Evol. 15, 619–629 (2015).
Google Scholar 

95.
Prathapan, K. D. et al. When the cure kills-CBD limits biodiversity research. Science 360, 1405–1406 (2018).
ADS  CAS  PubMed  Google Scholar 

96.
Neumann, D. et al. Global biodiversity research tied up by juridical interpretations of access and benefit sharing. Org. Divers. Evol. 18, 1–12 (2017).
Google Scholar 

97.
Leimu, R. & Koricheva, J. What determines the citation frequency of ecological papers? Trends Ecol. Evol. 20, 28–32 (2005).
PubMed  Google Scholar 

98.
Hugerth, L. W. & Andersson, A. F. Analysing microbial community composition through amplicon sequencing: from sampling to hypothesis testing. Front. Microbiol. 8, 1561 (2017).
PubMed  PubMed Central  Google Scholar 

99.
Terrat, S. et al. Meta-barcoded evaluation of the ISO standard 11063 DNA extraction procedure to characterize soil bacterial and fungal community diversity and composition. Microb. Biotechnol. 8, 131–142 (2015).
CAS  PubMed  Google Scholar 

100.
Kõljalg, U., Larsson, K. H. & Abarenkov, K. UNITE: a database providing web‐based methods for the molecular identification of ectomycorrhizal fungi. New Phytol 166, 1063–1068 (2005).

101.
Mathieu, J., Caro, G. & Dupont, L. Methods for studying earthworm dispersal. Appl. Soil Ecol. 123, 339–344 (2018).
Google Scholar 

102.
Pauchard, N. Access and benefit sharing under the convention on biological diversity and its protocol: what can some numbers tell us about the effectiveness of the regulatory regime? Resources 6, 11 (2017).
Google Scholar 

103.
Saha, S., Saha, S. & Saha, S. K. Barriers in Bangladesh. Elife 7, e41926 (2018).

104.
Prathapan, K. D. & Rajan, P. D. Biodiversity access and benefit-sharing: weaving a rope of sand. Curr. Sci. 100, 290–293 (2011).
Google Scholar 

105.
van der Linde, S. et al. Environment and host as large-scale controls of ectomycorrhizal fungi. Nature 558, 243–248 (2018).
ADS  PubMed  Google Scholar 

106.
Terrat, S. et al. Mapping and predictive variations of soil bacterial richness across France. PLoS ONE 12, e0186766 (2017).
PubMed  PubMed Central  Google Scholar 

107.
Makiola, A., et al. Key questions for next-generation biomonitoring. Front. Environ. Sci. 7, 197 (2020).

108.
Maestre, F. T. & Eisenhauer, N. Recommendations for establishing global collaborative networks in soil ecology. Soil Organ. 91, 73–85 (2019).
Google Scholar 

109.
Phillips, H. R. P. et al. Red list of a black box. Nat. Ecol. Evol. 1, 0103 (2017).
Google Scholar 

110.
Davison, J. et al. Microbial island biogeography: isolation shapes the life history characteristics but not diversity of root-symbiotic fungal communities. ISME J. https://doi.org/10.1038/s41396-018-0196-8 (2018).

111.
Overmann, J. Significance and future role of microbial resource centers. Syst. Appl. Microbiol. 38, 258–265 (2015).
PubMed  Google Scholar 

112.
Overmann, J. & Scholz, A. H. Microbiological research under the nagoya protocol: facts and fiction. Trends Microbiol. 25, 85–88 (2017).
CAS  PubMed  Google Scholar 

113.
Bockmann, F. A. et al. Brazil’s government attacks biodiversity. Science 360, 865 (2018).
ADS  CAS  PubMed  Google Scholar 

114.
Scbd-Unep. Nagoya Declaration on Biodiversity in Development Cooperation. 2 (UNEP, 2010).

115.
Perrings, C. et al. Ecosystem services for 2020. Science 330, 323–324 (2010).
ADS  CAS  PubMed  Google Scholar 

116.
Bond-Lamberty, B. & Thomson, A. A global database of soil respiration data. Biogeosci. Discuss. 7, 1321–1344 (2010).
ADS  Google Scholar 

117.
Bamforth, S. S. Interpreting soil ciliate biodiversity. Plant Soil 170, 159–164 (1995).
CAS  Google Scholar 

118.
Mathieu, J. EGrowth: a global database on intraspecific body growth variability in earthworm. Soil Biol. Biochem. 122, 71–80 (2018).
CAS  Google Scholar 

119.
Fierer, N., Strickland, M. S., Liptzin, D., Bradford, M. A. & Cleveland, C. C. Global patterns in belowground communities. Ecol. Lett. 12, 1238–1249 (2009).
PubMed  Google Scholar 

120.
Nelson, M. B., Martiny, A. C. & Martiny, J. B. H. Global biogeography of microbial nitrogen-cycling traits in soil. Proc. Natl Acad. Sci. USA 113, 8033–8040 (2016).
CAS  PubMed  Google Scholar 

121.
Chen, J., Yang, S. T., Li, H. W., Zhang, B. & Lv, J. R. Research on geographical environment unit division based on the method of natural breaks (Jenks). ISPRS – International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XL-4/W3, pp. 47–50 (2013).

122.
Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).
PubMed  PubMed Central  Google Scholar 

123.
Rousseeuw, P. J. & van Zomeren, B. C. Unmasking multivariate outliers and leverage points. J. Am. Stat. Assoc. 85, 633–639 (1990).
Google Scholar 

124.
Jackson, D. A. & Chen, Y. Robust principal component analysis and outlier detection with ecological data. Environmetrics 15, 129–139 (2004).
Google Scholar 

125.
Mallavan B. P., Minasny B., McBratney A. B., in Digital Soil Mapping. pp. 137–150 (Springer, Dordrecht, 2010).

126.
Chao, A. & Jost, L. Coverage-based rarefaction and extrapolation: standardizing samples by completeness rather than size. Ecology 93, 2533–2547 (2012).
PubMed  Google Scholar 

127.
Hengl, T. et al. SoilGrids1km–global soil information based on automated mapping. PLoS ONE 9, e105992 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

128.
Trabucco, A., Zomer, R. J., Bossio, D. A., van Straaten, O. & Verchot, L. V. Climate change mitigation through afforestation/reforestation: a global analysis of hydrologic impacts with four case studies. Agric. Ecosyst. Environ. 126, 81–97 (2008).
Google Scholar 

129.
Karger, D. N. et al. Climatologies at high resolution for the Earth land surface areas. Sci. Data 4, 1–19 (2017).
Google Scholar 

130.
Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) | The Long Term Archive. Available at: https://lta.cr.usgs.gov/GMTED2010. Accessed on 6 December 2018.

131.
European Space Agency. ESA – Land Cover CCI – Product User Guide Version 2.0. (2017).

132.
Frostegård, Å., Tunlid, A. & Bååth, E. Microbial biomass measured as total lipid phosphate in soils of different organic content. J. Microbiol. Methods 14, 151–163 (1991).
Google Scholar 

133.
Campbell, C. D., Chapman, S. J., Cameron, C. M., Davidson, M. S. & Potts, J. M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).
CAS  PubMed  PubMed Central  Google Scholar 

134.
Eppo, P. M. Nematode extraction. EPPO Bull. 43, 471–495 (2013).
Google Scholar 

135.
ISO/FDIS. Soil Quality – Sampling Of Soil Invertebrates – Part 1: Hand-sorting And Extraction Of Earthworms. (ISO, 2018).

136.
ISO. Soil quality – Sampling Of Soil Invertebrates – Part 4: Sampling, Extraction And Identification Of Soil-Inhabiting Nematodes. (ISO, 09-2011).

137.
Hunter, P. A. DEAL for open access: The negotiations between the German DEAL project and publishers have global implications for academic publishing beyond just Germany. EMBO Rep. 19, e46317 (2018).

138.
Knapp, A. K. et al. Past, present, and future roles of long-term experiments in the LTER network. Bioscience 62, 377–389 (2012).

139.
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
ADS  CAS  PubMed  Google Scholar 

140.
Ramirez, K. S. et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat. Microbiol. 3, 1–8 (2017).
Google Scholar 

141.
Leff, J. W. et al. Consistent responses of soil microbial communities to elevated nutrient inputs in grasslands across the globe. Proc. Natl Acad. Sci. USA 112, 10967–10972 (2015).
ADS  CAS  PubMed  Google Scholar 

142.
Gilbert, J. A., Jansson, J. K. & Knight, R. The earth microbiome project: successes and aspirations. BMC Biol. 12, 69 (2014).
PubMed  PubMed Central  Google Scholar 

143.
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl Acad. Sci. USA 103, 626–631 (2006).
ADS  CAS  PubMed  Google Scholar 

144.
Darcy, J. L., Lynch, R. C., King, A. J., Robeson, M. S. & Schmidt, S. K. Global distribution of Polaromonas phylotypes–evidence for a highly successful dispersal capacity. PLoS ONE 6, e23742 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

145.
Hendershot, J. N., Read, Q. D., Henning, J. A., Sanders, N. J. & Classen, A. T. Consistently inconsistent drivers of microbial diversity and abundance at macroecological scales. Ecology 98, 1757–1763 (2017).
PubMed  Google Scholar 

146.
Locey, K. J. & Lennon, J. T. Scaling laws predict global microbial diversity. Proc. Natl Acad. Sci. USA 113, 5970–5975 (2016).
ADS  CAS  PubMed  Google Scholar 

147.
Lozupone, C. A. & Knight, R. Global patterns in bacterial diversity. Proc. Natl Acad. Sci. USA 104, 11436–11440 (2007).
ADS  CAS  PubMed  Google Scholar 

148.
Neal, A. L. et al. Phylogenetic distribution, biogeography and the effects of land management upon bacterial non-specific Acid phosphatase Gene diversity and abundance. Plant Soil 427, 175–189 (2018).
CAS  PubMed  Google Scholar 

149.
Shoemaker, W. R., Locey, K. J. & Lennon, J. T. A macroecological theory of microbial biodiversity. Nat. Ecol. Evol. 1, 107 (2017).
PubMed  Google Scholar 

150.
Bates, S. T. et al. Examining the global distribution of dominant archaeal populations in soil. ISME J. 5, 908–917 (2011).
MathSciNet  CAS  PubMed  Google Scholar 

151.
Davison, J. et al. Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science 349, 970–973 (2015).
ADS  CAS  PubMed  Google Scholar 

152.
Kivlin, S. N., Hawkes, C. V. & Treseder, K. K. Global diversity and distribution of arbuscular mycorrhizal fungi. Soil Biol. Biochem. 43, 2294–2303 (2011).
CAS  Google Scholar 

153.
Pärtel, M. et al. Historical biome distribution and recent human disturbance shape the diversity of arbuscular mycorrhizal fungi. New Phytol. 216, 227–238 (2017).
PubMed  Google Scholar 

154.
Põlme, S. et al. Biogeography of ectomycorrhizal fungi associated with alders (Alnus spp.) in relation to biotic and abiotic variables at the global scale. New Phytol. 198, 1239–1249 (2013).
PubMed  Google Scholar 

155.
Sharrock, R. A. et al. A global assessment using PCR techniques of mycorrhizal fungal populations colonising Tithonia diversifolia. Mycorrhiza 14, 103–109 (2004).
CAS  PubMed  Google Scholar 

156.
Tedersoo, L. et al. Towards global patterns in the diversity and community structure of ectomycorrhizal fungi. Mol. Ecol. 21, 4160–4170 (2012).
PubMed  Google Scholar 

157.
Öpik, M., Moora, M., Liira, J. & Zobel, M. Composition of root-colonizing arbuscular mycorrhizal fungal communities in different ecosystems around the globe: arbuscular mycorrhizal fungal communities around the globe. J. Ecol. 94, 778–790 (2006).
Google Scholar 

158.
Öpik, M. et al. The online database MaarjAM reveals global and ecosystemic distribution patterns in arbuscular mycorrhizal fungi (Glomeromycota). New Phytol. 188, 223–241 (2010).
PubMed  Google Scholar 

159.
Stürmer, S. L., Bever, J. D. & Morton, J. B. Biogeography of arbuscular mycorrhizal fungi (Glomeromycota): a phylogenetic perspective on species distribution patterns. Mycorrhiza 28, 587–603 (2018).
PubMed  Google Scholar 

160.
Bates, S. T. et al. Global biogeography of highly diverse protistan communities in soil. ISME J. 7, 652–659 (2013).
CAS  PubMed  Google Scholar 

161.
Lara, E., Roussel‐Delif, L. & Fournier, B. Soil microorganisms behave like macroscopic organisms: patterns in the global distribution of soil euglyphid testate amoebae. J. Biogeogr. 43, 520–532 (2016).

162.
Finlay, B. J., Esteban, G. F., Clarke, K. J. & Olmo, J. L. Biodiversity of terrestrial protozoa appears homogeneous across local and global spatial scales. Protist 152, 355–366 (2001).
CAS  PubMed  Google Scholar 

163.
Chao, A., C. Li, P., Agatha, S. & Foissner, W. A statistical approach to estimate soil ciliate diversity and distribution based on data from five continents. Oikos 114, 479–493 (2006).

164.
Foissner, W. Global soil ciliate (Protozoa, Ciliophora) diversity: a probability-based approach using large sample collections from Africa, Australia and Antarctica. Biodivers. Conserv. 6, 1627–1638 (1997).
Google Scholar 

165.
Nielsen, U. N. et al. Global-scale patterns of assemblage structure of soil nematodes in relation to climate and ecosystem properties: Global-scale patterns of soil nematode assemblage structure. Glob. Ecol. Biogeogr. 23, 968–978 (2014).
Google Scholar 

166.
Wu, T., Ayres, E., Bardgett, R. D., Wall, D. H. & Garey, J. R. Molecular study of worldwide distribution and diversity of soil animals. Proc. Natl Acad. Sci. USA 108, 17720–17725 (2011).
ADS  CAS  PubMed  Google Scholar 

167.
Robeson, M. S. et al. Soil rotifer communities are extremely diverse globally but spatially autocorrelated locally. Proc. Natl Acad. Sci. USA 108, 4406–4410 (2011).
ADS  CAS  PubMed  Google Scholar 

168.
Wall, D. H. et al. Global decomposition experiment shows soil animal impacts on decomposition are climate-dependent. Glob. Chang. Biol. 14, 2661–2677 (2008).

169.
Pachl, P. et al. The tropics as an ancient cradle of oribatid mite diversity. Acarologia 57, 309–322 (2016).
Google Scholar 

170.
Dahlsjö, C. A. L. et al. First comparison of quantitative estimates of termite biomass and abundance reveals strong intercontinental differences. J. Trop. Ecol. 30, 143–152 (2014).
Google Scholar 

171.
Briones, M. J. I., Ineson, P. & Heinemeyer, A. Predicting potential impacts of climate change on the geographical distribution of enchytraeids: a meta‐analysis approach. Glob. Chang. Biol. 13, 2252–2269 (2007).

172.
Silver, W. L. & Miya, R. K. Global patterns in root decomposition: comparisons of climate and litter quality effects. Oecologia 129, 407–419 (2001).
ADS  PubMed  Google Scholar 

173.
Zhang, T. ’an, Chen, H. Y. H. & Ruan, H. Global negative effects of nitrogen deposition on soil microbes. ISME J. 12, 1817–1825 (2018).
CAS  PubMed  PubMed Central  Google Scholar 

174.
Sinsabaugh, R. L., Turner, B. L. & Talbot, J. M. Stoichiometry of microbial carbon use efficiency in soils. Ecol. Monogr. 86, 172–189 (2016).

175.
Xu, M. & Shang, H. Contribution of soil respiration to the global carbon equation. J. Plant Physiol. 203, 16–28 (2016).
CAS  PubMed  Google Scholar 

176.
Raich, J. W. & Tufekciogul, A. Vegetation and soil respiration: correlations and controls. Biogeochemistry 48, 71–90 (2000).
CAS  Google Scholar 

177.
Wang, J., Chadwick, D. R., Cheng, Y. & Yan, X. Global analysis of agricultural soil denitrification in response to fertilizer nitrogen. Sci. Total Environ. 616-617, 908–917 (2018).
ADS  CAS  PubMed  Google Scholar 

178.
Rahmati, M. et al. Development and analysis of the Soil Water Infiltration Global database. Earth Syst. Sci. Data 10, 1237–1263 (2017).
ADS  Google Scholar 

179.
Serna-Chavez, H. M., Fierer, N. & van Bodegom, P. M. Global drivers and patterns of microbial abundance in soil: Global patterns of soil microbial biomass. Glob. Ecol. Biogeogr. 22, 1162–1172 (2013).
Google Scholar 

180.
Howison, R. A., Olff, H., Koppel, J. & Smit, C. Biotically driven vegetation mosaics in grazing ecosystems: the battle between bioturbation and biocompaction. Ecol. Monogr. 87, 363–378 (2017).

181.
Lehmann, A., Zheng, W. & Rillig, M. C. Soil biota contributions to soil aggregation. Nat. Ecol. Evol.1, 1–9 (2017).
Google Scholar 

182.
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).
Google Scholar 

183.
van Straaten Oliver, Z. R. T. A. & Bossio, D. Carbon, Land And Water: A Global Analysis Of The Hydrologic Dimensions Of Climate Change Mitigation Through Afforestation/reforestation. (IWMI, 2006). More

1.
van Valen, L. M. Resetting the Phanerozoic community evolution. Nature 307, 93–106 (1984).
Google Scholar 
2.
Alroy, J. Dynamics of origination and extinction in the marine fossil record. Proc. Natl Acad. Sci. USA 105, 11536–11542 (2008).
CAS  PubMed  Google Scholar 

3.
Foote, M. Origination and extinction through the Phanerozoic: a new approach. J. Geol. 111, 125–148 (2003).
Google Scholar 

4.
Gilinsky, N. L. Volatility and the Phanerozoic decline of background extinction intensity. Paleobiology 20, 445–458 (1994).
Google Scholar 

5.
Lieberman, B. S. & Melott, A. L. Declining volatility, a general property of disparate systems: from fossils, to stocks, to the stars. Palaeontology 56, 1297–1304 (2013).
Google Scholar 

6.
Knope, M. L., Bush, A. M., Frishkoff, L. O., Heim, N. A. & Payne, J. L. Ecologically diverse clades dominate the oceans via extinction resistance. Science 367, 1035–1038 (2020).
CAS  PubMed  Google Scholar 

7.
Janzen, D. H. On ecological fitting. Oikos 45, 308–310 (1985).
Google Scholar 

8.
Agosta, S. J. & Klemens, J. A. Ecological fitting by phenotypically flexible genotypes: implications for species associations, community assembly and evolution. Ecol. Lett. 11, 1123–1134 (2008).
PubMed  Google Scholar 

9.
Nielsen, S. N. & Müller, F. in Handbook of Ecosystem Theories and Management (eds Jørgensen, S. E. & Müller, F.) 195–216 (CRC Press, 2000).

10.
Hui, C. et al. Defining invasiveness and invasibility in ecological networks. Biol. Invasions 18, 971–983 (2016).
Google Scholar 

11.
Foote, M. et al. Rise and fall of species occupancy in Cenozoic fossil mollusks. Science 318, 1131–1134 (2007).
CAS  PubMed  Google Scholar 

12.
Foote, M. Symmetric waxing and waning of marine invertebrate genera. Paleobiology 33, 517–529 (2007).
Google Scholar 

13.
Liow, L. H. & Stenseth, N. C. The rise and fall of species: implications for macroevolutionary and macroecological studies. Proc. R. Soc. Lond. B 274, 2745–2752 (2007).
Google Scholar 

14.
Zliobaite, I., Fortelius, M. & Stenseth, N. C. Reconciling taxon senescence with the Red Queen’s hypothesis. Nature 552, 92–95 (2017).
CAS  PubMed  Google Scholar 

15.
Gillespie, R. G. et al. Comparing adaptive radiations across space, time, and taxa. J. Hered. 111, 1–20 (2020).
PubMed  Google Scholar 

16.
Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism: American Society of Naturalists EO Wilson Award address. Am. Nat. 175, 623–639 (2010).
PubMed  Google Scholar 

17.
Yoder, J. B. et al. Ecological opportunity and the origin of adaptive radiations. J. Evol. Biol. 23, 1581–1596 (2010).
CAS  PubMed  Google Scholar 

18.
Erwin, D. H. Novelty and innovation in the history of life. Curr. Biol. 25, R930–R940 (2015).
CAS  PubMed  Google Scholar 

19.
Gould, S. J. & Vrba, E. S. Exaptation—a missing term in the science of form. Paleobiology 8, 4–15 (1982).
Google Scholar 

20.
Cooper, A. & Fortey, R. Evolutionary explosions and the phylogenetic fuse. Trends Ecol. Evol. 13, 151–156 (1998).
CAS  PubMed  Google Scholar 

21.
Jablonski, D. & Bottjer, D. J. in Major Evolutionary Radiations (eds Taylor, P. D. & Larwood, G. P.) 17–57 (Systematics Association, 1990).

22.
Jablonski, D. Approaches to macroevolution: 1. general concepts and origin of variation. Evol. Biol. 44, 427–450 (2017).
PubMed  PubMed Central  Google Scholar 

23.
Uyeda, J. C., Hansen, T. F., Arnold, S. J. & Pienaar, J. The million-year wait for macroevolutionary bursts. Proc. Natl Acad. Sci. USA 108, 15908–15913 (2011).
CAS  PubMed  Google Scholar 

24.
Kröger, B., Desrochers, A. & Ernst, A. The reengineering of reef habitats during the Great Ordovician Biodiversification Event. PALAIOS 32, 584–599 (2017).
Google Scholar 

25.
Robeck, H. E., Maley, C. C. & Donoghue, M. J. Taxonomy and temporal diversity patterns. Paleobiology 26, 171–187 (2000).
Google Scholar 

26.
Hendricks, J. R., Saupe, E. E., Myers, C. E., Hermsen, E. J. & Allmon, W. D. The generification of the fossil record. Paleobiology 40, 511–528 (2014).
Google Scholar 

27.
Wagner, P. J., Aberhan, M., Hendy, A. & Kiessling, W. The effects of taxonomic standardization on sampling-standardized estimates of historical diversity. Proc. R. Soc. B 274, 439–444 (2007).
PubMed  Google Scholar 

28.
Plotnick, R. E. & Wagner, P. J. Roundup of the usual suspects: common genera in the fossil record and the nature of the wastebasket taxa. Paleobiology 32, 126–146 (2006).
Google Scholar 

29.
Bambach, R. K., Bush, M. A. & Erwin, D. H. Autecology and the filling of ecospace: key metazoan radiations. Palaeontology 50, 1–22 (2007).
Google Scholar 

30.
Knope, M. L., Heim, N. A., Frishkoff, L. O. & Payne, J. L. Limited role of functional differentiation in early diversification of animals. Nat. Commun. 6, 6455 (2015).
CAS  PubMed  PubMed Central  Google Scholar 

31.
Sepkoski, J. J. A kinetic model of Phanerozoic taxonomic diversity. III Post-Paleozoic families and mass extinctions. Paleobiology 10, 246–267 (1984).
Google Scholar 

32.
Alroy, J. The shifting balance of diversity among major marine animal groups. Science 329, 1191–1194 (2010).
CAS  PubMed  Google Scholar 

33.
Liow, L. H. & Nichols, J. D. in The Paleontological Society Short Course, October 30th 2010 (eds Alroy, J. & Hunt, G.) 81–94 (Cambridge Univ. Press, 2010).

34.
Kiessling, W. & Kocsis, Á. T. Adding fossil occupancy trajectories to the assessment of modern extinction risk. Biol. Lett. 12, 20150813 (2016).
PubMed  PubMed Central  Google Scholar 

35.
Fridley, J. D., Vandermast, D. B., Kuppinger, D. M., Manthey, M. L. & Peet, R. K. Co-occurrence based assessment of habitat generalists and specialists: a new approach for the measurement of niche width. J. Ecol. 95, 707–722 (2007).
Google Scholar 

36.
Hofmann, R., Tietje, M. & Aberhan, M. Diversity partitioning in Phanerozoic benthic marine communities. Proc. Natl Acad. Sci. USA 116, 79–83 (2019).
PubMed  Google Scholar 

37.
Bottjer, D. J., Hagadorn, J. W. & Dornbos, S. Q. The Cambrian substrate revolution. GSA Today 10, 1–7 (2000).
Google Scholar 

38.
Knoll, A. H. & Follows, M. J. A bottom-up perspective on ecosystem change in Mesozoic oceans. Proc. R. Soc. B 283, 20161755 (2016).
PubMed  Google Scholar 

39.
Bambach, R. K. Seafood through time: changes in biomass, energetics, and productivity in the marine ecosystem. Paleobiology 19, 372–397 (1993).
Google Scholar 

40.
Westrop, S. R. The life habits of the Ordovician illaenine trilobite Bumastoides. Lethaia 16, 15–24 (1983).
Google Scholar 

41.
O’Dea, A. & Jackson, J. Environmental change drove macroevolution in cupuladriid bryozoans. Proc. R. Soc. B 276, 3629–3634 (2009).
PubMed  Google Scholar 

42.
Rasmussen, C. M. Ø., Kröger, B., Nielsen, M. L. & Colmenar, J. Cascading trend of Early Paleozoic marine radiations paused by Late Ordovician extinctions. Proc. Natl Acad. Sci. USA 116, 7207 (2019).
PubMed  Google Scholar 

43.
Bush, A. M. & Bambach, R. K. Sustained Mesozoic–Cenozoic diversification of marine Metazoa: a consistent signal from the fossil record. Geology 43, 979–982 (2015).
Google Scholar 

44.
Leibold, M. A. & McPeek, M. A. Coexistence of the niche and neutral perspectives in community ecology. Ecology 87, 1399–1410 (2006).
PubMed  Google Scholar 

45.
McPeek, M. A. The ecological dynamics of clade diversification and community assembly. Am. Nat. 172, E270–E284 (2008).
PubMed  Google Scholar 

46.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Google Scholar 

47.
Wagner, P. J., Aberhan, M., Hendy, A. & W, K. The effects of taxonomic standardization on occurrence-based estimates of diversity. Proc. R. Soc. Lond. B 274, 439–444 (2007).
Google Scholar 

48.
Cohen, K. M., Harper, D. A. T. & Gibbard, P. L. ICS International Chronostratigraphic Chart 2018/08 (International Commission on Stratigraphy, IUGS, 2018); www.stratigraphy.org

49.
Nichols, J. D. & Pollock, K. H. Estimating taxonomic diversity, extinction rates, and speciation rates from fossil data using capture–recapture models. Paleobiology 9, 150–163 (1983).
Google Scholar 

50.
Connolly, S. R. & Miller, A. I. Joint estimation of sampling and turnover rates from fossil databases: capture–mark–recapture methods revisited. Paleobiology 27, 767–751 (2001).
Google Scholar 

51.
Schwarz, C. J. & Arnason, A. N. A general methodology for the analysis of capture–recapture experiments in open populations. Biometrics 52, 860–873 (1996).
Google Scholar 

52.
Pradel, R. Utilization of capture–mark–recapture for the study of recruitment and population growth rate. Biometrics 52, 703–709 (1996).
Google Scholar 

53.
Liow, L. H., Reitan, T. & Harnik, P. G. Ecological interactions on macroevolutionary time scales: clams and brachiopods are more than ships that pass in the night. Ecol. Lett. 18, 1030–1039 (2015).
PubMed  Google Scholar 

54.
Alroy, J. A more precise speciation and extinction rate estimator. Paleobiology 41, 633–639 (2015).
Google Scholar 

55.
Kocsis, Á. T., Reddin, C. J., Alroy, J. & Kiessling, W. The r package divDyn for quantifying diversity dynamics using fossil sampling data. Methods Ecol. Evol. 10, 735–743 (2019).
Google Scholar 

56.
Cornette, J. L. & Lieberman, B. S. Random walks in the history of life. Proc. Natl Acad. Sci. USA 101, 187–191 (2004).
CAS  PubMed  Google Scholar 

57.
Fuller, W. A. Introduction to Statistical Time Series (Wiley, 2009).

58.
Manthey, M. & Fridley, J. D. Beta diversity metrics and the estimation of niche width via species co-occurrence data: reply to Zeleny. J. Ecol. 97, 18–22 (2009).
Google Scholar  More

1.
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
ADS  CAS  PubMed  Google Scholar 
2.
McKinney, M. L. Urbanization, biodiversity, and conservation. Bioscience 52, 883 (2002).
Google Scholar 

3.
Lowry, H., Lill, A. & Wong, B. B. M. Behavioural responses of wildlife to urban environments: behavioural responses to urban environments. Biol. Rev. 88, 537–549 (2013).
PubMed  Google Scholar 

4.
Sih, A. Understanding variation in behavioural responses to human-induced rapid environmental change: a conceptual overview. Anim. Behav. 85, 1077–1088 (2013).
Google Scholar 

5.
Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change: behaviour and evolution. Evol. Appl. 4, 367–387 (2011).
PubMed  PubMed Central  Google Scholar 

6.
Lapiedra, O., Chejanovski, Z. & Kolbe, J. J. Urbanization and biological invasion shape animal personalities. Glob. Change Biol. 23, 592–603 (2017).
ADS  Google Scholar 

7.
Sih, A., Stamps, J., Yang, L. H., McElreath, R. & Ramenofsky, M. Behavior as a key component of integrative biology in a human-altered world. Integr. Comp. Biol. 50, 934–944 (2010).
PubMed  Google Scholar 

8.
Alberti, M. Eco-evolutionary dynamics in an urbanizing planet. Trends Ecol. Evol. 30, 114–126 (2015).
PubMed  Google Scholar 

9.
Sol, D., Lapiedra, O. & González-Lagos, C. Behavioural adjustments for a life in the city. Anim. Behav. 85, 1101–1112 (2013).
Google Scholar 

10.
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).
PubMed  Google Scholar 

11.
Seferta, A., Guay, P.-J., Marzinotto, E. & Lefebvre, L. Learning differences between feral pigeons and zenaida doves: the role of neophobia and human proximity. Ethology 107, 281–293 (2001).
Google Scholar 

12.
Sol, D., Bacher, S., Reader, S. M. & Lefebvre, L. Brain size predicts the success of mammal species introduced into novel environments. Am. Nat. 172, S63–S71 (2008).
PubMed  Google Scholar 

13.
Sol, D., Duncan, R. P., Blackburn, T. M., Cassey, P. & Lefebvre, L. Big brains, enhanced cognition, and response of birds to novel environments. Proc. Natl. Acad. Sci. 102, 5460–5465 (2005).
ADS  CAS  PubMed  Google Scholar 

14.
Webster, S. J. & Lefebvre, L. Problem solving and neophobia in a columbiform–passeriform assemblage in Barbados. Anim. Behav. 62, 23–32 (2001).
Google Scholar 

15.
Lowry, H., Lill, A. & Wong, B. B. M. Tolerance of auditory disturbance by an avian urban adapter, the noisy miner: tolerance of auditory disturbance by an avian urban adapter. Ethology 117, 490–497 (2011).
Google Scholar 

16.
Rodríguez-Prieto, I., Martín, J. & Fernández-Juricic, E. Individual variation in behavioural plasticity: direct and indirect effects of boldness, exploration and sociability on habituation to predators in lizards. Proc. R. Soc. B 278, 266–273 (2011).
PubMed  Google Scholar 

17.
Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).
PubMed  Google Scholar 

18.
Gosling, S. D. From mice to men: what can we learn about personality from animal research?. Psychol. Bull. 127, 45–86 (2001).
CAS  PubMed  Google Scholar 

19.
Koolhaas, J. M. et al. Coping styles in animals: current status in behavior and stress-physiology. Neurosci. Biobehav. Rev. 23, 925–935 (1999).
CAS  PubMed  Google Scholar 

20.
Sih, A., Cote, J., Evans, M., Fogarty, S. & Pruitt, J. Ecological implications of behavioural syndromes: ecological implications of behavioural syndromes. Ecol. Lett. 15, 278–289 (2012).
PubMed  Google Scholar 

21.
Wolf, M. & Weissing, F. J. Animal personalities: consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).
PubMed  Google Scholar 

22.
Hardman, S. I. & Dalesman, S. Repeatability and degree of territorial aggression differs among urban and rural great tits (Parus major). Sci. Rep. 8, 5042 (2018).
ADS  PubMed  PubMed Central  Google Scholar 

23.
Wilson, D. S., Coleman, K., Clark, A. B. & Biederman, L. Shy-bold continuum in pumpkinseed sunfish (Lepomis gibbosus): an ecological study of a psychological trait. J. Comp. Psychol. 107, 250–260 (1993).
Google Scholar 

24.
Wilson, A. D. M. & Godin, J.-G.J. Boldness and intermittent locomotion in the bluegill sunfish Lepomis macrochirus. Behav. Ecol. 21, 57–62 (2010).
Google Scholar 

25.
Ward, A. J. W., Hart, P. J. B. & Webster, M. M. Boldness is influenced by social context in threespine sticklebacks (Gasterosteus aculeatus). Behaviour 144, 351–371 (2007).
Google Scholar 

26.
Dammhahn, M. & Almeling, L. Is risk taking during foraging a personality trait? A field test for cross-context consistency in boldness. Anim. Behav. 84, 1131–1139 (2012).
Google Scholar 

27.
Sih, A., Bell, A. M., Johnson, J. C. & Ziemba, R. E. Behavioral syndromes: an integrative overview. Q. Rev. Biol. 79, 241–277 (2004).
PubMed  Google Scholar 

28.
Smith, B. R. & Blumstein, D. T. Fitness consequences of personality: a meta-analysis. Behav. Ecol. 19, 448–455 (2008).
Google Scholar 

29.
Dingemanse, N. J., Both, C., Drent, P. J. & Tinbergen, J. M. Fitness consequences of avian personalities in a fluctuating environment. Proc. R. Soc. Lond. B 271, 847–852 (2004).
Google Scholar 

30.
Sinn, D. L., Apiolaza, L. A. & Moltschaniwskyj, N. A. Heritability and fitness-related consequences of squid personality traits. J. Evol. Biol. 19, 1437–1447 (2006).
CAS  PubMed  Google Scholar 

31.
Ariyomo, T. O., Carter, M. & Watt, P. J. Heritability of boldness and aggressiveness in the zebrafish. Behav. Genet. 43, 161–167 (2013).
PubMed  Google Scholar 

32.
van Oers, K., de Jong, G., Drent, P. J. & van Noordwijk, A. J. A genetic analysis of avian personality traits: correlated response to artificial selection. Behav. Genet. 34, 611–619 (2004).
PubMed  Google Scholar 

33.
Réale, D. & Festa-Bianchet, M. Predator-induced natural selection on temperament in bighorn ewes. Anim. Behav. 65, 463–470 (2003).
Google Scholar 

34.
Grand, T. C. Risk-taking behaviour and the timing of life history events: consequences of body size and season. Oikos 85, 467 (1999).
Google Scholar 

35.
Fraser, D. F., Gilliam, J. F., Daley, M. J., Le, A. N. & Skalski, G. T. Explaining leptokurtic movement distributions: intrapopulation variation in boldness and exploration. Am. Nat. 158, 124–135 (2001).
CAS  PubMed  Google Scholar 

36.
Mazza, V., Jacob, J., Dammhahn, M., Zaccaroni, M. & Eccard, J. A. Individual variation in cognitive style reflects foraging and anti-predator strategies in a small mammal. Sci. Rep. 9, 10157 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

37.
Patrick, S. C. & Weimerskirch, H. Personality, foraging and fitness consequences in a long lived seabird. PLoS ONE 9, e87269 (2014).
ADS  PubMed  PubMed Central  Google Scholar 

38.
Brown, J. S. & Kotler, B. P. Hazardous duty pay and the foraging cost of predation: foraging cost of predation. Ecol. Lett. 7, 999–1014 (2004).
Google Scholar 

39.
Godin, J. G. & Dugatkin, L. A. Female mating preference for bold males in the guppy, Poecilia reticulata. Proc. Natl. Acad. Sci. 93, 10262–10267 (1996).
ADS  CAS  PubMed  Google Scholar 

40.
Ariyomo, T. O. & Watt, P. J. Disassortative mating for boldness decreases reproductive success in the guppy. Behav. Ecol. 24, 1320–1326 (2013).
Google Scholar 

41.
Collins, S. M., Hatch, S. A., Elliott, K. H. & Jacobs, S. R. Boldness, mate choice and reproductive success in Rissa tridactyla. Anim. Behav. 154, 67–74 (2019).
Google Scholar 

42.
Mettke-Hofmann, C., Winkler, H. & Leisler, B. The Significance of ecological factors for exploration and neophobia in parrots. Ethology 108, 249–272 (2002).
Google Scholar 

43.
Burstal, J., Clulow, S., Colyvas, K., Kark, S. & Griffin, A. S. Radiotracking invasive spread: are common mynas more active and exploratory on the invasion front?. Biol Invasions https://doi.org/10.1007/s10530-020-02269-7 (2020).
Article  Google Scholar 

44.
Carter, A. J., Feeney, W. E., Marshall, H. H., Cowlishaw, G. & Heinsohn, R. Animal personality: what are behavioural ecologists measuring?. Biol. Rev. 88, 465–475 (2013).
PubMed  Google Scholar 

45.
Perals, D., Griffin, A. S., Bartomeus, I. & Sol, D. Revisiting the open-field test: what does it really tell us about animal personality?. Anim. Behav. 123, 69–79 (2017).
Google Scholar 

46.
Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis ). Proc. R. Soc. B 277, 1571–1579 (2010).
PubMed  Google Scholar 

47.
Dingemanse, N. J., Both, C., Drent, P. J., van Oers, K. & van Noordwijk, A. J. Repeatability and heritability of exploratory behaviour in great tits from the wild. Anim. Behav. 64, 929–938 (2002).
Google Scholar 

48.
Dingemanse, N. J. et al. Individual experience and evolutionary history of predation affect expression of heritable variation in fish personality and morphology. Proc. R. Soc. B. 276, 1285–1293 (2009).
PubMed  Google Scholar 

49.
Careau, V. et al. Genetic correlation between resting metabolic rate and exploratory behaviour in deer mice (Peromyscus maniculatus): pace-of-life in a muroid rodent. J. Evol. Biol. 24, 2153–2163 (2011).
CAS  PubMed  Google Scholar 

50.
Korsten, P., van Overveld, T., Adriaensen, F. & Matthysen, E. Genetic integration of local dispersal and exploratory behaviour in a wild bird. Nat. Commun. 4, 2362 (2013).
ADS  PubMed  Google Scholar 

51.
Drent, P. J., van Oers, K. & van Noordwijk, A. J. Realized heritability of personalities in the great tit (Parus major). Proc. R. Soc. Lond. B 270, 45–51 (2003).
Google Scholar 

52.
Dingemanse, N. J. & Réale, D. Natural selection and animal personality. Behavior 142, 1159–1184 (2005).
Google Scholar 

53.
Both, C., Dingemanse, N. J., Drent, P. J. & Tinbergen, J. M. Pairs of extreme avian personalities have highest reproductive success. J. Anim. Ecol. 74, 667–674 (2005).
Google Scholar 

54.
Mutzel, A., Dingemanse, N. J., Araya-Ajoy, Y. G. & Kempenaers, B. Parental provisioning behaviour plays a key role in linking personality with reproductive success. Proc. R. Soc. B 280, 20131019 (2013).
CAS  PubMed  Google Scholar 

55.
Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. & Drent, P. J. Natal dispersal and personalities in great tits (Parus major). Proc. R. Soc. Lond. B 270, 741–747 (2003).
Google Scholar 

56.
Haughland, D. L. & Larsen, K. W. Exploration correlates with settlement: red squirrel dispersal in contrasting habitats. J. Anim. Ecol. 73, 1024–1034 (2004).
Google Scholar 

57.
Alford, R. A., Brown, G. P., Schwarzkopf, L., Phillips, B. L. & Shine, R. Comparisons through time and space suggest rapid evolution of dispersal behaviour in an invasive species. Wildl. Res. 36, 23 (2009).
Google Scholar 

58.
Hoset, K. S. et al. Natal dispersal correlates with behavioral traits that are not consistent across early life stages. Behav. Ecol. 22, 176–183 (2011).
Google Scholar 

59.
Debeffe, L. et al. Exploration as a key component of natal dispersal: dispersers explore more than philopatric individuals in roe deer. Anim. Behav. 86, 143–151 (2013).
Google Scholar 

60.
Schirmer, A., Herde, A., Eccard, J. A. & Dammhahn, M. Individuals in space: personality-dependent space use, movement and microhabitat use facilitate individual spatial niche specialization. Oecologia 189, 647–660 (2019).
ADS  PubMed  PubMed Central  Google Scholar 

61.
Schirmer, A., Hoffmann, J., Eccard, J. A. & Dammhahn, M. My niche: individual spatial niche specialization affects within- and between-species interactions. Proc. R. Soc. B 287, 20192211 (2020).
PubMed  Google Scholar 

62.
Duckworth, R. A. & Badyaev, A. V. Coupling of dispersal and aggression facilitates the rapid range expansion of a passerine bird. Proc. Natl. Acad. Sci. 104, 15017–15022 (2007).
ADS  CAS  PubMed  Google Scholar 

63.
Carrete, M. & Tella, J. L. Behavioral correlations associated with fear of humans differ between rural and urban burrowing owls. Front. Ecol. Evol. 5, 54 (2017).
Google Scholar 

64.
Evans, J., Boudreau, K. & Hyman, J. Behavioural syndromes in urban and rural populations of song sparrows. Ethology 116, 588–595 (2010).
Google Scholar 

65.
Miranda, A. C., Schielzeth, H., Sonntag, T. & Partecke, J. Urbanization and its effects on personality traits: a result of microevolution or phenotypic plasticity?. Glob. Change Biol. 19, 2634–2644 (2013).
ADS  Google Scholar 

66.
Reil, D. et al. Puumala hantavirus infections in bank vole populations: host and virus dynamics in Central Europe. BMC Ecol. 17, 9 (2017).
PubMed  PubMed Central  Google Scholar 

67.
Andrzejewski, R., Babińska-Werka, J., Gliwicz, J. & Goszczyński, J. Synurbization processes in population of Apodemus agrarius. I. Characteristics of populations in an urbanization gradient. Acta Theriol. 23, 341–358 (1978).
Google Scholar 

68.
Babińska-Werka, J. Food of the striped field mouse in different types of urban green areas. Acta Theriol. 26, 285–299 (1981).
Google Scholar 

69.
Liro, A. Variation in weights of body and internal organs of the field mouse in a gradient of urban habitats. Acta Theriol. 30, 359–377 (1985).
Google Scholar 

70.
Sikorski, M. D. Craniometric variation of Apodemus agrarius (Pallas, 1771) in urban green areas. Acta Theriol. 27, 71–81 (1982).
Google Scholar 

71.
Babińska-Werka, J., Gliwicz, J. & Goszczyński, J. Demographic processes in an urban population of the striped field mouse. Acta Theriol. 26, 275–283 (1981).
Google Scholar 

72.
Gortat, T., Rutkowski, R., Gryczynska-Siemiatkowska, A., Kozakiewicz, A. & Kozakiewicz, M. Genetic structure in urban and rural populations of Apodemus agrarius in Poland. Mamm. Biol. 78, 171–177 (2013).
Google Scholar 

73.
McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Google Scholar 

74.
Moule, H., Michelangeli, M., Thompson, M. B. & Chapple, D. G. The influence of urbanization on the behaviour of an Australian lizard and the presence of an activity-exploratory behavioural syndrome: impact of urbanization on the delicate skink. J. Zool. 298, 103–111 (2016).
Google Scholar 

75.
Boon, A. K., Réale, D. & Boutin, S. Personality, habitat use, and their consequences for survival in North American red squirrels Tamiasciurus hudsonicus. Oikos 117, 1321–1328 (2008).
Google Scholar 

76.
Wolf, M., van Doorn, G. S., Leimar, O. & Weissing, F. J. Life-history trade-offs favour the evolution of animal personalities. Nature 447, 581–584 (2007).
ADS  CAS  PubMed  Google Scholar 

77.
Fischer, J. D., Cleeton, S. H., Lyons, T. P. & Miller, J. R. Urbanization and the predation paradox: the role of trophic dynamics in structuring vertebrate communities. Bioscience 62, 809–818 (2012).
Google Scholar 

78.
Shochat, E. Credit or debit? Resource input changes population dynamics of city-slicker birds. Oikos 106, 622–626 (2004).
Google Scholar 

79.
Bateman, P. W. & Fleming, P. A. Big city life: carnivores in urban environments: urban carnivores. J. Zool. 287, 1–23 (2012).
Google Scholar 

80.
Kettel, E. F., Gentle, L. K., Quinn, J. L. & Yarnell, R. W. The breeding performance of raptors in urban landscapes: a review and meta-analysis. J. Ornithol. 159, 1–18 (2018).
Google Scholar 

81.
Vines, A. & Lill, A. Boldness and urban dwelling in little ravens. Wildl. Res. 42, 590 (2015).
Google Scholar 

82.
Uchida K, Shimamoto T, Yanagawa H, Koizumi I (2020) Comparison of multiple behavioral traits between urban and rural squirrels. Urban Ecosyst. 1, 1–10 (2020).

83.
Atwell, J. W. et al. Boldness behavior and stress physiology in a novel urban environment suggest rapid correlated evolutionary adaptation. Behav. Ecol. 23, 960–969 (2012).
PubMed  PubMed Central  Google Scholar 

84.
Bókony, V., Kulcsár, A., Tóth, Z. & Liker, A. Personality traits and behavioral syndromes in differently urbanized populations of house sparrows (Passer domesticus). PLoS ONE 7, e36639 (2012).
ADS  PubMed  PubMed Central  Google Scholar 

85.
Greenberg, R. The Role of Neophobia and Neophilia in the Development of Innovative Behaviour of Birds. In Animal Innovation (eds Reader, S. M. & Laland, K. N.) 175–196 (Oxford University Press, Oxford, 2003). https://doi.org/10.1093/acprof:oso/9780198526223.003.0008.
Google Scholar 

86.
delBarco-Trillo, J. Shyer and larger bird species show more reduced fear of humans when living in urban environments. Biol. Lett. 14, 20170730 (2018).
PubMed  PubMed Central  Google Scholar 

87.
Greggor, A. L., Clayton, N. S., Fulford, A. J. C. & Thornton, A. Street smart: faster approach towards litter in urban areas by highly neophobic corvids and less fearful birds. Anim. Behav. 117, 123–133 (2016).
PubMed  PubMed Central  Google Scholar 

88.
Seress, G., Bókony, V., Heszberger, J. & Liker, A. Response to predation risk in urban and rural house sparrows: response to predation risk in house sparrows. Ethology 117, 896–907 (2011).
Google Scholar 

89.
Rymer, T., Pillay, N. & Schradin, C. Extinction or survival? Behavioral flexibility in response to environmental change in the african striped mouse rhabdomys. Sustainability 5, 163–186 (2013).
Google Scholar 

90.
Martin, J. G. A. & Réale, D. Temperament, risk assessment and habituation to novelty in eastern chipmunks Tamias striatus. Anim. Behav. 75, 309–318 (2008).
Google Scholar 

91.
Carere, C. & Locurto, C. Interaction between animal personality and animal cognition. Curr. Zool. 57, 491–498 (2011).
Google Scholar 

92.
Sih, A. & Del Giudice, M. Linking behavioural syndromes and cognition: a behavioural ecology perspective. Philos. Trans. R. Soc. B 367, 2762–2772 (2012).
Google Scholar 

93.
Mettke-Hofmann, C. & Gwinner, E. Differential assessment of environmental information in a migratory and a nonmigratory passerine. Anim. Behav. 68, 1079–1086 (2004).
Google Scholar 

94.
Mettke-Hofmann, C., Rowe, K. C., Hayden, T. J. & Canoine, V. Effects of experience and object complexity on exploration in garden warblers (Sylvia borin). J Zool. 268, 405–413 (2006).
Google Scholar 

95.
Boyer, N., Réale, D., Marmet, J., Pisanu, B. & Chapuis, J.-L. Personality, space use and tick load in an introduced population of Siberian chipmunks Tamias sibiricus. J. Anim. Ecol. 79, 538–547 (2010).
PubMed  Google Scholar 

96.
Barber, I. & Dingemanse, N. J. Parasitism and the evolutionary ecology of animal personality. Philos. Trans. R. Soc. B 365, 4077–4088 (2010).
Google Scholar 

97.
Jones, K. A. & Godin, J.-G.J. Are fast explorers slow reactors? Linking personality type and anti-predator behaviour. Proc. R. Soc. B 277, 625–632 (2010).
PubMed  Google Scholar 

98.
Sol, D., Griffin, A. S., Bartomeus, I. & Boyce, H. Exploring or avoiding novel food resources? The novelty conflict in an invasive bird. PLoS ONE 6, e19535 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

99.
Couchoux, C. & Cresswell, W. Personality constraints versus flexible antipredation behaviors: how important is boldness in risk management of redshanks (Tringa totanus) foraging in a natural system?. Behav. Ecol. 23, 290–301 (2012).
Google Scholar 

100.
Patergnani, M. et al. Environmental influence on urban rodent bait consumption. J. Pest Sci. 83, 347–359 (2010).
Google Scholar 

101.
Lehrer, E. W., Schooley, R. L. & Whittington, J. K. Survival and antipredator behavior of woodchucks (Marmota monax) along an urban-agricultural gradient. Can. J. Zool. 90, 12–21 (2012).
Google Scholar 

102.
Niemelä, P. T., Vainikka, A., Forsman, J. T., Loukola, O. J. & Kortet, R. How does variation in the environment and individual cognition explain the existence of consistent behavioral differences?. Ecol. Evol. 3, 457–464 (2013).
PubMed  Google Scholar 

103.
Garamszegi, L. Z. et al. Among-year variation in the repeatability, within- and between-individual, and phenotypic correlations of behaviors in a natural population. Behav. Ecol. Sociobiol. 69, 2005–2017 (2015).
PubMed  PubMed Central  Google Scholar 

104.
Stewart, I. D. & Oke, T. R. Local climate zones for urban temperature studies. Bull. Am. Meteorol. Soc. 93, 1879–1900 (2012).
ADS  Google Scholar 

105.
Semenov, M., Donatelli, M., Stratonovitch, P., Chatzidaki, E. & Baruth, B. ELPIS: a dataset of local-scale daily climate scenarios for Europe. Clim. Res. 44, 3–15 (2010).
Google Scholar 

106.
Hall, S. J. et al. Convergence of microclimate in residential landscapes across diverse cities in the United States. Landsc. Ecol. 31, 101–117 (2016).
Google Scholar 

107.
Janković, V. A historical review of urban climatology and the atmospheres of the industrialized world: review of urban climatology and the atmospheres of the industrialized world. WIREs Clim. Change 4, 539–553 (2013).
Google Scholar 

108.
Grimmond, S. Urbanization and global environmental change: local effects of urban warming. Geogr. J. 173, 83–88 (2007).
Google Scholar 

109.
Oke, T. R. The energetic basis of the urban heat island. Q. J. R. Meteorol. Soc. 108, 1–24 (1982).
ADS  Google Scholar 

110.
Charmantier, A., Demeyrier, V., Lambrechts, M., Perret, S. & Grégoire, A. Urbanization is associated with divergence in pace-of-life in great tits. Front. Ecol. Evol. 5, 53 (2017).
Google Scholar 

111.
Badyaev, A. V., Young, R. L., Oh, K. P. & Addison, C. Evolution on a local scale: developmental, functional, and genetic bases of divergence in bill form and associated changes in song structure between adjacent habitats. Evolution 62, 1951–1964 (2008).
PubMed  Google Scholar 

112.
Alberti, M., Marzluff, J. & Hunt, V. M. Urban driven phenotypic changes: empirical observations and theoretical implications for eco-evolutionary feedback. Philos. Trans. R. Soc. B 372, 20160029 (2017).
Google Scholar 

113.
Buchholz, S., Hannig, K., Möller, M. & Schirmel, J. Reducing management intensity and isolation as promising tools to enhance ground-dwelling arthropod diversity in urban grasslands. Urban Ecosyst. 21, 1139–1149 (2018).
Google Scholar 

114.
Seress, G., Lipovits, Á, Bókony, V. & Czúni, L. Quantifying the urban gradient: a practical method for broad measurements. Landsc. Urban Plan. 131, 42–50 (2014).
Google Scholar 

115.
Senatsverwaltung für Umwelt, Verkehr und Klimaschutz. Berlin Environmental Atlas—05.08 Biotopes (2016). https://fbinter.stadt-berlin.de/fb/index.jsp?loginkey=showMap&mapId=k_fb_berlinbtk@senstadt. Accessed 15 Dec 2019.

116.
GIS, E. A. v10. Environmental Systems Research Institute. Inc., Redlands, CA, USA (2011).

117.
Herde, A. & Eccard, J. A. Consistency in boldness, activity and exploration at different stages of life. BMC Ecol. 13, 49 (2013).
PubMed  PubMed Central  Google Scholar 

118.
Young, R. & Johnson, D. N. A fully automated light/dark apparatus useful for comparing anxiolytic agents. Pharmacol. Biochem. Behav. 40, 739–743 (1991).
CAS  PubMed  Google Scholar 

119.
Hall, C. S. Emotional behavior in the rat. I. Defecation and urination as measures of individual differences in emotionality. J. Comp. Psychol. 18, 385–403 (1934).
Google Scholar 

120.
Archer, J. Tests for emotionality in rats and mice: a review. Anim. Behav. 21, 205–235 (1973).
CAS  PubMed  Google Scholar 

121.
Walsh, R. N. & Cummins, R. A. The open-field test: a critical review. Psychol. Bull. 83, 482–504 (1976).
CAS  PubMed  Google Scholar 

122.
Cavigelli, S. A., Michael, K. C. & Ragan, C. M. Behavioral, physiological, and health biases in laboratory rodents: a basis for understanding mechanistic links between human personality and health. In Animal Personalities: Behavior, Physiology, and Evolution (eds Carere, C. & Maestripieri, D.) 441–498 (University of Chicago Press, Chicago, 2013).
Google Scholar 

123.
Gharnit, E., Bergeron, P., Garant, D. & Réale, D. Exploration profiles drive activity patterns and temporal niche specialization in a wild rodent. Behav. Ecol. 31, 772–783 (2020).
Google Scholar 

124.
Weiss, A. & Adams, M. J. Differential behavioral ecology. In Animal personalities: behavior, physiology and evolution (eds Carere, C. & Maestripieri, D.) 96–123 (University of Chicago Press, Chicago, 2013).
Google Scholar 

125.
Russell, P. A. Fear-evoking stimuli. In Fear in Animals and Man (ed. Sluckin, W.) 86–124 (Van Nostrand Reinhold Company, New York, 1979).
Google Scholar 

126.
Grossen, N. E. & Kelley, M. J. Species-specific behavior and acquisition of avoidance behavior in rats. J. Comp. Physiol. Psychol. 81, 307–310 (1972).
CAS  PubMed  Google Scholar 

127.
Mazza, V., Eccard, J. A., Zaccaroni, M., Jacob, J. & Dammhahn, M. The fast and the flexible: cognitive style drives individual variation in cognition in a small mammal. Anim. Behav. 137, 119–132 (2018).
Google Scholar 

128.
Geng, R. et al. Diet and prey consumption of breeding common Kestrel (Falco tinnunculus) in Northeast China. Prog. Nat. Sci. 19, 1501–1507 (2009).
Google Scholar 

129.
Jedrzejewska, B. & Jedrzejewski, W. Predation in Vertebrate Communities: The Bialowieza Primeval Forest as a Case Study, vol. 135 (Springer, Berlin, 2013).
Google Scholar 

130.
Sándor, A. D. & Ionescu, D. T. Diet of the eagle owl (Bubo bubo) in Braşov Romania. N.-West. J. Zool. 5, 170–178 (2009).
Google Scholar 

131.
Apfelbach, R., Blanchard, C. D., Blanchard, R. J., Hayes, R. A. & McGregor, I. S. The effects of predator odors in mammalian prey species: a review of field and laboratory studies. Neurosci. Biobehav. Rev. 29, 1123–1144 (2005).
PubMed  Google Scholar 

132.
Adibi, M. Whisker-mediated touch system in rodents: from neuron to behavior. Front. Syst. Neurosci. 13, 40 (2019).
PubMed  PubMed Central  Google Scholar 

133.
Lavenex, P. & Schenk, F. Olfactory cues potentiate learning of distant visuospatial information. Neurobiol. Learn. Mem. 68, 140–153 (1997).
CAS  PubMed  Google Scholar 

134.
Tomlinson, W. T. & Johnston, T. D. Hamsters remember spatial information derived from olfactory cues. Anim. Learn. Behav. 19, 185–190 (1991).
Google Scholar 

135.
Casarrubea, M. et al. Temporal structure of the rat’s behavior in elevated plus maze test. Behav. Brain Res. 237, 290–299 (2013).
CAS  PubMed  Google Scholar 

136.
Takahashi, A., Kato, K., Makino, J., Shiroishi, T. & Koide, T. Multivariate analysis of temporal descriptions of open-field behavior in wild-derived mouse strains. Behav. Genet. 36, 763–774 (2006).
PubMed  Google Scholar 

137.
Krupa, D. J., Matell, M. S., Brisben, A. J., Oliveira, L. M. & Nicolelis, M. A. L. Behavioral properties of the trigeminal somatosensory system in rats performing whisker-dependent tactile discriminations. J. Neurosci. 21, 5752–5763 (2001).
CAS  PubMed  PubMed Central  Google Scholar 

138.
von Heimendahl, M., Itskov, P. M., Arabzadeh, E. & Diamond, M. E. Neuronal activity in rat barrel cortex underlying texture discrimination. PLoS Biol. 5, e305 (2007).
Google Scholar 

139.
Morita, T., Kang, H., Wolfe, J., Jadhav, S. P. & Feldman, D. E. Psychometric curve and behavioral strategies for whisker-based texture discrimination in rats. PLoS ONE 6, e20437 (2011).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

140.
Lavenex, P. & Schenk, F. Integration of olfactory information in a spatial representation enabling accurate arm choice in the radial arm maze. Learn. Mem. 2, 299–319 (1996).
CAS  PubMed  Google Scholar 

141.
Rangassamy, M., Dalmas, M., Féron, C., Gouat, P. & Rödel, H. G. Similarity of personalities speeds up reproduction in pairs of a monogamous rodent. Anim. Behav. 103, 7–15 (2015).
Google Scholar 

142.
Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: a practical guide for biologists. Biol. Rev. 85, 935–956 (2010).
Google Scholar 

143.
Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 

144.
Faraway, J. J. Extending the Linear Model with R (Chapman & Hall/CRC, Boca Raton, 2006).
Google Scholar 

145.
Zuur, A. F. Mixed Effects Models and Extensions in Ecology with R (Springer, Berlin, 2009).
Google Scholar 

146.
Bates, D. et al. Package ‘lme4’. Convergence 12, 2 (2015).
Google Scholar 

147.
Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & Team, R. C. nlme: linear and nonlinear mixed effects models. R Package Vers. 3, 111 (2013).
Google Scholar 

148.
Tabachnick, B. G. & Fidell, L. S. Principal components and factor analysis. Using Multivar. Stat. 4, 582–633 (2001).
Google Scholar 

149.
Kaiser, H. F. Unity as the universal upper bound for reliability. Percept. Mot. Skills 72, 218–218 (1991).
Google Scholar 

150.
Hadfield, J. D., Wilson, A. J., Garant, D., Sheldon, B. C. & Kruuk, L. E. B. The misuse of BLUP in ecology and evolution. Am. Nat. 175, 116–125 (2010).
PubMed  Google Scholar 

151.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar  More

Imagination is critical to sustainable and just futures for life on Earth8,13. Writing after the West African Ebola outbreak, Professor Michael Osterholm and colleagues called for more “creative imagination” to consider future pandemic scenarios14. This feels particularly salient five years on. Purely technocratic approaches fail to engage with the emotions that motivate action towards alternative futures: fear, hope, grief and agency8,15. By building new ways of thinking about longstanding problems, inclusive and creative processes can generate positive stories about the future in ways that are empowering8,10. Imagining the future can drive societies towards change by shaping common practices, aspirations and institutions16.
Methods for imagining, such as scenarios analysis, strategic foresight and speculative fiction are commonplace in research, investment and planning8,13,17. They can help the biodiversity community address the bleak futures that are projected for biodiversity. Research can play an important role in embracing imagination by fostering novel participatory methods that enable society to explore what is possible, plausible and desirable13. All models and scenarios are wrong, some are helpful: they contain assumptions about what matters, what is known and what is unknown. Embracing and communicating these assumptions and uncertainties builds trust in science, opening up spaces for deliberation about values, trade-offs and desirable futures18.
Imagination can build the anticipatory capacity to get ahead of the curve, rather than react to crisis17. Decision makers must learn to provide anticipatory leadership that fosters shared responsibility for actions that may have greater costs now, to avert harm in the future. Enabling transformations also requires those who benefit from the status quo to acknowledge the need for change. Policy frameworks need to consider the distribution of costs and benefits over longer timescales when setting current priorities. Ultimately, society needs to accept that the future is unknowable and uncertain, but that action is needed now.
These anticipatory capacities start with asking: what are the short- and long-term drivers of change? What values should be maintained into the future? What can be done differently over the next five years? Over the next 30 years? What do we need to know and what will we never know? How can options be created and traps avoided? What are the ethical implications of action and inaction? Considering these types of questions can provide a foundation for decision making despite uncertainty.
Our stories show that choices have consequences. Some close down options. Some open up multiple pathways. Either way, choices create winners and losers. The critical challenges of the Anthropocene require humility19 and the ability to respond20. Imagination can help the biodiversity community grapple with these challenges by embracing diverse ways of thinking, listening, being and knowing. And such diversity can be the foundation of more just and sustainable futures for life on Earth. More

1.
Wilga, C. A. D., Nishiguchi, M. K. & Tsukimura, B. Integr. Comp. Biol. 57, 7–17 (2017).
Article  Google Scholar 
2.
Rodriguez, J. E., Campbell, K. M. & Pololi, L. H. BMC Med. Educ. 15, 6 (2015).
Article  Google Scholar 

3.
Dashper, K. Gender Work Organ. 26, 541–557 (2017).
Article  Google Scholar 

4.
Timmers, T. M., Willemsen, T. M. & Tijdens, K. G. High. Educ. 59, 719–735 (2010).
Article  Google Scholar 

5.
Vangerven, B. et al. Omega 81, 38–47 (2018).
Article  Google Scholar 

6.
Fritz, C., Yankelevich, M., Zarubin, A. & Barger, P. J. Appl. Psychol. 95, 977–983 (2010).
Article  Google Scholar 

7.
Sonnentag, S. & Frese, M. Comprehensive Handbook of Psychology (eds Borman, W. C. et al.) 453–491 (Wiley, 2003).

8.
Smith, W. A., Allen, W. R. & Danley, L. L. Am. Behav. Sci. 51, 551–578 (2007).
Article  Google Scholar 

9.
Avey, J. B., Reichard, R. J., Luthans, F. & Mhatre, K. H. Hum. Resour. Dev. Q. 22, 127–152 (2011).
Article  Google Scholar 

10.
Nielsen, M. W., Bloch, C. W. & Schiebinger, L. Nat. Hum. Behav. 2, 726–734 (2018).
Article  Google Scholar 

11.
Johnson, E. J. & Goldstein, D. Science 302, 1338–1339 (2003).
CAS  Article  Google Scholar  More

The Cooper bundles were found just beneath the sand surface ~15 m up from the waterline. A sand slope angle of 10∘ was measured during a site investigation which would place the burial site ~3 m vertically above the water line. This location would only be immersed during times of high water and wave action. Dredging operations took place on the river and the sand was dumped slightly upstream of the burial location and could have contributed to additional sand on top of the bills. Sand is no longer deposited on the beach and it has undergone severe erosion. Rubber bands found intact but degraded on the bundles suggests they were initially buried without any significant exposure to the elements which is known to rapidly degrade them25.
In order to determine if a seasonal diatom timeline can be used to constrain the burial of the Cooper money, the first question to be answered is: can diatoms penetrate a bundle of money buried in sand? The diatom saturated water experiment showed that penetration is possible but only for the smaller range of diatoms and only a limited distance in from the edge on the order of millimeters. No “tide lines” of diatoms or small sand fragments were found on the Cooper bill. Since we know from the experiment that diatom accumulations were likely to happen on the edges, the lack of aggregations suggests they were destroyed with the severe degradation around the edges of the bundle. The inner degraded edge where the SEM samples were taken from showed no accumulations, suggesting the bills had congealed into a solid lump (consistent with the condition that the bills were found in), preventing any further diatom infiltration.
A second line of evidence that would signal diatom infiltration while buried would be an abundance of diatoms in the bills that were also found in the surrounding sand. The extraction of the diatoms from the Tena Bar sand showed a predominance of small forms on the order of 3–5 µm. These small diatoms are consistent with species that can survive in sand due to their ability to situate in the interstitial crevices of a single sand grain26,27. Larger diatoms, of which Asterionella and Fragilaria are among the largest, have low survivability in the proportionally boulder size sand grains26. The lack of predominantly smaller diatoms on the Cooper bill suggests little to no diatom infiltration to the inner portions of the stack occurred while buried. While similar small diatoms were found on the bills, they were not a dominant category as would be expected if they were the primary source of infiltration.
If the Cooper bill used in this examination was from the top of the stack, then one could expect to find a variety of diatoms from all sources. Figure 2C indicates conclusively that the examined bill is from the middle of the stack by finding an intact Fragilaria sandwiched between two bills. Due to the congealed nature of the bills, it was not uncommon to find intact fragments of other bills adhered to the larger bill. Fragilaria at ~80 µm28 is considered a larger diatom in the Columbia River system29. It is planktonic30 and therefore has no ability to move through sand. Its size and location interior to the stack (Fig. 1) and notably with no smaller diatoms surrounding it, suggests that it came to rest there while the bill was completely exposed to river water.
If the previous experiments and investigations rule out diatom infiltration while buried, then the findings suggest that diatoms found their way onto the bills during water immersion. As shown in Fig. 4, a stack of bills once saturated, will fan out in water exposing all surfaces to micro-particles in the water environment. The exposure of the fanned out stack to the river, suggests the simplest way for large, intact but fragile diatoms to be found alone interior to the bill stack. This would have occurred prior to burial and be in the water long enough for fan out to occur.
Figure 4

(A) Stack of bills bound with a rubber band immediately after placing in still water. (B) After several minutes, the stack becomes saturated and fans out exposing individual bills to the water. Shortly thereafter the entire stack will sink to the bottom.

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The Columbia River has seasonal blooms of diatoms with different species found in winter vs summer19. If the bills were submerged for an extended period covering multiple seasons, then diatom species found on the bill should also represent multiple seasons. Table 1 shows the genera found on the Cooper bill and the dollar bill soaked in the Columbia in November. The first notable observation is that there is little overlap in genera between the two seasons.
Asterionella followed by Fragilaria are key indicators in this study. Asterionella are relatively large up to 100 µm31, planktonic diatoms that undergo radical changes in population in the Columbia River (Fig. 5) of up to 10 × during the course of the year20. They assemble into star shaped colonies that are susceptible to damage. Asterionella were found broken but associated on the Cooper bill as shown in Fig. 2A. Although in pieces, the relatively complete association of parts suggests that the diatoms landed intact on the bill and were subsequently crushed and broken after the fact. Similar associations were found elsewhere on the Cooper samples.
Figure 5

Monthly abundance of Asterionella showing population bloom in May and June. Extremely low numbers are apparent for winter months. Data compiled from three sources19,20,21 graph shows relative numbers.

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Several examples Asterionella were found on the Cooper bills and this diatom is nearly absent in November when the jump occurred20,21. There is however a very large bloom of Asterionella in early summer during the months of May and June19,21. The other diatoms identified on the Cooper bill such as Stephanodiscus are also more prevalent in the summer season21. The diatoms found on the November bill are not consistent with species found on the Cooper bill. This suggests that the Cooper bill was immersed during the summer Asterionella bloom and the length of submersion did not extend into subsequent seasons.
Trace elements are incorporated into the diatom frustule during growth and elemental availability varies in rivers during the year17. Krivtsov et al. 2000 studied the elemental variation in A. formosa and found that it varied by the season5. There were not enough recovered Asterionella from the November time frame to do a direct comparison but elemental signatures from a variety of specimens were compared between the November and Cooper bills. Figure 6 shows the diatom’s elemental spectra of calcium and sodium overlaid. The spectra were normalized to silicon and show relative abundances. The detected levels were small and near the limit of EDS sensitivity so this data is provided as qualitative. Elemental differences between the two groups showed slightly enriched calcium and a lack of sodium in the November diatoms while showing the complete opposite for the Cooper diatoms. A single fragment potentially from Asterionella or Fragilaria was found in the November sand from Tena Bar (Fig. 4B). This spectrum showed elevated levels of calcium and sodium again suggesting a difference from the A. formosa found on the Cooper bill which only showed enriched sodium. The single diatom spectrum from the March bill showed no increase in either sodium or calcium suggesting the March time frame has a different elemental abundance in the water from either the winter or Cooper sample suspected to have summer diatoms. The reproductive lifetime of a diatom is on the order of days32 so a difference in elemental abundance suggests that these three assemblages were from different seasonal periods.
Figure 6

(A) EDS spectra overlay showing the sodium line. Red lines are spectra from the Cooper bill diatoms showing elevated sodium levels, green lines are from November samples. Blue line is the single Asterionella spectra from the November sand sample showing no enrichment in either sodium or calcium. (B) Calcium line showing elevated presence of calcium for November diatoms while Cooper samples show lower levels. Each group of diatoms showed opposite enrichment of sodium and calcium. Data is relative and qualitative.

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