More stories

  • in

    Avoid the reproduction of coloniality in decolonial studies in ecology

    Lander, E. A. A colonialidade do saber: eurocentrismo e ciencias sociais. Perspectivas latino-americanas (CLACSO, 2005).Césaire, A. Guaraguao 20, 157–193 (2005).
    Google Scholar 
    Fanon, F. Black Skin, White Masks (Pluto, 1986).Mbembe, A. Arts Humanit. High. Educ. 15, 29–45 (2016).Article 

    Google Scholar 
    Rivera Cusicanqui, S. Ch’ixinakax utxiwa: una reflexión sobre prácticas y discursosdescolonizadores (Tinta Limón, 2010).Castro-Gómez, S. & Grosfoguel, R. El Giro Decolonial (Siglo del hombre, 2007).Grosfoguel, R. Rev. (Fernand Braudel Center) 25, 203–224 (2002).
    Google Scholar 
    Quijano, A. Int. Sociol. 15, 215–232 (2000).Article 

    Google Scholar 
    Visvanathan, S. in Science and Citizens: Globalization and the Challenge of Engagement (eds Leach, M. et al.) 83–97 (Zed Books, 2005).Spivak, G. C. Can the Subaltern Speak? (Routledge, 1994).Bhargava, R. Glob. Policy 4, 413–417 (2013).Article 

    Google Scholar 
    Bhambra, G. K. Crit. Times 4, 73–89 (2021).Article 

    Google Scholar 
    Sousa Santos, B. Rev. (Fernand Braudel Center) 30, 45–89 (2007).
    Google Scholar 
    Maas, B. et al. Conserv. Lett. 14, e12797 (2021).Article 

    Google Scholar 
    Nuñez, M. A. et al. J. Appl. Ecol. 56, 4–9 (2019).Article 

    Google Scholar 
    Mohammed, R. S. et al. Am. Nat. 200, 140–155 (2022).Article 

    Google Scholar 
    Rau, J. et al. Ecol. Austral 27, 312–496 (2017).Article 

    Google Scholar 
    Raja, N. B. et al. Nat. Ecol. Evol. 6, 145–154 (2022).Article 

    Google Scholar 
    Trisos, C. H., Auerbach, J. & Katti, M. Nat. Ecol. Evol. 6, 1205–1212 (2021).Article 

    Google Scholar 
    Eichhorn, M. P., Baker, K. & Griffiths, M. Front. Biogeogr. 12, 1–7 (2020).
    Google Scholar 
    Baker, K., Eichhorn, M. P. & Griffiths, M. Biotropica 51, 288–292 (2019).Article 

    Google Scholar 
    Brandt, S. et al. Ecol. Evol. 10, 12450–12456 (2020).Article 

    Google Scholar 
    McGill, B. M. et al. Ecol. Evol. 11, 3636–3645 (2021).Article 

    Google Scholar 
    Melles, S. J. et al. Ecoscience 26, 323–340 (2019).Article 

    Google Scholar 
    Adebisi, F. Decolonisation is not about ticking a box: it must disrupt. criticallegalthinking.com, https://criticallegalthinking.com/2020/03/12/decolonisation-is-not-about-ticking-a-box/ (12 March 2020).Güttler, N. Ber. Wiss. 42, 235–258 (2019).
    Google Scholar 
    Worster, D. J. Nature’s Economy: A History of Ecological Ideas, 2nd edn (Cambridge Univ. Press, 1994).Nicolson, M. Hist. Sci. 26, 183–200 (1988).Article 

    Google Scholar 
    Lewinsohn, T. M. Filos. História Biol. 11, 347–381 (2016).
    Google Scholar 
    Raby, M. The Study of Ecology in Latin America and the Caribbean (Oxford Univ Press, 2021).Mignolo, W. D. Coloniality at Large: Time and the Colonial Difference (Taylor & Francis, 2020).Simpson, D. Y., Beatty, A. E. & Ballen, C. J. Trends Ecol. Evol. 36, 4–8 (2021).Article 

    Google Scholar 
    Smith, L. T. Decolonizing Methodologies: Research and Indigenous Peoples (Zed Books, 2013).Grosfoguel, R. Tabula Rasa 24, 123–143 (2016). More

  • in

    Tracking the British agricultural revolution through the isotopic analysis of dated parchment

    Jones, E. L. Agriculture and economic growth in England, 1660–1750: Agricultural change. J. Econ. Hist. 25, 1–18 (1965).Article 

    Google Scholar 
    Chambers, J. D. & Mingay, G. E. The Agricultural Revolution: 1750–1880 (Batsford, 1966).
    Google Scholar 
    Kerridge, E. The Agricultural Revolution (Allen & Unwin, Paris, 1967).
    Google Scholar 
    Thompson, F. M. L. The second agricultural revolution, 1815–1880. Econ. Hist. Rev. 21, 62–77 (1968).
    Google Scholar 
    Overton, M. Agricultural Revolution in England: The Transformation of the Agrarian Economy 1500–1850 (Cambridge University Press, 1996).Book 

    Google Scholar 
    Turner, M. E., Beckett, J. V. & Afton, B. Farm Production in England 1700–1914 (Oxford University Press, 2001).Book 

    Google Scholar 
    Williamson, T. The Transformation of Rural England: Farming and the Landscape, 1700–1870 (University of Exeter Press, 2002).
    Google Scholar 
    Davis, J. M. & Beckett, J. V. Animal husbandry and agricultural improvement: The archaeological evidence from animal bones and teeth. Rural Hist. 10, 1–17 (1999).Article 
    CAS 

    Google Scholar 
    Thomas, R. Zooarchaeology, improvement and the British agricultural revolution. Int. J. Hist. Archaeol. 9, 71–88 (2005).Article 

    Google Scholar 
    Sologestoa, I. G. & Albarella, U. (eds) The Rural World in the Sixteenth Century: Exploring the Archaeology of Innovation in Europe (Brepols, 2002).
    Google Scholar 
    Doherty, S. P. & Henderson, S. Production of parchment legal deeds in England, 1690–1830. Hist. Res. 95, 575–585 (2022).Article 

    Google Scholar 
    DeNiro, M. J. & Epstein, S. Influence of diet on the distribution of carbon isotopes in animals. Geochim. Cosmochim. Acta 42, 495–506 (1978).Article 
    ADS 
    CAS 

    Google Scholar 
    Bateman, A. S. & Kelly, S. D. Fertilizer nitrogen isotope signatures. Isotopes Environ. Health Stud. 43, 237–247 (2007).Article 
    CAS 

    Google Scholar 
    Szpak, P. Complexities of nitrogen isotope biogeochemistry in plant–soil systems: Implications for the study of ancient agricultural and animal management practices. Front. Plant Sci. 5, 288 (2014).Article 
    ADS 

    Google Scholar 
    Trentacoste, A. et al. Heading for the hills? A multi-isotope study of sheep management in first-millennium BC Italy. J. Archaeol. Sci. Rep. 29, 102036 (2020).
    Google Scholar 
    Doherty, S., Alexander, M. M., Vnouček, J., Newton, J. & Collins, M. J. Measuring the impact of parchment production on skin collagen stable isotope (δ13C and δ15N) values. STAR Sci. Technol. Archaeol. Res. 7, 1–12 (2021).
    Google Scholar 
    Doherty, S. P. et al. A modern baseline for the paired isotopic analysis of skin and bone in terrestrial mammals. R. Soc. Open Sci. 9, 211587 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Camin, F. et al. Multi-element (H, C, N, S) stable isotope characteristics of lamb meat from different European regions. Anal. Bioanal. Chem. 389, 309–320 (2007).Article 
    CAS 

    Google Scholar 
    Kohn, M. J. Carbon isotope compositions of terrestrial C3 plants as indicators of (paleo)ecology and (paleo)climate. Proc. Natl. Acad. Sci. U.S.A. 107, 19691–19695 (2010).Article 
    ADS 
    CAS 

    Google Scholar 
    Clarkson, L. A. The manufacture of leather. In The Agrarian History of England and Wales, Vol VI, 1750–1820 (ed. Mingay, G. E.) 466–485 (Cambridge University Press, 1989).
    Google Scholar 
    Millard, A. R., Dodd, L. & Nowell, G. Palace Green Library excavations 2013 (PGL13): Isotopic Studies Project Report (2015).Bleasdale, M. et al. Multidisciplinary investigations of the diets of two post-medieval populations from London using stable isotopes and microdebris analysis. Archaeol. Anthropol. Sci. 11, 6161–6181 (2019).Article 

    Google Scholar 
    Thirsk, J. The English Rural Landscape (Oxford University Press, 2000).
    Google Scholar 
    Home, T. H. The Complete Grazier 5th edn. (Baldwin & Cradock, 1830).
    Google Scholar 
    Ellman, J. On folding sheep. The Farmers Magazine 110 (1831).Bogaard, A., Heaton, T. H. E., Poulton, P. & Merbach, I. The impact of manuring on nitrogen isotope ratios in cereals: Archaeological implications for reconstruction of diet and crop management practices. J. Archaeol. Sci. 34, 335–343 (2007).Article 

    Google Scholar 
    Schwertl, M., Auerswald, K., Schäufele, R. & Schnyder, H. Carbon and nitrogen stable isotope composition of cattle hair: Ecological fingerprints of production systems?. Agric. Ecosyst. Environ. 109, 153–165 (2005).Article 
    CAS 

    Google Scholar 
    Trow-Smith, R. A History of British Livestock Husbandry, 1700–1900 (Keegan & Paul, 1959).
    Google Scholar 
    Babraj, J. A. et al. Collagen synthesis in human musculoskeletal tissues and skin. Am. J. Physiol. Endocrinol. Metab. 289, E864–E869 (2005).Article 
    CAS 

    Google Scholar 
    El-Harake, W. A. et al. Measurement of dermal collagen synthesis rate in vivo in humans. Am. J. Physiol. 274, E586–E591 (1998).CAS 

    Google Scholar 
    Fuller, B. T., Fuller, J. L., Harris, D. A. & Hedges, R. E. M. Detection of breastfeeding and weaning in modern human infants with carbon and nitrogen stable isotope ratios. Am. J. Phys. Anthropol. 129, 279–293 (2006).Article 
    CAS 

    Google Scholar 
    Houghton, J. Friday 7th 1694. In Husbandry and Trade Improv’d (ed. Bradley, R.) 323–330 (Woodman and Lyon, 1728).
    Google Scholar 
    de La Lande, J. & McCauley, G. The art of making parchment. Art Transl. 13, 326–386 (2021).Article 

    Google Scholar 
    Reed, R. Ancient Skins, Parchments and Leather (Seminar Press, 1973).
    Google Scholar 
    Mekota, A.-M., Grupe, G., Ufer, S. & Cuntz, U. Serial analysis of stable nitrogen and carbon isotopes in hair: Monitoring starvation and recovery phases of patients suffering from anorexia nervosa. Rapid Commun. Mass Spectrom. 20, 1604–1610 (2006).Article 
    ADS 
    CAS 

    Google Scholar 
    Neuberger, F. M., Jopp, E., Graw, M., Püschel, K. & Grupe, G. Signs of malnutrition and starvation–reconstruction of nutritional life histories by serial isotopic analyses of hair. Forensic Sci. Int. 226, 22–32 (2013).Article 
    CAS 

    Google Scholar 
    Hargis, A. M. & Myers, S. The intergument. In Pathological Basis of Veterinary Disease 6th edn (ed. Zachary, J. F.) 1009–1146 (Elsevier, 2017).Chapter 

    Google Scholar 
    Hansard’s Parliamentary Debates, Volume 28, Third Series, comprising the period from 22nd May to 26th June 1835. (T.C. Hansard, 1835).US Department of Agriculture. Foot Rot of Sheep, FB2206 (USDA, 1972).
    Google Scholar 
    The House of Commons. Reports from Committees, Volume 8, Part 1 79–288 (Select Committee on Agricultural Distress, 1836).
    Google Scholar 
    Pálsson, H. & Vergés, J. B. Effects of the plane of nutrition on growth and the development of carcass quality in lambs Part I. The effects of High and Low planes of nutrition at different ages. J. Agric. Sci. 42, 1–92 (1952).Article 

    Google Scholar 
    Grau-Sologestoa, I. & Albarella, U. The ‘long’ sixteenth century: A key period of animal husbandry change in England. Archaeol. Anthropol. Sci. 11, 2781–2803 (2019).Article 

    Google Scholar 
    Fisher, A. & Thomas, R. Isotopic and zooarchaeological investigation of later medieval and post-medieval cattle husbandry at Dudley Castle, West Midlands. Environ. Archaeol. 17, 151–167 (2012).Article 

    Google Scholar 
    Jones, E. L. The Development of English Agriculture, 1815–1873 (Palgrave, 1968).Book 

    Google Scholar 
    Perren, R. Agriculture in Depression 1870–1940 (Cambridge University Press, 1995).
    Google Scholar 
    Osorio, M. T., Moloney, A. P., Schmidt, O. & Monahan, F. J. Beef authentication and retrospective dietary verification using stable isotope ratio analysis of bovine muscle and tail hair. J. Agric. Food Chem. 59, 3295–3305 (2011).Article 
    CAS 

    Google Scholar 
    Zazzo, A. et al. Isotopic composition of sheep wool records seasonality of climate and diet. Rapid Commun. Mass Spectrom. 29, 1357–1369 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Anonymous. Supplementary Chapter to ‘An Essay on Calcareous Manures’. in The Farmer’s Register. Vol. I. 76–79 (Printed for Edmund Ruffin, 1834).Szpak, P., Longstaffe, F. J., Millaire, J.-F. & White, C. D. Stable isotope biogeochemistry of seabird guano fertilization: Results from growth chamber studies with maize (Zea mays). PLoS One 7, e33741 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Caird, S. J. English Agriculture in 1850–51 (Longman, Brown, Green, and Longmans, 1852).Book 

    Google Scholar 
    Prothero, R. E. English Farming, Past and Present (Longmans, Green, 1912).
    Google Scholar 
    Doherty, S. P., Henderson, S., Fiddyment, S., Finch, J. & Collins, M. J. Scratching the surface: The use of sheepskin parchment to deter textual erasure in early modern legal deeds. Herit. Sci. 9, 29 (2021).Article 
    CAS 

    Google Scholar 
    Fiddyment, S. et al. Animal origin of 13th-century uterine vellum revealed using noninvasive peptide fingerprinting. Proc. Natl. Acad. Sci. U.S.A. 112, 15066–15071 (2015).Article 
    ADS 
    CAS 

    Google Scholar 
    Campana, M. G. et al. A flock of sheep, goats and cattle: Ancient DNA analysis reveals complexities of historical parchment manufacture. J. Archaeol. Sci. 37, 1317–1325 (2010).Article 

    Google Scholar 
    Szpak, P., Metcalfe, J. Z. & Macdonald, R. A. Best practices for calibrating and reporting stable isotope measurements in archaeology. J. Archaeol. Sci. Rep. 13, 609–616 (2017).
    Google Scholar 
    Dombrosky, J. A. ~1000-year 13C Suess correction model for the study of past ecosystems. Holocene 30, 474–478 (2020).Article 
    ADS 

    Google Scholar  More

  • in

    Exposure of aquatic organisms to natural radionuclides in irrigation drains, Qena, Egypt

    Samples collection and preparationFreshwater and sediment samples were collected from 5 irrigation drains (EL-Shikah, EL- Tramsa, EL-Mahrosa, EL-Aslia, and EL-Rawy) located in the geographical area of Qena city, the capital of Qena Governorate, 600 km south of Cairo, (Figs. 1 and 2). 3 sites inside each drain were randomly selected as sampling site; one of these sites represents the outlet of the drain into the Nile River. In addition, one site facing each drain in the main stream of the Nile River was selected to collect freshwater only, thus the total number of samples are 20 freshwater and 15 sediment samples.Figure 1Location map of the area under study (ArcGIS software 10.8.1; ArcGIS Online).Full size imageFigure 2Irrigation drain under study.Full size imagePolyethylene Marinelli beakers with a capacity of 1.4 L are used as collection and measuring containers. The beakers were washed with dilute hydrochloric acid and distilled water before use, filled to brim, and then pressed the tight lid to eliminate the internal air. Drops of HNO3 were added to the samples to prevent the adhesive of radionuclides with bottle walls8.Sediment samples were collected by Ekman grab sediment sampler. The collected samples were dried using electrical oven at a temperature of 105℃ for 24 h, then sieved through 200 mesh size. The dried samples were filled in hermetical sealed 500 ml polyethylene beakers. The prepared water and sediment samples were stored for 4 weeks to reach a secular equilibrium of radium and thorium with their progenies9.Measuring systemsGamma-ray spectrometer consisting of ″3 × 3″ NaI (Tl) detector enclosed in 5 cm thick cylindrical lead shield to reduce the background radiation and connected with 1024 multichannel analyzer was used. The spectrometer was calibrated for energy using 60Co and 137Cs standard point sources, and calibrated for efficiency using a multi-nuclides standard solution which covers a wide range of energy10. The spectrum was accumulated from each sample over 24 h and analyzed by Maestro software. The background was measured under the same condition of sample measurement.226Ra was determined using 214Bi and 214Pb gamma-lines at 609 keV and 352 keV, respectively, while 232Th from gamma-lines of 228Ac (911 keV) and 212Pb (238 keV). 40K was determined from its single gamma-line at 1460 keV. The activity concentration was calculated using the following formula (Eq. 1)11.$$A = frac{{C_{n} }}{{T times varepsilon { } times {text{P}} times {text{V }}left( {{text{or}}} right){text{M}}}}$$
    (1)

    where A is the activity concentration (Bq kg−1) or (Bq l−1), Cn is the net counts under a given peak area, T the sample counting time, (varepsilon) is the detection efficiency at measured energy, P is the emission probability and V is the sample volume in liter, M is the sample mass in kilogram. Minimum detectable activity (MDA) was estimated according to Currie definition using Eq. 212 and the MDA values were 0.031, 0.035 and 1.94 Bq L−1 for 226Ra, 232Th, and 40K, respectively.$${text{MDA}} = frac{2.71 + 465sqrt B }{{T times varepsilon times P times V}}$$
    (2)

    where B is the background counts under a given peak area,T,ɛ, P, and V are defined above.Doses for aquatic organismsThe external and internal absorbed dose rate for aquatic organisms (Phytoplankton, Mollusca, and Crustacean) in the studied irrigation drains was calculated based on the measured activity concentrations of 226Ra, 232Th, and 40K in environmental media (water and sediment) and using dose conversion coefficients of a given radionuclide for the reference organisms according to the method outlined by Brown et al. described below13,14.$$begin{aligned}& left( {Sediment,, conc. ,,wet} right)_{radionuclide} = (Sediment ,,conc. ,,dry)_{radionuclide} times left( {solids ,,fraction} right) \& qquad qquad + (water ,,conc.)_{radionuclide} times (1 – left( {solids ,,fraction} right). \ end{aligned}$$
    (3)
    $$begin{aligned}& left( {user2{External ,,dose ,,rate}} right)_{radionuclide,, organism} = DPUC_{radionuclide, ,organism}^{external} times left[ {Sediment ,conc. ,wet_{radionuclide} times left( {fsed_{organism} + fsedsur_{organism} /2} right)} right. \& quad quad left. { + left( {fwater_{organism} + fsedsur_{organism} /2} right) times water ,conc._{radionuclide } /1000} right] \ end{aligned}$$
    (4)
    $$left( {user2{Internal,dose,rate}} right)_{{radionuclide,,organism}} = ~left( {water,conc.} right)_{{radionuclide}} times CF_{{radionuclide}}^{{organism}} times DPUC_{{radionuclide,,organism}}^{{internal}}$$
    (5)

    where sediment conc. is the sediment activity concentration of a given radionuclide in Bq kg−1,water conc. is the water activity concentration of a given radionuclide in Bq m−3, CF is distribution coefficient factors for given radionuclide in freshwater sediment in m3 kg−1, DPUC is the dose rate per unit concentration coefficients (fresh weight) in μGy h−1 per Bq kg−1 weighted for radiation type (alpha = 10, low energy beta = 3, and high energy beta and gamma = 1), solids fraction of wet sediment (0.4), fsed organism is the time fraction spends by organism in sediment, fsedsur organism is the time fraction spends by organism at the sediment/water interface, fwater organism is the time fraction spends by organism in the water column. All parameters used in calculation are taken from Pröhl (2003)15 and Vives i Battle et al. (2004)16. The total dose is then calculated by summating the external and internal doses. More

  • in

    Functional vegetation change over millennia

    Adeleye, M. A., Haberle, S. G., Gallagher, R., Andrew, S. C. & Herbert, A. Nat. Ecol. Evol., https://doi.org/10.1038/s41559-022-01943-4 (2023).Mokany, K. et al. Ecography 2022, e06426 (2022).Article 

    Google Scholar 
    Violle, C. et al. Oikos 116, 882–892 (2007).Article 

    Google Scholar 
    Birks, H. J. B. Front. Ecol. Evol. 8, 166 (2020).Article 

    Google Scholar 
    Reitalu, T. et al. J. Veg. Sci. 26, 911–922 (2015).Article 

    Google Scholar 
    Brussel, T. & Brewer, S. C. Front. Ecol. Evol. 8, 564609 (2021).Article 

    Google Scholar 
    van der Sande, M. T. et al. Ecol. Lett. 22, 925–935 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Veeken, A., Santos, M. J., McGowan, S., Davies, A. L. & Schrodt, F. Ecol. Lett. 25, 1937–1951 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Suárez-Castro, A. F., Raymundo, M., Bimler, M. & Mayfield, M. M. Ecography 2022, e05844 (2022).Article 

    Google Scholar 
    Biggs, C. R. et al. Ecosphere 11, e03184 (2020).Article 

    Google Scholar  More

  • in

    Changing plant functional diversity over the last 12,000 years provides perspectives for tracking future changes in vegetation communities

    Wingard, G. L., Bernhardt, C. E. & Wachnicka, A. H. The role of paleoecology in restoration and resource management—the past as a guide to future decision-making: review and example from the Greater Everglades ecosystem, U.S.A. Front. Ecol. Evol 5, 11 (2017).Article 

    Google Scholar 
    Gillson, L., Dirk, C. & Gell, P. Using long-term data to inform a decision pathway for restoration of ecosystem resilience. Anthropocene 36, 100315 (2021).Article 

    Google Scholar 
    Nieto-Lugilde, D. et al. Time to better integrate paleoecological research infrastructures with neoecology to improve understanding of biodiversity long-term dynamics and to inform future conservation. Environ. Res. Lett. 16, 095005 (2021).Article 

    Google Scholar 
    Leo, G. A. D. & Levin, S. A. The multifaceted aspects of ecosystem integrity. Conserv. Ecol. 1, 3 (1997).
    Google Scholar 
    Mason, N. & Mouillot, D. in Encyclopedia of Biodiversity (ed. Levin, S. A.) 597–608 (Elsevier, 2013).Carvalho, F. et al. A method for reconstructing temporal changes in vegetation functional trait composition using Holocene pollen assemblages. PLoS ONE 14, e0216698 (2019).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Brussel, T. & Brewer, S. C. Functional paleoecology and the pollen-plant functional trait linkage. Front. Ecol. Evol 8, 564609 (2021).Article 

    Google Scholar 
    Brussel, T., Minckley, T. A., Brewer, S. C. & Long, C. J. Community-level functional interactions with fire track long-term structural development and fire adaptation. J. Veg. Sci. 29, 450–458 (2018).Article 

    Google Scholar 
    Barboni, D. et al. Relationships between plant traits and climate in the Mediterranean region: a pollen data analysis. J. Veg. Sci. 15, 635–646 (2004).Article 

    Google Scholar 
    Reitalu, T. et al. Novel insights into post-glacial vegetation change: functional and phylogenetic diversity in pollen records. J. Veg. Sci. 26, 911–922 (2015).Article 

    Google Scholar 
    Blaus, A. et al. Modern pollen-plant diversity relationships inform palaeoecological reconstructions of functional and phylogenetic diversity in calcareous fens. Front. Ecol. Evol 8, 207 (2020).Article 

    Google Scholar 
    Morris, J. L. et al. Stable or seral? Fire-driven alternative states in aspen forests of western North America. Biol. Lett. 15, 20190011 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ordonez, A. & Svenning, J.-C. Greater tree species richness in eastern North America compared to Europe is coupled to denser, more clustered functional trait space filling, not to trait space expansion. Glob. Ecol. Biogeogr. 27, 1288–1299 (2018).Article 

    Google Scholar 
    van der Sande, M. T. et al. A 7000-year history of changing plant trait composition in an Amazonian landscape; the role of humans and climate. Ecol. Lett. 22, 925–935 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lacourse, T. & Adeleye, M. A. Climate and species traits drive changes in Holocene forest composition along an elevation gradient in Pacific Canada. Front. Ecol. Evol 10, 838545 (2022).Article 

    Google Scholar 
    Lacourse, T. Environmental change controls postglacial forest dynamics through interspecific differences in life-history traits. Ecology 90, 2149–2160 (2009).Article 
    PubMed 

    Google Scholar 
    Veeken, A., Santos, M. J., McGowan, S., Davies, A. L. & Schrodt, F. Pollen-based reconstruction reveals the impact of the onset of agriculture on plant functional trait composition. Ecol. Lett. 25, 1937–1951 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ellis, E. C., Antill, E. C. & Kreft, H. All is not loss: plant biodiversity in the anthropocene. PLoS ONE 7, e30535 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    GILL, A. M. Fire and the Australian flora: a review. Aust. For. 38, 4–25 (1975).Article 

    Google Scholar 
    Crisp, M. D., Burrows, G. E., Cook, L. G., Thornhill, A. H. & Bowman, D. M. J. S. Flammable biomes dominated by eucalypts originated at the Cretaceous–Palaeogene boundary. Nat. Commun. 2, 193 (2011).Article 
    PubMed 

    Google Scholar 
    Keith, D. A. Australian Vegetation (Cambridge Univ. Press, 2017).Woinarski, J. C. Z., Burbidge, A. A. & Harrison, P. L. Ongoing unraveling of a continental fauna: decline and extinction of Australian mammals since European settlement. Proc. Natl Acad. Sci. USA 112, 4531–4540 (2015).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Broadhurst, L. & Coates, D. Plant conservation in Australia: current directions and future challenges. Plant Divers. 39, 348–356 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Adeleye, M. A., Connor, S. E., Haberle, S. G., Herbert, A. & Brown, J. European colonization and the emergence of novel fire regimes in southeast Australia. Anthr. Rev. https://doi.org/10.1177/205301962110446 (2021).Gallagher, R. V. et al. High fire frequency and the impact of the 2019–2020 megafires on Australian plant diversity. Divers. Distrib. 27, 1166–1179 (2021).Article 

    Google Scholar 
    Gallagher, R. V. et al. An integrated approach to assessing abiotic and biotic threats to post-fire plant species recovery: lessons from the 2019–2020 Australian fire season. Glob. Ecol. Biogeogr. 31, 2056–2069.Mariani, M. et al. Disruption of cultural burning promotes shrub encroachment and unprecedented wildfires. Front. Ecol. Environ. 20, 292–300 (2022).Article 

    Google Scholar 
    Williams, A. N., Mooney, S. D., Sisson, S. A. & Marlon, J. Exploring the relationship between Aboriginal population indices and fire in Australia over the last 20,000 years. Palaeogeogr. Palaeoclimatol. Palaeoecol. 432, 49–57 (2015).Article 

    Google Scholar 
    Bird, M. I., O’Grady, D. & Ulm, S. Humans, water, and the colonization of Australia. Proc. Natl Acad. Sci. USA 113, 11477–11482 (2016).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Adeleye, M. A., Haberle, S. G., Connor, S. E., Stevenson, J. & Bowman, D. M. J. S. Indigenous fire-managed landscapes in Southeast Australia during the Holocene—new insights from the Furneaux Group Islands, Bass Strait. Fire 4, 17 (2021).Article 

    Google Scholar 
    Fletcher, M.-S., Romano, A., Connor, S., Mariani, M. & Maezumi, S. Y. Catastrophic bushfires, Indigenous fire knowledge and reframing science in Southeast Australia. Fire 4, 61 (2021).Article 

    Google Scholar 
    Fletcher, M.-S., Hall, T. & Alexandra, A. N. The loss of an indigenous constructed landscape following British invasion of Australia: an insight into the deep human imprint on the Australian landscape. Ambio 50, 138–149 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Adeleye, M. A. et al. Long-term drivers of vegetation turnover in Southern Hemisphere temperate ecosystems. Glob. Ecol. Biogeogr. 30, 557–571 (2021).Article 

    Google Scholar 
    Kershaw, A. P., D’Costa, D. M., McEwen Mason, J. R. C. & Wagstaff, B. E. Palynological evidence for Quaternary vegetation and environments of mainland southeastern Australia. Quat. Sci. Rev. 10, 391–404 (1991).Article 

    Google Scholar 
    Colhoun, E. A. & Shimeld, P. W. in Peopled Landscapes: Archaeological and Biogeographic Approaches to Landscapes (eds. Haberle, S. G. & David, B.) 297–328 (ANU Press, 2012).Madani, N. et al. Future global productivity will be affected by plant trait response to climate. Sci. Rep. 8, 2870 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Squire, D. T. et al. Likelihood of unprecedented drought and fire weather during Australia’s 2019 megafires. npj Clim. Atmos. Sci. 4, 64 (2021).Article 

    Google Scholar 
    Ukkola, A. M., De Kauwe, M. G., Roderick, M. L., Abramowitz, G. & Pitman, A. J. Robust future changes in meteorological drought in CMIP6 projections despite uncertainty in precipitation. Geophys. Res. Lett. 47, e2020GL087820 (2020).Article 

    Google Scholar 
    Mori, A. S., Furukawa, T. & Sasaki, T. Response diversity determines the resilience of ecosystems to environmental change. Biol. Rev. 88, 349–364 (2013).Article 
    PubMed 

    Google Scholar 
    Arias, P. A. et al. In Climate Change 2021: The Physical Science Basis (eds. Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).Laliberté, E., Legendre, P. & Shipley, B. FD: measuring (FD) from multiple traits, and other tools for functional ecology. R package version 1.0-12 (2014).Laliberté, E. & Legendre, P. A distance-based framework for measuring from multiple traits. Ecology 91, 299–305 (2010).Article 
    PubMed 

    Google Scholar 
    Tilman, D. in Encyclopedia of Biodiversity (ed. Levin, S. A.) 109–120 (Elsevier, 2001).Fletcher, M.-S. & Moreno, P. I. Have the Southern Westerlies changed in a zonally symmetric manner over the last 14,000 years? A hemisphere-wide take on a controversial problem. Quat. Int. 253, 32–46 (2012).Article 

    Google Scholar 
    Markgraf, V., Bradbury, J. P. & Busby, J. R. Paleoclimates in Southwestern Tasmania during the last 13,000 years. PALAIOS 1, 368 (1986).Article 

    Google Scholar 
    Moros, M. et al. Hydrographic shifts south of Australia over the last deglaciation and possible interhemispheric linkages. Quat. Res. 102, 130–141 (2021).Article 

    Google Scholar 
    Perner, K. et al. Heat export from the tropics drives mid to late Holocene palaeoceanographic changes offshore southern Australia. Quat. Sci. Rev. 180, 96–110 (2018).Article 

    Google Scholar 
    Mariani, M. & Fletcher, M.-S. Long-term climate dynamics in the extra-tropics of the South Pacific revealed from sedimentary charcoal analysis. Quat. Sci. Rev. 173, 181–192 (2017).Article 

    Google Scholar 
    McWethy, D. B. et al. A conceptual framework for predicting temperate ecosystem sensitivity to human impacts on fire regimes. Glob. Ecol. Biogeogr. 22, 900–912 (2013).Article 

    Google Scholar 
    Baker, A. G., Catterall, C. & Benkendorff, K. Invading rain forest pioneers initiate positive fire suppression feedbacks that reinforce shifts from open to closed forest in eastern Australia. J. Veg. Sci. 32, e13102 (2021).Article 

    Google Scholar 
    Lambeck, K. & Chappell, J. Sea level change through the last glacial cycle. Science 292, 679–686 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Sloss, C. R., Murray-Wallace, C. V. & Jones, B. G. Holocene sea-level change on the southeast coast of Australia: a review. Holocene 17, 999–1014 (2007).Article 

    Google Scholar 
    Adeleye, M. A. et al. Holocene heathland development in temperate oceanic Southern Hemisphere: key drivers in a global context. J. Biogeogr. 48, 1048–1062 (2021).Article 

    Google Scholar 
    McWethy, D. B., Haberle, S. G., Hopf, F. & Bowman, D. M. J. S. Aboriginal impacts on fire and vegetation on a Tasmanian island. J. Biogeogr. 44, 1319–1330 (2017).Article 

    Google Scholar 
    Hope, G. Vegetation and fire response to late Holocene human occupation in island and mainland north west Tasmania. Quat. Int. 59, 47–60 (1999).Article 

    Google Scholar 
    Sim, R. The Archaeology of Isolation? Prehistoric Occupation in the Furneaux Group of Islands, Bass Strait, Tasmania. PhD thesis, Australian National Univ. (1998).Lourandos, H. Intensification: a late Pleistocene-Holocene archaeological sequence from Southwestern Victoria. Archaeol. Ocean. 18, 81–94 (1983).Article 

    Google Scholar 
    Bowman, D. M. J. S. The impact of Aboriginal landscape burning on the Australian biota. New Phytol. 140, 385–410 (1998).Article 
    CAS 
    PubMed 

    Google Scholar 
    Iversen, J. in Systematics of Today (ed. Hedberg, O.) 210–215 (Acta Universitatis Upsaliensis/Uppsala Universitets Årsskrift, 1958).Colhoun, E. A. Application of Iversen’s glacial–interglacial cycle to interpretation of the late last glacial and Holocene vegetation history of western Tasmania. Quat. Sci. Rev. 15, 557–580 (1996).Article 

    Google Scholar 
    Adeleye, M. A., Haberle, S. G., Ondei, S. & Bowman, D. M. J. S. Ecosystem transformation following the mid-nineteenth century cessation of Aboriginal fire management in Cape Pillar, Tasmania. Reg. Environ. Change 22, 99 (2022).Article 

    Google Scholar 
    Mccann, K. The diversity–stability debate. Nature 405, 228–233 (2000).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hallett, L. M., Stein, C. & Suding, K. N. Functional diversity increases ecological stability in a grazed grassland. Oecologia 183, 831–840 (2017).Article 
    PubMed 

    Google Scholar 
    Bello, Fde et al. Functional trait effects on ecosystem stability: assembling the jigsaw puzzle. Trends Ecol. Evol. 36, 822–836 (2021).Article 
    PubMed 

    Google Scholar 
    Lucini, F. A., Morone, F., Tomassone, M. S. & Makse, H. A. Diversity increases the stability of ecosystems. PLoS ONE 15, e0228692 (2020).Article 

    Google Scholar 
    Tilman, D., Reich, P. B. & Knops, J. M. H. Biodiversity and ecosystem stability in a decade-long grassland experiment. Nature 441, 629–632 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Gallagher, R. V., Hughes, L. & Leishman, M. R. Species loss and gain in communities under future climate change: consequences for functional diversity. Ecography 36, 531–540 (2013).Article 

    Google Scholar 
    Song, Y., Wang, P., Li, G. & Zhou, D. Relationships between and ecosystem functioning: a review. Acta Ecol. Sin. 34, 85–91 (2014).Article 
    CAS 

    Google Scholar 
    Li, W. et al. Plant can be independent of species diversity: observations based on the impact of 4-yrs of nitrogen and phosphorus additions in an alpine meadow. PLoS ONE 10, e0136040 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lewis, C. J., Huang, Y., Siems, S. T. & Manton, M. J. Wintertime orographic precipitation over western Tasmania. J. South. Hemisphere Earth Syst. Sci. 68, 22–40 (2018).Article 

    Google Scholar 
    Andrew, S. C. et al. Functional diversity of the Australian flora: strong links to species richness and climate. J. Veg. Sci. 32, e13018 (2021).Article 

    Google Scholar 
    Biswas, S. R. & Mallik, A. U. Species diversity and relationship varies with disturbance intensity. Ecosphere 2, art52 (2011).Article 

    Google Scholar 
    Gallagher, R. V. et al. A guide to using species trait data in conservation. One Earth 4, 927–936 (2021).Article 

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

    Google Scholar 
    Harris, S. & Kitchener, A. From Forest to Fjaeldmark. Descriptions of Tasmania’s Vegetation (Department of Primary Industries, Water and Environment, Tasmania, 2005).Adeleye, M. A., Haberle, S. G., McWethy, D., Connor, S. E. & Stevenson, J. Environmental change during the last glacial on an ancient land bridge of southeast Australia. J. Biogeogr. 48, 2946–2960 (2021).Article 

    Google Scholar 
    Hopf, F. V. L., Colhoun, E. A. & Barton, C. E. Late-glacial and Holocene record of vegetation and climate from Cynthia Bay, Lake St Clair, Tasmania. J. Quat. Sci. 15, 725–732 (2000).Article 

    Google Scholar 
    Stahle, L. N., Whitlock, C. & Haberle, S. G. A 17,000-year-long record of vegetation and fire from Cradle Mountain National Park, Tasmania. Front. Ecol. Evol 4, 82 (2016).Article 

    Google Scholar 
    Michael-Shawn, F. et al. The influence of climatic change, fire and species invasion on a Tasmanian temperate rainforest system over the past 18,000 years. Quat. Sci. Rev. 260, 106824 (2021).Article 

    Google Scholar 
    Climate and Water Availability in South-Eastern Australia: A Synthesis of Findings From Phase 2 of the South-Eastern Australian Climate initiative (SEACI) (CSIRO, 2012); https://doi.org/10.4225/08/584af3986fe96Australian Climate Influences (Commonwealth of Australia, Bureau of Meteorology, 2010); http://www.bom.gov.au/watl/about-weather-and-climate/australian-climate-influences.shtmlRisbey, J. S., Pook, M. J., McIntosh, P. C., Wheeler, M. C. & Hendon, H. H. On the remote drivers of rainfall variability in Australia. Mon. Weather Rev. 137, 3233–3253 (2009).Article 

    Google Scholar 
    Mariani, M., Fletcher, M.-S., Holz, A. & Nyman, P. ENSO controls interannual fire activity in southeast Australia. Geophys. Res. Lett. 43, 10891–10900 (2016).Article 

    Google Scholar 
    Mariani, M. & Fletcher, M.-S. The Southern Annular Mode determines interannual and centennial-scale fire activity in temperate southwest Tasmania, Australia. Geophys. Res. Lett. 43, 1702–1709 (2016).Article 

    Google Scholar 
    Herbert, A. V. & Harrison, S. P. Evaluation of a modern-analogue methodology for reconstructing Australian palaeoclimate from pollen. Rev. Palaeobot. Palynol. 226, 65–77 (2016).Article 

    Google Scholar 
    Blaauw, M. et al. rbacon: Age-depth modelling using Bayesian statistics. R package version 4.2.0 (2022).Hogg, A. G. et al. SHCal20 Southern Hemisphere calibration, 0–55,000 years cal BP. Radiocarbon 62, 759–778 (2020).Article 
    CAS 

    Google Scholar 
    Falster, D. et al. AusTraits, a curated plant trait database for the Australian flora. Sci. Data 8, 254 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pérez-Harguindeguy, N. et al. Corrigendum to: New handbook for standardised measurement of plant functional traits worldwide. Aust. J. Bot. 64, 715–716 (2016).Article 

    Google Scholar 
    Wright, I. J. et al. Global climatic drivers of leaf size. Science 357, 917–921 (2017).Article 
    CAS 
    PubMed 

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

    Google Scholar 
    Westoby, M., Falster, D. S., Moles, A. T., Vesk, P. A. & Wright, I. J. Plant ecological strategies: some leading dimensions of variation between species. Annu. Rev. Ecol. Syst. 33, 125–159 (2002).Article 

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

    Google Scholar 
    Leishman, M. R. & Westoby, M. The role of seed size in seedling establishment in dry soil conditions—experimental evidence from semi-arid species. J. Ecol. 82, 249–258 (1994).Article 

    Google Scholar 
    Falster, D. S. & Westoby, M. Plant height and evolutionary games. Trends Ecol. Evol. 18, 337–343 (2003).Article 

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

    Google Scholar 
    Mason, N. W. H., Mouillot, D., Lee, W. G. & Wilson, J. B. Functional richness, functional evenness and functional divergence: the primary components of functional diversity. Oikos 111, 112–118 (2005).Article 

    Google Scholar 
    Pakeman, R. J. Functional trait metrics are sensitive to the completeness of the species’ trait data? Methods Ecol. Evol. 5, 9–15 (2014).Article 

    Google Scholar 
    Scheiner, S. M., Kosman, E., Presley, S. J. & Willig, M. R. Decomposing. Methods Ecol. Evol. 8, 809–820 (2017).Article 

    Google Scholar 
    Ripley, B. et al. MASS: Support functions and datasets for venables and Ripley’s MASS. R package version ??? (2022).Moy, C. M., Seltzer, G. O., Rodbell, D. T. & Anderson, D. M. Variability of El Niño/Southern Oscillation activity at millennial timescales during the Holocene epoch. Nature 420, 162–165 (2002).Article 
    CAS 
    PubMed 

    Google Scholar  More

  • in

    Coral reefs and coastal tourism in Hawaii

    Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).Article 
    CAS 

    Google Scholar 
    Arkema, K. K., Fisher, D. M., Wyatt, K., Wood, S. A. & Payne, H. J. Advancing sustainable development and protected area mManagement with social media-based tourism data. Sustainability 13, 2427 (2021).Article 

    Google Scholar 
    Tourism in the 2030 Agenda (UNWTO, 2015); https://www.unwto.org/tourism-in-2030-agendaCowburn, B., Moritz, C., Birrell, C., Grimsditch, G. & Abdulla, A. Can luxury and environmental sustainability co-exist? Assessing the environmental impact of resort tourism on coral reefs in the Maldives. Ocean Coast. Manag. 158, 120–127 (2018).Article 

    Google Scholar 
    Lin, B. Close encounters of the worst kind: reforms needed to curb coral reef damage by recreational divers. Coral Reefs 40, 1429–1435 (2021).Article 

    Google Scholar 
    Asner, G. P. et al. Large-scale mapping of live corals to guide reef conservation. Proc. Natl Acad. Sci. USA 117, 33711–33718 (2020).Article 
    CAS 

    Google Scholar 
    Wood, S. A., Guerry, A. D., Silver, J. M. & Lacayo, M. Using social media to quantify nature-based tourism and recreation. Sci. Rep. 3, 2976 (2013).Article 

    Google Scholar 
    Wood, S. A. et al. Next-generation visitation models using social media to estimate recreation on public lands. Sci. Rep. 10, 15419 (2020).Article 
    CAS 

    Google Scholar 
    Hausmann, A. et al. Social media data can be used to understand tourists’ preferences for nature-based experiences in protected areas. Conserv. Lett. 11, e12343 (2018).Article 

    Google Scholar 
    Tenkanen, H. et al. Instagram, Flickr, or Twitter: assessing the usability of social media data for visitor monitoring in protected areas. Sci. Rep. 7, 17615 (2017).Article 

    Google Scholar 
    Sessions, C., Wood, S. A., Rabotyagov, S. & Fisher, D. M. Measuring recreational visitation at U.S. National Parks with crowd-sourced photographs. J. Environ. Manag. 183, 703–711 (2016).Article 

    Google Scholar 
    Mancini, F., Coghill, G. M. & Lusseau, D. Using social media to quantify spatial and temporal dynamics of nature-based recreational activities. PLoS One 13, e0200565 (2018).Article 

    Google Scholar 
    Spalding, M. et al. Mapping the global value and distribution of coral reef tourism. Mar. Policy 82, 104–113 (2017).Article 

    Google Scholar 
    van Zanten, B. T. et al. Continental-scale quantification of landscape values using social media data. Proc. Natl Acad. Sci. USA 113, 12974–12979 (2016).Article 

    Google Scholar 
    Department of Land and Natural Resources. Beach Access (Office of Conservation and Coastal Lands, 2013); https://dlnr.hawaii.gov/occl/beach-access/Mobile LTE Coverage Map (Federal Communications Commission, 2021).Arkema, K. K. et al. Embedding ecosystem services in coastal planning leads to better outcomes for people and nature. Proc. Natl Acad. Sci. USA 112, 7390–7395 (2015).Article 
    CAS 

    Google Scholar 
    Neuvonen, M., Pouta, E., Puustinen, J. & Sievänen, T. Visits to national parks: effects of park characteristics and spatial demand. J. Nat. Conserv. 18, 224–229 (2010).Article 

    Google Scholar 
    Rodgers, K., Cox, E. & Newtson, C. Effects of mechanical fracturing and experimental trampling on hawaiian corals. Environ. Manag. 31, 0377–0384 (2003).Article 

    Google Scholar 
    Downs, C. A. et al. Toxicopathological effects of the sunscreen UV filter, oxybenzone (benzophenone-3), on coral planulae and cultured primary cells and its environmental contamination in Hawaii and the U.S. Virgin Islands. Arch. Environ. Contam. Toxicol. 70, 265–288 (2016).Article 
    CAS 

    Google Scholar 
    Côté, I. M., Darling, E. S. & Brown, C. J. Interactions among ecosystem stressors and their importance in conservation. Proc. R. Soc. B. 283, 20152592 (2016).Article 

    Google Scholar 
    Bruno, J. F. & Valdivia, A. Coral reef degradation is not correlated with local human population density. Sci. Rep. 6, 29778 (2016).Article 
    CAS 

    Google Scholar 
    Johnson, J. V., Dick, J. T. A. & Pincheira-Donoso, D. Local anthropogenic stress does not exacerbate coral bleaching under global climate change. Glob. Ecol. Biogeogr. (2022).Darling, E. S., McClanahan, T. R. & Côté, I. M. Combined effects of two stressors on Kenyan coral reefs are additive or antagonistic, not synergistic. Conserv. Lett. 3, 122–130 (2010).Article 

    Google Scholar 
    Severino, S. J. L., Rodgers, K. S., Stender, Y. & Stefanak, M. Hanauma Bay Biological Carrying Capacity Survey 2019–20 2nd Annual Report https://www.honolulu.gov/rep/site/dpr/hanaumabay_docs/Hanauma_Bay_Carrying_Capacity_Report_August_2020.pdf (City and County of Honolulu Parks and Recreation Department, 2020).Selenium WebDriver (Software Freedom Conservancy, 2022); https://www.selenium.dev/documentation/en/webdriver/Geospatial Data Portal. Hawaii Statewide GIS Program (Hawaii State Office of Planning, 2017); https://geoportal.hawaii.gov/Wedding, L. M. et al. Advancing the integration of spatial data to map human and natural drivers on coral reefs. PLoS One 13, e0189792 (2018).Article 

    Google Scholar 
    Nguyen, T., Liquet, B., Mengersen, K. & Sous, D. Mapping of coral reefs with multispectral satellites: a review of recent papers. Remote Sens. 13, 4470 (2021).Article 

    Google Scholar 
    Wicaksono, P., Aryaguna, P. A. & Lazuardi, W. Benthic habitat mapping model and cross validation using machine-learning classification algorithms. Remote Sens. 11, 1279 (2019).Article 

    Google Scholar  More

  • in

    Resolving the intricate role of climate in litter decomposition

    Swift, M. J., Heal, O. W. & Anderson, J. M. Decomposition in Terrestrial Ecosystems. Vol. 5.5 (Blackwell, 1979).Aerts, R. Climate, leaf litter chemistry and leaf litter decomposition in terrestrial ecosystems: a triangular relationship. Oikos 79, 439 (1997).Article 

    Google Scholar 
    Makkonen, M. et al. Highly consistent effects of plant litter identity and functional traits on decomposition across a latitudinal gradient. Ecol. Lett. 15, 1033–1041 (2012).Article 

    Google Scholar 
    Coûteaux, M. M., Bottner, P. & Berg, B. Litter decomposition, climate and liter quality. Trends Ecol. Evol. 10, 63–66 (1995).Article 

    Google Scholar 
    Cornwell, W. K. et al. Plant species traits are the predominant control on litter decomposition rates within biomes worldwide. Ecol. Lett. 11, 1065–1071 (2008).Article 

    Google Scholar 
    Bradford, M. A. et al. Climate fails to predict wood decomposition at regional scales. Nat. Clim. Change 4, 625–630 (2014).Article 
    CAS 

    Google Scholar 
    Bradford, M. A., Berg, B., Maynard, D. S., Wieder, W. R. & Wood, S. A. Understanding the dominant controls on litter decomposition. J. Ecol. 104, 229–238 (2016).Article 
    CAS 

    Google Scholar 
    Joly, F.-X. et al. Tree species diversity affects decomposition through modified micro-environmental conditions across European forests. New Phytol. 214, 1281–1293 (2017).Article 
    CAS 

    Google Scholar 
    Bradford, M. A. et al. A test of the hierarchical model of litter decomposition. Nat. Ecol. Evol. 1, 1836–1845 (2017).Article 

    Google Scholar 
    Berg, B. et al. Litter mass loss rates in pine forests of Europe and Eastern United States: some relationships with climate and litter quality. Biogeochemistry 20, 127–159 (1993).Article 

    Google Scholar 
    Powers, J. S. et al. Decomposition in tropical forests: a pan-tropical study of the effects of litter type, litter placement and mesofaunal exclusion across a precipitation gradient. J. Ecol. 97, 801–811 (2009).Article 
    CAS 

    Google Scholar 
    Djukic, I. et al. Early stage litter decomposition across biomes. Sci. Total Environ. 628–629, 1369–1394 (2018).Article 

    Google Scholar 
    Cornelissen, J. H. C. & Thompson, K. Functional leaf attributes predict litter decomposition rate in herbaceous plants. New Phytol. 135, 109–114 (1997).Article 
    CAS 

    Google Scholar 
    Coq, S., Souquet, J.-M., Meudec, E., Cheynier, V. & Hättenschwiler, S. Interspecific variation in leaf litter tannins drives decomposition in a tropical rain forest of French Guiana. Ecology 91, 2080–2091 (2010).Article 

    Google Scholar 
    Sun, T. et al. Contrasting dynamics and trait controls in first-order root compared with leaf litter decomposition. Proc. Natl Acad. Sci. USA 115, 10392–10397 (2018).Article 
    CAS 

    Google Scholar 
    Baeten, L. et al. A novel comparative research platform designed to determine the functional significance of tree species diversity in European forests. Perspect. Plant Ecol. Evol. Syst. 15, 281–291 (2013).Article 

    Google Scholar 
    Hobbie, S. E. et al. Tree species effects on decomposition and forest floor dynamics in a common garden. Ecology 87, 2288–2297 (2006).Article 

    Google Scholar 
    von Arx, G., Graf Pannatier, E., Thimonier, A. & Rebetez, M. Microclimate in forests with varying leaf area index and soil moisture: potential implications for seedling establishment in a changing climate. J. Ecol. 101, 1201–1213 (2013).Article 

    Google Scholar 
    Ayres, E. et al. Home-field advantage accelerates leaf litter decomposition in forests. Soil Biol. Biochem. 41, 606–610 (2009).Article 
    CAS 

    Google Scholar 
    Freschet, G. T., Aerts, R. & Cornelissen, J. H. C. Multiple mechanisms for trait effects on litter decomposition: moving beyond home-field advantage with a new hypothesis. J. Ecol. 100, 619–630 (2012).Article 

    Google Scholar 
    Meentemeyer, V. Macroclimate and lignin control of litter decomposition rates. Ecology 59, 465–472 (1978).Article 
    CAS 

    Google Scholar 
    Currie, W. S. et al. Cross-biome transplants of plant litter show decomposition models extend to a broader climatic range but lose predictability at the decadal time scale. Glob. Change Biol. 16, 1744–1761 (2010).Article 

    Google Scholar 
    Canessa, R. et al. Relative effects of climate and litter traits on decomposition change with time, climate and trait variability. J. Ecol. 109, 447–458 (2021).Article 

    Google Scholar 
    García-Palacios, P., Shaw, E. A., Wall, D. H. & Hättenschwiler, S. Temporal dynamics of biotic and abiotic drivers of litter decomposition. Ecol. Lett. 19, 554–563 (2016).Article 

    Google Scholar 
    Prescott, C. E. Litter decomposition: what controls it and how can we alter it to sequester more carbon in forest soils? Biogeochemistry 101, 133–149 (2010).Article 
    CAS 

    Google Scholar 
    Prescott, C. E. & Vesterdal, L. Decomposition and transformations along the continuum from litter to soil organic matter in forest soils. For. Ecol. Manage. 498, 119522 (2021).Article 

    Google Scholar 
    Stadler, S. J. in Encyclopedia of World Climatology 89–94 (Springer, 2005).Moore, T. R., Bubier, J. L. & Bledzki, L. Litter decomposition in temperate peatland ecosystems: the effect of substrate and site. Ecosystems 10, 949–963 (2007).Article 

    Google Scholar 
    Austin, A. T. Has water limited our imagination for aridland biogeochemistry. Trends Ecol. Evol. 26, 229–235 (2011).Article 

    Google Scholar 
    Joly, F.-X., Kurupas, K. & Throop, H. Pulse frequency and soil-litter mixing alter the control of cumulative precipitation over litter decomposition. Ecology 98, 2255–2260 (2017).Article 

    Google Scholar 
    Scherer-Lorenzen, M., Bonilla, J. L. & Potvin, C. Tree species richness affects litter production and decomposition rates in a tropical biodiversity experiment. Oikos 116, 2108–2124 (2007).Article 

    Google Scholar 
    Vivanco, L. & Austin, A. T. Tree species identity alters forest litter decomposition through long-term plant and soil interactions in Patagonia, Argentina. J. Ecol. 96, 727–736 (2008).Article 
    CAS 

    Google Scholar 
    Fanin, N. et al. Home‐field advantage of litter decomposition: from the phyllosphere to the soil. New Phytol. 231, 1353–1358 (2021).Article 

    Google Scholar 
    Hättenschwiler, S., Tiunov, A. V. & Scheu, S. Biodiversity and litter decomposition in terrestrial ecosystems. Annu. Rev. Ecol. Evol. Syst. 36, 191–218 (2005).Article 

    Google Scholar 
    Keuskamp, J. A., Dingemans, B. J. J., Lehtinen, T., Sarneel, J. M. & Hefting, M. M. Tea Bag Index: a novel approach to collect uniform decomposition data across ecosystems. Methods Ecol. Evol. 4, 1070–1075 (2013).Article 

    Google Scholar 
    Thakur, M. P. et al. Reduced feeding activity of soil detritivores under warmer and drier conditions. Nat. Clim. Change 8, 75–78 (2018).Article 

    Google Scholar 
    Harrison, A. F., Latter, P. M. & Walton, D. W. H. (eds) Cotton Strip Assay: An Index of Decomposition in Soils (Institute of Terrestrial Ecology, 1988).García-Palacios, P., Maestre, F. T., Kattge, J. & Wall, D. H. Climate and litter quality differently modulate the effects of soil fauna on litter decomposition across biomes. Ecol. Lett. 16, 1045–1053 (2013).Article 

    Google Scholar 
    Garnier, E. et al. Plant functional markers capture ecosystem properties during secondary succession. Ecology 85, 2630–2637 (2004).Article 

    Google Scholar 
    Dawud, S. M. et al. Tree species functional group is a more important driver of soil properties than tree species diversity across major European forest types. Funct. Ecol. 31, 1153–1162 (2017).Article 

    Google Scholar 
    Pollastrini, M. et al. Taxonomic and ecological relevance of the chlorophyll a fluorescence signature of tree species in mixed European forests. New Phytol. 212, 51–65 (2016).Article 
    CAS 

    Google Scholar 
    R Development Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2013).Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Lefcheck, J. S. piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

    Google Scholar  More

  • in

    Ecological insights into soil health according to the genomic traits and environment-wide associations of bacteria in agricultural soils

    Doran JW. Soil health and global sustainability: translating science into practice. Agric Ecosyst Environ. 2002;88:119–27.Article 

    Google Scholar 
    Wander MM, Cihacek LJ, Coyne M, Drijber RA, Grossman JM, Gutknecht JLM, et al. Developments in Agricultural Soil Quality and Health: Reflections by the Research Committee on Soil Organic Matter Management. Front Environ Sci. 2019;7:1–9.Article 

    Google Scholar 
    Stewart RD, Jian J, Gyawali AJ, Thomason WE, Badgley BD, Reiter MS, et al. What we talk about when we talk about soil health. Agric Environ Lett. 2018;3:5–9.Article 

    Google Scholar 
    Rinot O, Levy GJ, Steinberger Y, Svoray T, Eshel G. Soil health assessment: A critical review of current methodologies and a proposed new approach. Sci Total Environ. 2019;648:1484–91.Article 
    CAS 

    Google Scholar 
    Hurisso TT, Culman SW, Zhao K. Repeatability and spatiotemporal variability of emerging soil health indicators relative to routine soil nutrient tests. Soil Sci Soc Am J. 2018;82:939–48.Article 
    CAS 

    Google Scholar 
    Lilburne L, Sparling G, Schipper L. Soil quality monitoring in New Zealand: Development of an interpretative framework. Agric Ecosyst Environ. 2004;104:535–44.Article 

    Google Scholar 
    Moebius-Clune BN, Moebius-Clune DJ, Gugino BK, Idowu OJ, Schindelbeck RR, Ristow AJ, et al. Comprehensive assessment of soil health – the Cornell framework manual, 3rd ed. Ithaca, NY:Cornell University; 2017.Fierer N, Wood SA, Bueno de Mesquita CP. How microbes can, and cannot, be used to assess soil health. Soil Biol Biochem. 2021;153:108111.Article 
    CAS 

    Google Scholar 
    Amsili JP, van Es HM, Schindelbeck RR. Cropping system and soil texture shape soil health outcomes and scoring functions. Soil Secur. 2021;4:100012.Article 

    Google Scholar 
    Wade J, Culman SW, Gasch CK, Lazcano C, Maltais-Landry G, Margenot AJ, et al. Rigorous, empirical, and quantitative: a proposed pipeline for soil health assessments. Soil Biol Biochem. 2022;170:108710.Article 
    CAS 

    Google Scholar 
    Simonin M, Voss KA, Hassett BA, Rocca JD, Wang SY, Bier RL, et al. In search of microbial indicator taxa: shifts in stream bacterial communities along an urbanization gradient. Environ Microbiol. 2019;21:3653–68.Article 

    Google Scholar 
    Bissett A, Brown MV, Siciliano SD, Thrall PH. Microbial community responses to anthropogenically induced environmental change: Towards a systems approach. Ecol Lett. 2013;16:128–39.Article 

    Google Scholar 
    Wilhelm RC, Cardenas E, Maas KR, Leung H, McNeil L, Berch S, et al. Biogeography and organic matter removal shape long-term effects of timber harvesting on forest soil microbial communities. ISME J. 2017;11:2552–68.Article 

    Google Scholar 
    Gibbons SM, Scholz M, Hutchison AL, Dinner AR, Gilbert JA, Colemana ML, et al. Disturbance regimes predictably alter diversity in an ecologically complex bacterial system. MBio. 2016;7:1–10.Article 

    Google Scholar 
    Trivedi P, Delgado-Baquerizo M, Anderson IC, Singh BK. Response of soil properties and microbial communities to agriculture: Implications for primary productivity and soil health indicators. Front Plant Sci. 2016;7:1–13.Article 

    Google Scholar 
    Jiao S, Xu Y, Zhang J, Hao X. Core microbiota in agricultural soils and their potential associations with nutrient cycling. mSystems. 2019;4:1–16.Article 

    Google Scholar 
    Chang HX, Haudenshield JS, Bowen CR, Allen R, Iii W, Parnell JJ, et al. Metagenome-wide association study and machine learning prediction of bulk soil microbiome and crop productivity. Front Microbiol. 2017;8:519.Article 

    Google Scholar 
    Trivedi P, Delgado-Baquerizo M, Jeffries TC, Trivedi C, Anderson IC, Lai K, et al. Soil aggregation and associated microbial communities modify the impact of agricultural management on carbon content. Environ Microbiol. 2017;19:3070–86.Article 
    CAS 

    Google Scholar 
    Armbruster M, Goodall T, Hirsch PR, Ostle N, Puissant J, Fagan KC, et al. Bacterial and archaeal taxa are reliable indicators of soil restoration across distributed calcareous grasslands. Eur J Soil Sci. 2021;72:2430–44.Rieke EL, Cappellazzi SB, Cope M, Liptzin D, Mac Bean G, Greub KLH, et al. Linking soil microbial community structure to potential carbon mineralization: A continental scale assessment of reduced tillage. Soil Biol Biochem. 2022;168:108618.Article 
    CAS 

    Google Scholar 
    Wilhelm RC, Van Es HM, Buckley DH. Predicting measures of soil health using the microbiome and supervised machine learning. Soil Biol Biochem. 2022;164:108472.Article 
    CAS 

    Google Scholar 
    Douglas GM, Maffei VJ, Zaneveld J, Yurgel SN, Brown JR, Taylor CM, et al. PICRUSt2: An improved and customizable approach for metagenome inference 2. bioRxiv. 2020. https://doi.org/10.1101/672295.Gravuer K, Eskelinen A. Nutrient and rainfall additions shift phylogenetically estimated traits of soil microbial communities. Front Microbiol. 2017;8:1–16.Article 

    Google Scholar 
    Chen Y, Maier RM, Barberán A, Neilson JW, Kushwaha P, Maier RM, et al. Life-history strategies of soil microbial communities in an arid ecosystem. ISME J. 2021;15:649–57.Article 
    CAS 

    Google Scholar 
    Fierer N. Embracing the unknown: Disentangling the complexities of the soil microbiome. Nat Rev Microbiol. 2017;15:579–90.Article 
    CAS 

    Google Scholar 
    Malik AA, Martiny JBHH, Brodie EL, Martiny AC, Treseder KK, Allison SD, et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 2020;14:1–9.Article 
    CAS 

    Google Scholar 
    Roller BRK, Stoddard SF, Schmidt TM. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat Microbiol. 2016;1:1–7.Nunan N, Schmidt H, Raynaud X, Schmidt H. The ecology of heterogeneity: Soil bacterial communities and C dynamics. Philos Trans R Soc B Biol Sci. 2020;375:20190249.Article 
    CAS 

    Google Scholar 
    Grime JP. Evidence for the existence of three primary strategies in plants and its relevance for ecological and evolutionary theory. Am Nat. 1977;111:1169–94.Article 

    Google Scholar 
    Barnett S, Youngblut ND, Koechli CN, Buckley DH. Multisubstrate DNA stable isotope probing reveals guild structure of bacteria that mediate soil carbon cycling. PNAS. 2021;118:e2115292118.Wilhelm RC, Pepe-Ranney C, Weisenhorn P, Lipton M, Buckley DH. Competitive exclusion and metabolic dependency among microorganisms structure the cellulose economy of an agricultural soil. MBio. 2021;12:1–19.Article 

    Google Scholar 
    Schmidt R, Gravuer K, Bossange AV, Mitchell J, Scow K. Long-term use of cover crops and no-till shift soil microbial community life strategies in agricultural soil. PLoS ONE. 2018;13:1–19.Article 

    Google Scholar 
    Neal AL, Hughes D, Clark IM, Jansson JK, Hirsch PR. Microbiome Aggregated Traits and Assembly Are More Sensitive to Soil Management than Diversity. mSystems 2021;6:e0105620.Lupatini M, Korthals GW, de Hollander M, Janssens TKS, Kuramae EE. Soil microbiome is more heterogeneous in organic than in conventional farming system. Front Microbiol. 2017;7:1–13.Article 

    Google Scholar 
    Koechli C, Campbell AN, Pepe-ranney C, Buckley DH. Assessing fungal contributions to cellulose degradation in soil by using high- throughput stable isotope probing. Soil Biol Biochem. 2019;130:150–8.Article 
    CAS 

    Google Scholar 
    Furtak K, Grządziel J, Gałązka A, Niedźwiecki J. Prevalence of unclassified bacteria in the soil bacterial community from floodplain meadows (fluvisols) under simulated flood conditions revealed by a metataxonomic approachss. Catena. 2020;188:104448.Article 
    CAS 

    Google Scholar 
    Schmidt R, Mitchell J, Scow K. Cover cropping and no-till increase diversity and symbiotroph: saprotroph ratios of soil fungal communities. Soil Biol Biochem. 2019;129:99–109.Article 
    CAS 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.Article 
    CAS 

    Google Scholar 
    Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017;11:2639–43.Article 

    Google Scholar 
    Levy R, Borenstein E. Reverse Ecology: From systems to environments and back. Adv Exp Med Biol. 2012;751:329–45.Article 
    CAS 

    Google Scholar 
    Nguyen NH, Song Z, Bates ST, Branco S, Tedersoo L, Menke J, et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 2016;20:241–8.Article 

    Google Scholar 
    Hamilton JP, Neeno-Eckwall EC, Adhikari BN, Perna NT, Tisserat N, Leach JE, et al. The Comprehensive Phytopathogen Genomics Resource: a web-based resource for data-mining plant pathogen genomes. Database. 2011;2011:bar053.Detheridge AP, Brand G, Fychan R, Crotty FV, Sanderson R, Griffith GW, et al. The legacy effect of cover crops on soil fungal populations in a cereal rotation. Agric Ecosyst Environ. 2016;228:49–61.Article 

    Google Scholar 
    McKenna TP, Crews TE, Kemp L, Sikes BA. Community structure of soil fungi in a novel perennial crop monoculture, annual agriculture, and native prairie reconstruction. PLoS ONE. 2020;15:1–15.Article 

    Google Scholar 
    Rocca JD, Simonin M, Blaszczak JR, Ernakovich JG, Gibbons SM, Midani FS, et al. The Microbiome Stress Project: Toward a global meta-analysis of environmental stressors and their effects on microbial communities. Front Microbiol. 2019;9:3272.Article 

    Google Scholar 
    Ramirez KS, Knight CG, De Hollander M, Brearley FQ, Constantinides B, Cotton A, et al. Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nat Microbiol. 2018;3:189–96.Article 
    CAS 

    Google Scholar 
    Thompson LR, Sanders JG, McDonald D, Amir A, Ladau J, Locey KJ, et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature. 2017;551:457–63.Article 
    CAS 

    Google Scholar 
    Lagkouvardos I, Joseph D, Kapfhammer M, Giritli S, Horn M, Haller D, et al. IMNGS: A comprehensive open resource of processed 16S rRNA microbial profiles for ecology and diversity studies. Sci Rep. 2016;6:1–9.Article 

    Google Scholar 
    Jurburg SD, Konzack M, Eisenhauer N, Heintz-Buschart A. The archives are half-empty: a field-wide assessment of the availability of microbial community sequencing data. Commun Biol. 2020;3:474.Emerson JB, Everhart SE, Eversole K, Frost KE, Herr JR, Huerta AI, et al. Community-driven metadata standards for agricultural microbiome research. Phytobiomes J. 2020; 4:115-121.Anderson TH, Martens R. DNA determinations during growth of soil microbial biomasses. Soil Biol Biochem. 2013;57:487–95.Article 
    CAS 

    Google Scholar 
    Kozich JJ, Westcott SL, Baxter NT, Highlander SK, Schloss PD. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the Miseq Illumina sequencing platform. Appl Environ Microbiol. 2013;79:5112–20.Article 
    CAS 

    Google Scholar 
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–7.Article 
    CAS 

    Google Scholar 
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 2013;41:590–6.Article 

    Google Scholar 
    Harrell F, Dupont C. Hmisc: Harrell miscellaneous. R Package 2015.Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc. 1995;57:289–300.
    Google Scholar 
    Weiss S, Van Treuren W, Lozupone C, Faust K, Friedman J, Deng Y, et al. Correlation detection strategies in microbial data sets vary widely in sensitivity and precision. ISME J. 2016;10:1669–81.Article 
    CAS 

    Google Scholar 
    Mills RH, Dulai PS, Vázquez-Baeza Y, Sauceda C, Daniel N, Gerner RR, et al. Multi-omics analyses of the ulcerative colitis gut microbiome link Bacteroides vulgatus proteases with disease severity. Nat Microbiol. 2022;7:262–76.Article 
    CAS 

    Google Scholar 
    De Cáceres M, Legendre P, De Caceres M, Legendre P. Associations between species and groups of sites: indices and statistical inference. Ecology. 2009;90:3566–74.Article 

    Google Scholar 
    Markowitz VM, Ivanova NN, Szeto E, Palaniappan K, Chu K, Dalevi D, et al. IMG/M: A data management and analysis system for metagenomes. Nucleic Acids Res. 2008;36:534–8.Article 

    Google Scholar 
    Stoddard SF, Smith BJ, Hein R, Roller BRK, Schmidt M. rrnDB: improved tools for interpreting rRNA gene abundance in bacteria and archaea and a new foundation for future development. Nucleic Acids Res. 2015;43:593–8.R Core Team. R: a language and environment for statistical computing. R Foundation. 2020.Wickham H. Reshaping data with the reshape package. J Stat Soft. 2007;21:1–20.Article 

    Google Scholar 
    Wickham H. The split-apply-combine strategy for data analysis. J Stat Soft. 2009;40:1–29.
    Google Scholar 
    Wickham H. Elegant graphics for data analysis. Media. 2009;35:211.
    Google Scholar 
    McMurdie PJ, Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 2013;8:e61217.Grömping U. Relative importance for linear regression in R: the package relaimpo. J Stat Softw. 2006;17:1–27.Article 

    Google Scholar 
    Bastian M, Heymann S. Gephi: an open source software for exploring and manipulating networks. Proc Int AAAI Conf Web Soc Media. 2009:361–2.Hu Y. Efficient, high-quality force-directed graph drawing. Math J. 2006;10:37–71.
    Google Scholar 
    Ranea JAG, Grant A, Thornton JM, Orengo CA. Microeconomic principles explain an optimal genome size in bacteria. Trends Genet. 2005;21:21–5.Article 
    CAS 

    Google Scholar 
    Nielsen DA, Fierer N, Geoghegan JL, Gillings MR, Gumerov V, Madin JS, et al. Aerobic bacteria and archaea tend to have larger and more versatile genomes. Oikos. 2021;130:501–11.Article 
    CAS 

    Google Scholar 
    Chen Y, Leung PM, Wood JL, Bay SK, Kessler AJ, Shelley G, et al. Metabolic flexibility allows bacterial habitat generalists to become dominant in a frequently disturbed ecosystem. ISME J. 2021;15:2986–3004.Article 
    CAS 

    Google Scholar 
    Brewer TE, Handley KM, Carini P, Gilbert JA, Fierer N. Genome reduction in an abundant and ubiquitous soil bacterium ‘Candidatus Udaeobacter copiosus’. Nat Microbiol. 2016;2:16198.Willms IM, Rudolph AY, Göschel I, Bolz SH, Schneider D, Penone C, et al. Globally Abundant “Candidatus Udaeobacter” Benefits from Release of Antibiotics in Soil and Potentially Performs Trace Gas Scavenging. mSphere. 2020;5:1–17.Article 

    Google Scholar 
    Kaboré OD, Godreuil S, Drancourt M. Planctomycetes as host-associated bacteria: a perspective that holds promise for their future isolations, by mimicking their native environmental niches in clinical microbiology laboratories. Front Cell Infect Microbiol. 2020;10:1–19.Article 

    Google Scholar 
    Martens-Habbena W, Berube PM, Urakawa H, De La Torre JR, Stahl DA. Ammonia oxidation kinetics determine niche separation of nitrifying Archaea and Bacteria. Nature. 2009;461:976–9.Article 
    CAS 

    Google Scholar 
    Zhalnina K, De Quadros PD, Gano KA, Davis-Richardson A, Fagen JR, Brown CT, et al. Ca. Nitrososphaera and Bradyrhizobium are inversely correlated and related to agricultural practices in long-term field experiments. Front Microbiol. 2013;4:1–13.Article 

    Google Scholar 
    Land M, Hauser L, Jun S, Nookaew I, Leuze MR, Ahn T, et al. Insights from 20 years of bacterial genome sequencing. Funct Integr Genom. 2015;15:141–61.Article 
    CAS 

    Google Scholar 
    Gil R, Latorre A, Postal A. Factors behind junk DNA in bacteria. Genes (Basel). 2012;3:634–50.Article 

    Google Scholar 
    Williamson KE, Radosevich M, Wommack KE. Abundance and diversity of viruses in six Delaware soils. Appl Environ Microbiol. 2005;71:3119–25.Article 
    CAS 

    Google Scholar 
    Williamson KE, Corzo KA, Drissi CL, Buckingham JM, Thompson CP, Helton RR. Estimates of viral abundance in soils are strongly influenced by extraction and enumeration methods. Biol Fertil Soils. 2013;49:857–69.Article 

    Google Scholar 
    Van Goethem MW, Swenson TL, Trubl G, Roux S, Northen TR. Characteristics of wetting-induced bacteriophage blooms in biological soil crust. MBio. 2019;10:e02287-19.Westra ER, Van Houte S, Gandon S, Whitaker R, Van Houte S, Gandon S, et al. The ecology and evolution of microbial CRISPR-Cas adaptive immune systems. Philos Trans R Soc B Biol Sci. 2019;374:20190101.Martinez-Gutierrez CA, Aylward FO. Genome size distributions in bacteria and archaea are strongly linked to evolutionary history at broad phylogenetic scales. PLoS Genet. 2022;18:1–17.Article 

    Google Scholar 
    Saifuddin M, Bhatnagar JM, Finzi AC, Segrè D, Finzi AC. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat Commun. 2019;10:1–10.Article 
    CAS 

    Google Scholar  More