More stories

  • in

    New Permian radiolarians from east Asia and the quantitative reconstruction of their evolutionary and ecological significances

    1.Aitchison, J. C., Suzuki, N., Caridroit, M., Danelian, T. & Noble, P. Paleozoic radiolarian biostratigraphy. Geodiversitas 393, 503–531 (2017).Article 

    Google Scholar 
    2.Wang, Y. J., Luo, H. & Yang, Q. Late Paleozoic radiolarians in the Qinfang area, southeast Guangxi. University of Science and Technology of China, Anhui, 127 p. (2012) (In Chinese with English abstract).3.Nakagawa, T. & Wakita, K. Morphological insights from extremely well-preserved Parafollicucullus (Radiolaria, Order Albaillellaria) from a probable Roadian (Guadalupian, middle Permian) manganese nodule in the Nishiki Group of the Akiyoshi Belt, Southwest Japan. Paleontol. Res. 24, 161–177 (2020).Article 

    Google Scholar 
    4.Saesaengseerung, D., Agematsu, S., Sashida, K. & Sardsud, A. Discovery of Lower Permian radiolarian and conodont faunas from the bedded chert of the Chanthaburi area along the Sra Kaeo suture zone, eastern Thailand. Paleontol. Res. 13, 119–138 (2009).Article 

    Google Scholar 
    5.Nestell, G. P. & Nestell, M. K. Roadian (earliest Guadalupian, Middle Permian) radiolarians from the Guadalupe Mountains, West Texas, USA Part I: Albaillellaria and Entactinaria. Micropaleontology 66, 1–50 (2020).
    Google Scholar 
    6.Xiao, Y. F., Suzuki, N. & He, W. H. Low-latitudinal standard radiolarian biostratigraphy for multiple purposes with Unitary Association, Graphic Correlation, and Bayesian inference methods. Earth Sci. Rev. 179, 168–206 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Kobayashi, F. Middle Permian biogeography based on fusulinacean faunas. pp. 73–76 in C. A. Ross, J. R. P. Ross & P. L. Brenckle (eds) Late Paleozoic Foraminifera; Their Biogeography, Evolution, and Paleoecology; and the Mid-Carboniferous Boundary. Cushman Foundation for Foraminiferal Research, Special Publication, 36 (1997).8.Ormiston, A. R. & Babcock, L. Follicucullus, new radiolarian genus from the Guadalupian (Permian) Lamar Limestone of the Delaware Basin. J. Paleontol. 53, 323–334 (1979).
    Google Scholar 
    9.Caridroit, M. et al. An illustrated catalogue and revised classification of Paleozoic radiolarian genera. Geodiversitas 39, 363–417 (2017).Article 

    Google Scholar 
    10.Noble, P. J. et al. Taxonomy of Paleozoic radiolarian genera. Geodiversitas 39, 419–502 (2017).Article 

    Google Scholar 
    11.Ito, T. Taxonomic re-evaluation of the Permian radiolarian genus Longtanella Sheng and Wang (Follicucullidae, Albaillellaria). Rev. Micropaléontol. 66, 100406 (2020).Article 

    Google Scholar 
    12.Xiao, Y. F. et al. Verifiability of genus-level classification under quantification and parsimony theories: a case study of follicucullid radiolarians. Paleobiology 46, 337–355 (2020).Article 

    Google Scholar 
    13.Zheng, Y. F., Xiao, W. J. & Zhao, G. C. Introduction to tectonics of China. Gondwana Res. 23, 1189–1206 (2013).ADS 
    Article 

    Google Scholar 
    14.Ke, X. et al. Radiolarian and detrital zircon in the Upper Carboniferous to Permian Bancheng Formation, Qinfang Basin, and the geological significance. J. Earth Sci. 29, 594–606 (2018).CAS 

    Google Scholar 
    15.Editorial Committee of Stratigraphical Lexicon of China. Stratigraphical Lexicon of China: Permian. 149 (Geological Publishing House, Beijing, 2000) (in Chinese).16.Bureau of Geology and Mineral Ressources of Guangxi Autonomous Region. Stratigraphy (Lithostratigraphy) of Guangxi Zhuang Autonomous Region. 310 (CUG Press, Wuhan, 1997) (in Chinese).17.Wang, Y. J., Luo, D., Kuang, G. D. & Li, J. X. Late Devonian-Late Permian strata of cherty facies at Xiaodong and Bancheng counties of the Qinzhou area, SE Guangxi. Acta Micropalaeontol. Sin. 15, 351–366 (1998) (in Chinese with English Abstract).CAS 

    Google Scholar 
    18.Zhang, N., Henderson, C. M., Xia, W. C., Wang, G. Q. & Shang, H. J. Conodonts and radiolarians through the Cisuralian-Guadalupian boundary from the Pingxiang and Dachongling sections, Guangxi region, South China. Alcheringa 34, 135–160 (2010).CAS 
    Article 

    Google Scholar 
    19.Ito, T., Zhang, L., Feng, Q. L. & Matsuoka, A. Guadalupian (Middle Permian) Radiolarian and sponge spicule faunas from the Bancheng Formation of the Qinzhou Allochthon, South China. J. Earth Sci. 24, 145–156 (2013).Article 

    Google Scholar 
    20.He, W. H. et al. Sedimentary and tectonic evolution of Nanhuan–Permian in South China. Earth Sci. J. China Univ. Geosci. 39, 929–953 (2014) (in Chinese with English abstract).
    Google Scholar 
    21.Li, Y. P., Chen, S. Y. & Peng, B. X. A Diwa surpassing the platform stage—Geotectnic evolutionary characteristics of Qinzhou District, Guangxi. J. Cent. South Univ. 25, 282–287 (1994) (in Chinese with English abstract).
    Google Scholar 
    22.Xu, D. M. et al. Research history and current situation of Qinzhou–Hangzhou metallogenic belt, South China. Geol. Miner. Resour. South China 28, 277–289 (2012) (in Chinese with English abstract).
    Google Scholar 
    23.Wang, Z. C., Wu, H. L. & Kuang, G. D. Geochemistry and origin of Late Paleozoic cherts in Guangxi and their explanation of tectonic environments. Acta Petrol. Sin. 11, 449–455 (1995) (in Chinese with English Abstract).
    Google Scholar 
    24.Hu, L. S. et al. Geochemical characteristics and its geological significance of the Late Paleozoic siliceous rocks in Qinfang Trough, southeastern Guangxi. J. Palaeogeogr. 16, 77–87 (2014) (in Chinese with English Abstract).CAS 

    Google Scholar 
    25.Silva, I. P. & Boersma, A. Atlantic Eocene planktonic foraminiferal historical biogeography and paleohydrographic indices. Palaeogeogr. Palaeoclimatol. Palaeoecol. 67, 315–356 (1988).Article 

    Google Scholar 
    26.Zhang, Y. C. & Wang, Y. 2018. Permian fusuline biostratigraphy. pp. 253–288 in S. G. Lucas & S. Z. Shen (eds) The Permian Timescale. Geological Society, London, Special Publications, 450.27.Mei, S. L. & Henderson, C. M. Evolution of Permian conodont provincialism and its significance in global correlation and paleoclimate implication. Palaeogeogr. Palaeoclimatol. Palaeoecol. 170, 237–260 (2001).Article 

    Google Scholar 
    28.Leonova, T. B. Permian ammonoids: Biostratigraphic, biogeographical, and ecological analysis. Paleontol. J. 45, 1206–1312 (2011).Article 

    Google Scholar 
    29.Romano, C. et al. Permian-Triassic Osteichthyes (bony fishes): Diversity dynamics and body size evolution. Biol. Rev. 91, 106–147 (2014).PubMed 
    Article 

    Google Scholar 
    30.Afanasieva, M. S., Amon, E. O. & Chuvashov, B. I. Radiolarians in Carboniferous stratigraphy and paleogeography in Eastern Europe (PreCaspian and Southern Cis-Urals). Lithosphere 4, 22–62 (2002) (in Russian with English abstract).
    Google Scholar 
    31.Murchey, B. L. Age and depositional setting of siliceous sediments in the upper Paleozoic Havallah sequence near Battle Mountain, Nevada: Implications for the paleogeography and structural evolution of the western margin of North America. Geol. Soc. Am. Spec. Pap. 225, 137–155 (1990).
    Google Scholar 
    32.Noble, P. J. & Jin, Y. X. Radiolarians from the Lamar Limestone, Guadalupe Mountains, West Texas. Micropaleontology 56, 117–147 (2010).
    Google Scholar 
    33.Kuwahara, K. & Yao, A. Diversity of late Permian radiolarian assemblages. News Osaka Micropaleontol. 11, 33–46 (1998) (in Japanese with English abstract).
    Google Scholar 
    34.Feng, Q. L. et al. Radiolarian evolution during the latest Permian in South China. Global Planet. Change 55, 177–192 (2007).ADS 
    Article 

    Google Scholar 
    35.Lucas, S. G. The Permian and Triassic Chronostratigraphic Scales—Framework for Ordering Events. In Permo-Triassic salt provinces of Europe, North Africa and the Atlantic Margins (eds Soto, J. I. et al.) 43–55 (Elsevier, 2017).
    Google Scholar 
    36.Kobayashi, F. Tethyan uppermost Permian (Dzhulfian and Dorashamian) foraminiferal faunas and their paleobiogeographic and tectonic implications. Palaeogeogr. Palaeoclimatol. Palaeoecol. 150, 279–307 (1999).Article 

    Google Scholar 
    37.Ueno, K. The Permian antitropical fusulinoidean genus Monodiexodina: Distribution, taxonomy, paleobiogeography and paleoecology. J. Asian Earth Sci. 26, 380–404 (2006).ADS 
    Article 

    Google Scholar 
    38.Niu, Z. J. & Wu, J. Fusulinid Fauna of Permian Volcanic–Depositional Succession (Setting) in Southern Qinghai, Norwest China. China University of Geoscience Publishing House, Wuhan, 199 (2016). (in Chinese with English summary)39.Zhou, J. P., Zhang, L. X., Wang, Y. J. & Yang, Q. Permian biogeographic provinces of fusulinids in China. J. Stratigr. 24, 378–393 (2000) (in Chinese with English abstract).
    Google Scholar 
    40.Sheng, J. Z., Zhang, L. X. & Wang, J. H. Fusulinids 240 (Science Press, 1988) (in Chinese).
    Google Scholar 
    41.Loeblich, A. R. & Tappan, H. Implications of wall composition and structure in agglutinated foraminifers. J. Paleontol. 63, 769–777 (1989).Article 

    Google Scholar 
    42.Sheng, J. Z. & Wang, Y. J. Permian fusulinids from Xizang with reference to their geographical provincialism. Acta Palaeontol. Sin. 20, 546–551 (1981) (in Chinese with English abstract).
    Google Scholar 
    43.Davydov, V. I. & Arefifard, S. Middle Permian (Guadalupian) fusulinid taxonomy and biostratigraphy of the mid-latitude Dalan Basin, Zargos, Iran and their applications in paleoclimate dynamics and paleogeography. GeoArabia 18, 17–62 (2013).
    Google Scholar 
    44.Ishii, K. Provinciality of some fusulinacean faunas of Japan. In Pre-Cretaceous Terranes of Japan (eds Ichikawa, K. et al.) 297–305 (Osaka City University, 1990).
    Google Scholar 
    45.Kobayashi, F. & Ujimaru, A. Chinese fusulinaceans kept in the museum of nature and human activities, Hyogo, Japan. Nat. Hum. Act. 5, 5–25 (2000).
    Google Scholar 
    46.Ishiga, H., Kito, T. & Imoto, N. Middle Permian radiolarian assemblages in the Tamba District and an adjacent area, southwest Japan. Earth Sci. (Chikyu Kagaku) 36, 272–281 (1982).
    Google Scholar 
    47.Wang, Y. J., Cheng, Y. N. & Yang, Q. Biostratigraphy and systematics of Permian radiolarians in China. Palaeoworld 4, 172–202 (1994).
    Google Scholar 
    48.Holdsworth, B. K. & Jones, D. L. Preliminary radiolarian zonation for late Devonian through Permian time. Geology 8, 281–285 (1980).ADS 
    Article 

    Google Scholar 
    49.Nishimura, K. & Ishiga, H. Radiolarian biostratigraphy of the Maizuru Group in Yanahara area, Southwest Japan. Mem. Fac. Sci. Shimane Univ. 21, 169–188 (1987).
    Google Scholar 
    50.Kojima, S. et al. Pre-cretaceous accretionary complex. In The Geology of Japan (eds Moreno, T. et al.) 61–100 (Geological Society, 2016).
    Google Scholar 
    51.Wallis, S. R. et al. The basement geology of Japan from A to Z. Island Arc 29, e12339 (2020).
    Google Scholar 
    52.Kametaka, M., Nakae, S. & Kamada, K. Early Permian radiolarians from siliceous mudstone in the Rikuchu–Seki District, North Kitakami Terrane. Bull. Geol. Surv. Jpn. 56, 237–243 (2005) (in Japanese with English abstract).Article 

    Google Scholar 
    53.Suzuki, N. et al. Geology of the Kuzumaki-Kamaishi Subbelt of the North Kitakami Belt (a Jurassic accretionary complex), Northeast Japan: Case study of the Kawai-Yamada area, eastern Iwate Prefecture. Bull. Tohoku Univ. Mus. 6, 103–174 (2007).
    Google Scholar 
    54.Ito, T., Kitagawa, Y. & Matsuoka, A. Middle and Late Permian radiolarians from chert blocks within conglomerates of the Kamiaso Unit of the Mino Terrane in Gifu Prefecture, central Japan. J. Geol. Soc. Jpn 122, 249–259 (2016) (in Japanese with English abstract).Article 

    Google Scholar 
    55.Niko, S., Yamakita, S., Otoh, S., Yanai, S. & Hamada, T. Permian radiolarians from the Mizuyagadani Formation in Fukuji area, Hida Marginal Belt and their significance. J. Geol. Soc. Jpn 93, 431–433 (1987) (in Japanese).Article 

    Google Scholar 
    56.Isozaki, Y. & Tamura, H. Late Carboniferous and Early Permian radiolarians from the Nagato Tectonic Zone and their implication to geologic structure of the Inner Zone, Southwest Japan. Mem. Geol. Soc. Jpn. 33, 167–176 (1989) (in Japanese with English abstract).
    Google Scholar 
    57.Ujiié, H. & Oba, T. Geology and Permo-Jurassic Radiolaria of the Iheya Zone, innermost belt of the Okinawa Islands region, middle Ryukyu island arc, Japan. Part 1: Geology and Permian radiolaria. Bull. Coll. Sci. Univ. Ryukyus 51, 35–55 (1991).
    Google Scholar 
    58.Hori, N. Permian radiolarians from chert of the Chichibu Belt in the Toyohashi district, Aichi Prefecture, Southwest Japan. Bull. Geol. Surv. Jpn 55, 287–301 (2004) (in Japanese with English abstract).Article 

    Google Scholar 
    59.Hada, S., Salo, E., Takeshima, H. & Kawakami, A. Age of the covering strata in the Kurosegawa Terrane: Dismembered continental fragment in southwest Japan. Palaeogeogr. Palaeoclimatol. Palaeoecol. 96, 59–69 (1992).Article 

    Google Scholar 
    60.Kashiwagi, K. & Isaji, S. Paleozoic and Mesozoic radiolarians from chert pebbles and cobbles of the Lower Cretaceous Choshi Group, Japan. Nat. Hist. Res. (Nat. Hist. Mus. Inst. Chiba) 13, 35–46 (2015).
    Google Scholar 
    61.Feng, Q. L. & Ye, M. Radiolarian stratigraphy of Devonian through Middle Triassic in Southwestern Yunnan. In Devonian to Triassic Tethys in Western Yunnan China (ed. Fang, N. Q.) 15–22 ( China University of Geosciences Press, 1996).
    Google Scholar 
    62.Yao, A. & Kuwahara, K. Paleozoic and Mesozoic radiolarians from the Changning-Menglian Terrane, Western Yunnan, China. In Biotic and Geological Development of the Paleo-Tethys in China (eds Yao, A. et al.) 17–42 (Peking University Press, 1999).
    Google Scholar 
    63.Toriyama, R. The fusulinacean zones of Japan. Mem. Fac. Sci. Kyushu Univ. Ser. D Geol. 18, 35–260 (1967).
    Google Scholar 
    64.Morikawa, R. & Isomi, H. A new genus Biwaella, Schwagerina-like Schubertella. Sci. Rep. Saitama Univ. Ser. B 3, 301–305 (1960).
    Google Scholar 
    65.Toriyama, R. Summary of the fusuline faunas in Thailand and Malaysia. In Geology and Palaeontology of Southeast Asia Vol. 25 (eds Kobayashi, T. et al.) 137–146 (University of Tokyo Press, 1984).
    Google Scholar 
    66.Wang, S. Y., Wang, H. M. & Zhang, H. The Longlinian (Early Permian) fusulinid communities and sedimentary environments in the Liuzhi-Panxian region, Guizhou. Sediment. Geol. Tethyan Geol. 25, 37–41 (2005) (in Chinese with English abstract).
    Google Scholar 
    67.Xiao, C. T., Gong, K. & Liang, W. J. Research on paleoecology of middle Permian–middle Triassic in the western Sichuan Basin. Adv. Earth Sci. 29, 819–827 (2014) (in Chinese with English abstract).
    Google Scholar 
    68.Geng, Q. R., Peng, Z. M. & Zhang, Z. New advances in the study of Carboniferous-Permian paleontology in Guoganjianianshan-Rongma area of Qiangtang region, Tibetan Plateau. Geol. Bull. China 31, 510–520 (2012) (in Chinese with English abstract).
    Google Scholar 
    69.Jasin, B. Significance of Monodiexodina (Fusulininacea) in geology of Peninsular Malaysia. Bull. Geol. Soc. Malays. 29, 171–181 (1991).Article 

    Google Scholar 
    70.Hassan, M. H. A., Al Zamruddin, N. N. S., Sim, Y. B. & Samad, A. S. S. A. Sedimentology of the Permian Monodiexodina-bearing bed of the uppermost Kubang Pasu Formation, northwest Peninsular Malaysia: Interpretation as storm-generated, transgressive lag deposits. Bull. Geol. Soc. Malays. 64, 51–58 (2017).Article 

    Google Scholar 
    71.Ueno, K. A peculiar fusulinacean fauna from the Yasuba Conglomerate, Kochi Prefecture, Shikoku. Trans. Proc. Palaeontol. Soc. Jpn. New Ser. 164, 1004–1008 (1991).
    Google Scholar 
    72.Yamashita, N. Yabeina-Lepidolina fauna, found in the Sakawa Basin, Shikoku, and its significance. J. Geol. Soc. Jpn 64, 92–94 (1958) (in Japanese).Article 

    Google Scholar 
    73.Ding, P. Z., Jin, T. A. & Sun, X. F. The marine Permian strata and its faunal assemblages in Xikou area of Zhen’an County, south Shaanxi, east Qinling Range. Bull. Xi’an Inst. Geol. Miner. Resour. Chin. Acad. Geol. Sci. 25, 1–65 (1989) (in Chinese).
    Google Scholar 
    74.Danner, W. R., Nestell, M. K. & Nestell, G. P. Geology and paleontology of the Carboniferous and Permian of the exotic terranes of southwestern British Columbia. pp. 1–124 in The Committee for the XIV International Congress on the Carboniferous–Permian (ed.) Precongress Field Trip No. 9. XIV International Congress on the Carboniferous–Permian, August 12–16, 1999. Alberta (1999).75.Win, Z. Fusuline biostratigraphy and paleontology of the Akasaka Limestone, Gifu Prefecture, Japan. Bull. Kitakyushu Mus. Nat. Hist. 18, 1–76 (1999).
    Google Scholar 
    76.Van der Meer, D. G., Torsvik, T. H., Spakman, W., Van Hinsbergen, D. J. J. & Amaru, M. L. Intra-Panthalassa Ocean subduction zones revealed by fossil arcs and mantle structure. Nat. Geosci. 5, 215–219 (2012).ADS 
    Article 
    CAS 

    Google Scholar 
    77.Ross, C. A. Development of fusulinid (Foraminiferida) faunal realms. J. Paleontol. 41, 1341–1354 (1967).
    Google Scholar 
    78.Davydov, V. I., Belasky, P. & Karavayeva, N. I. Permian fusulinids from the Koryak Terrane, northeastern Russia, and their paleobiogeographic affinity. J. Foramin. Res. 26, 213–243 (1996).Article 

    Google Scholar 
    79.Kobayashi, F., Ross, C. A. & Ross, J. R. Age and generic assignment of Yabeina columbiana (Guadalupian Fusulinacea) in southern British Columbia. J. Paleontol. 81, 238–253 (2007).Article 

    Google Scholar 
    80.Roscher, M., Stordal, F. & Svensen, H. The effect of global warming and global cooling on the distribution of the latest Permian climate zones. Palaeogeogr. Palaeoclimatol. Palaeoecol. 309, 186–200 (2011).Article 

    Google Scholar 
    81.Campi, M. J. The Permian—A time of major evolutions and revolutions in the history of life. In Earth and Life (ed. Talent, J. A.) 705–718 (Springer, 2012).
    Google Scholar 
    82.Tomczak, M. & Godfrey, J.S. Regional Oceanography: An Introduction. (2005) https://www.mt-oceanography.info/regoc/pdfversion.html . 24-Oct-2020.83.Talley, L. D., Pickard, G. L., Emery, W. J. & Swift, J. H. Descriptive Physical Oceanography: An Introduction 6th edn, 560 (Elsevier, 2011).
    Google Scholar 
    84.Shi, G. R. & Archbold, N. W. Permian marine biogeography of SE Asia. In Biogeography and Geological Evolution of SE Asia (eds Hall, R. & Holloway, J. D.) 57–72 (Backhuys Publishers, 1998).
    Google Scholar 
    85.Noble, P. J. et al. Paleohydrographic influences on Permian radiolarians in the Lamar Limestone, Guadalupe Mountains, West Texas, elucidated by organic biomarker and stable isotope geochemistry. Palaios 26, 180–186 (2011).ADS 
    Article 

    Google Scholar 
    86.Siedler, G., Griffies, S. M., Gould, J. & Church, J. A. Ocean Circulation and Climate—A 21st Century Perspective 2nd edn, 868 (Elsevier, 2013).
    Google Scholar 
    87.Suzuki, N. & Not, F. Biology and ecology of radiolaria. In Marine Protists: Diversity and Dynamics (eds Ohtsuka, S. et al.) 179–222 (Springer, 2015).
    Google Scholar 
    88.Xiao, Y. F., Suzuki, N. & He, W. H. Water depths of the latest Permian (Changhsingian) radiolarians estimated from correspondence analysis. Earth Sci. Rev. 173, 141–158 (2017).ADS 
    Article 

    Google Scholar 
    89.Haig, D. W. et al. Late Artinskian-Early Kungurian (Early Permian) warming and maximum marine flooding in the East Gondwana interior rift, Timor and Western Australia, and comparisons across East Gondwana. Palaeogeogr. Palaeoclimatol. Palaeoecol. 468, 88–121 (2017).Article 

    Google Scholar 
    90.Zhang, L., Feng, Q. L. & He, W. H. Permian radiolarian biostratigraphy. In The Permian Timescale (eds Lucas, S. G. & Shen, S. Z.) 143–163 (Geological Society, 2018).
    Google Scholar 
    91.Catalano, R., Di Stefano, P. & Kozur, H. Lower Permian Albaillellacea (Radiolaria) from Sicily and their stratigraphic and paleogeographic significance. Rend. dell’Accad. delle Sci. fsiche Mat. Ser. IV 56, 1–24 (1989).
    Google Scholar 
    92.Moix, P. et al. Geology and correlation of the Mersin Mélanges, Southern Turkey. Turk. J. Earth Sci. 20, 57–98 (2011).
    Google Scholar 
    93.Spiller, F. C. P. Radiolarian biostratigraphy of Peninsular Malaysia and implications for regional palaeotectonics and palaeogeography. Palaeontogr. Abteilung A Palaeozool. Stratigr. 266, 1–91 (2002).
    Google Scholar 
    94.Metcalfe, I., Spiller, F. C. P., Liu, B. P., Wu, H. R. & Sashida, K. The Palaeo-Tethys in Mainland East and Southeast Asia: contributions from radiolarian studies, in: I. Metcalfe (ed.) Gondwana Dispersion and Asian Accretion. IGCP321 Final Results Volume 259–281 (A.A. Balkema, Rotterdam, 1999).95.Rudenko, V.S. & Panasenko, E.S. Biostratigraphy of Permian deposits of Sikhote-Alin based on radiolarians. in A. Baud, I. Popova, J. M. Dickins, S. Lucas, Y. Zakharov (eds) Late Paleozoic and Early Mesozoic Circum-Paciric Events: Biostratigraphy, Tectonic and Ore Deposits of Primoryie (Far East Russia). IGCP Project 272. Mémoires de Géologie (Lausanne), 30, 73–84 (1997).96.Takemura, A. et al. Preliminary report on the lithostratigraphy of the Arrow Rocks, and geologic age of the northern part of the Waipapa Terrane, New Zealand. News Osaka Micropaleontol. Spec. 11, 47–57 (1998).
    Google Scholar 
    97.Cordey, F. Radiolaires des complexes d’accrétion de la Cordillère Canadienne(Colombie-Britannique). Geol. Surv. Can. Bull. 509, 209 (1998) (in French with English summary).
    Google Scholar 
    98.Nestell, G. P. & Nestell, M. K. Late Capitanian (latest Guadalupian, Middle Permian) radiolarians from the Apache Mountains, West Texas. Micropaleontology 56, 7–68 (2010).
    Google Scholar 
    99.Metcalfe, I. Gondwana dispersion and Asian accretion: Tectonic and palaeogeographic evolution of eastern Tethys. J. Asian Earth Sci. 66, 1–33 (2013).ADS 
    Article 

    Google Scholar  More

  • in

    Fostering a climate-smart intensification for oil palm

    1.Pirker, J., Mosnier, A., Kraxner, F., Havlík, P. & Obersteiner, M. What are the limits to oil palm expansion? Glob. Environ. Change 40, 73–81 (2016).Article 

    Google Scholar 
    2.Bonan, G. B. Forests and climate change: forcings, feedbacks, and the climate benefits of forests. Science 320, 1444–1449 (2008).CAS 
    Article 

    Google Scholar 
    3.Colchester, M. et al. Justice in the Forest (CIFOR, 2006).4.Johnson, C. N. et al. Biodiversity losses and conservation responses in the Anthropocene. Science 356, 270–275 (2017).CAS 
    Article 

    Google Scholar 
    5.Seymour, F. & Harris, N. L. Reducing tropical deforestation. Science 365, 756–757 (2019).CAS 
    Article 

    Google Scholar 
    6.First Nationally Determined Contribution Submitted to UNFCCC (Republic of Indonesia, 2016).7.FAOSTAT (FAO, accessed 1 March 2020); http://www.fao.org/faostat/en/#data8.Austin, K. G., Schwantes, A., Gu, Y. & Kasibhatla, P. S. What causes deforestation in Indonesia? Environ. Res. Lett. 14, 024007 (2019).9.Tree Crop Estate Statistics of Indonesia 2017–2019 (Directorate General of Estate Crops, 2019).10.Woittiez, L. S., van Wijk, M. T., Slingerland, M., van Noordwijk, M. & Giller, K. E. Yield gaps in oil palm: a quantitative review of contributing factors. Eur. J. Agron. 83, 57–77 (2017).Article 

    Google Scholar 
    11.Wilcove, D. S., Giam, X., Edwards, D. P., Fisher, B. & Koh, L. P. Navjot’s nightmare revisited: logging, agriculture, and biodiversity in Southeast Asia. Trends Ecol. Evol. 28, 531–540 (2013).Article 

    Google Scholar 
    12.Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).CAS 
    Article 

    Google Scholar 
    13.Gaveau, D. L. A. et al. Rapid conversions and avoided deforestation: Examining four decades of industrial plantation expansion in Borneo. Sci. Rep. 6, 32017 (2016).CAS 
    Article 

    Google Scholar 
    14.Srinivas, A. & Koh, L. P. Oil palm expansion drives avifaunal decline in the Pucallpa region of Peruvian Amazonia. Glob. Ecol. Conserv. 7, 183–200 (2016).Article 

    Google Scholar 
    15.Byerlee, D., Stevenson, J. & Villoria, N. Does intensification slow crop land expansion or encourage deforestation? Glob. Food Sec. 3, 92–98 (2014).Article 

    Google Scholar 
    16.Cassman, K. G. Ecological intensification of cereal production systems: Yield potential, soil quality, and precision agriculture. Proc. Natl Acad. Sci. USA 96, 5952–5959 (1999).CAS 
    Article 

    Google Scholar 
    17.Cassman, K. G. & Grassini, P. A global perspective on sustainable intensification research. Nat. Sustain. 3, 262–268 (2020).Article 

    Google Scholar 
    18.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418, 671–677 (2002).CAS 
    Article 

    Google Scholar 
    19.Statistics Indonesia (BPS, accessed 1 March 2020); https://www.bps.go.id20.Jelsma, I., Schoneveld, G. C., Zoomers, A. & van Westen, A. C. M. Unpacking Indonesia’s independent oil palm smallholders: an actor-disaggregated approach to identifying environmental and social performance challenges. Land Use Policy 69, 281–297 (2017).Article 

    Google Scholar 
    21.Roadmap for the National Oil Palm Industry Towards 2045 (Indonesian cross-ministry team and oil palm institutions and local associations, 2019).22.The Palm Oil Dilemma: Policy Tensions Among Higher Productivity, Rising Demand, and Deforestation (IFPRI, 2019).23.OECD-FAO Agricultural Outlook 2020–2029 (OECD, 2020).24.Lobell, D. B., Cassman, K. G. & Field, C. B. Crop yield gaps: their importance, magnitudes, and causes. Annu. Rev. Environ. Resour. 34, 179–204 (2009).Article 

    Google Scholar 
    25.Hoffmann, M. P. et al. Yield gap analysis in oil palm: framework development and application in commercial operations in Southeast Asia. Agric. Syst. 151, 12–19 (2017).Article 

    Google Scholar 
    26.Molenaar, J. W., Persch-Orth, M., Taylor, C. & Harms, J. Diagnostic Study on Indonesia Oil Palm Smallholders: Developing a Better Understanding of their Performance and Potential (IFC, 2013).27.The Future of Food and Agriculture: Trends and Challenges (FAO, 2017).28.Hoffmann, M. P. et al. Simulating potential growth and yield of oil palm (Elaeis guineensis) with PALMSIM: model description, evaluation and application. Agric. Syst. 131, 1–10 (2014).Article 

    Google Scholar 
    29.Euler, M., Hoffmann, M. P., Fathoni, Z. & Schwarze, S. Exploring yield gaps in smallholder oil palm production systems in eastern Sumatra, Indonesia. Agric. Syst. 146, 111–119 (2016).Article 

    Google Scholar 
    30.Soliman, T., Lim, F. K. S. S., Lee, J. S. H. H. & Carrasco, L. R. Closing oil palm yield gaps among Indonesian smallholders through industry schemes, pruning, weeding and improved seeds. R. Soc. Open Sci. 3, 160292 (2016).CAS 
    Article 

    Google Scholar 
    31.Grassini, P. et al. How good is good enough? Data requirements for reliable crop yield simulations and yield-gap analysis. Field Crop. Res. 177, 49–63 (2015).Article 

    Google Scholar 
    32.Tilman, D. et al. Future threats to biodiversity and pathways to their prevention. Nature 546, 73–81 (2017).CAS 
    Article 

    Google Scholar 
    33.Mitchard, E. T. A. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).CAS 
    Article 

    Google Scholar 
    34.Zabel, F. et al. Global impacts of future cropland expansion and intensification on agricultural markets and biodiversity. Nat. Commun. 10, 2844 (2019).Article 
    CAS 

    Google Scholar 
    35.Barlow, J. et al. The future of hyperdiverse tropical ecosystems. Nature 559, 517–526 (2018).CAS 
    Article 

    Google Scholar 
    36.Indonesia. Second Biennial Update Report. Under the United Nations Framework Convention on Climate Change (Directorate General of Climate Change, Ministry of Environment and Forestry, 2018).37.Rhebergen, T. et al. Closing yield gaps in oil palm production systems in Ghana through best management practices. Eur. J. Agron. 115, 126011 (2020).Article 

    Google Scholar 
    38.Woittiez, L. S., Slingerland, M., Rafik, R. & Giller, K. E. Nutritional imbalance in smallholder oil palm plantations in Indonesia. Nutr. Cycl. Agroecosyst. 111, 73–86 (2018).Article 

    Google Scholar 
    39.Corley, R. H. V. & Lee, C. H. The physiological basis for genetic improvement of oil palm in Malaysia. Euphytica 60, 179–184 (1992).
    Google Scholar 
    40.Jelsma, I., Woittiez, L. S., Ollivier, J. & Dharmawan, A. H. Do wealthy farmers implement better agricultural practices? An assessment of implementation of Good Agricultural Practices among different types of independent oil palm smallholders in Riau, Indonesia. Agric. Syst. 170, 63–76 (2019).Article 

    Google Scholar 
    41.Deininger, K. Challenges posed by the new wave of farmland investment. J. Peasant Stud. 38, 217–247 (2011).Article 

    Google Scholar 
    42.Agricultural Innovation Systems: An Investment Sourcebook (The World Bank, 2012).43.Cock, J. et al. Learning from commercial crop performance: oil palm yield response to management under well-defined growing conditions. Agric. Syst. 149, 99–111 (2016).Article 

    Google Scholar 
    44.Jelsma, I., Slingerland, M., Giller, K. E. & Bijman, J. Collective action in a smallholder oil palm production system in Indonesia: the key to sustainable and inclusive smallholder palm oil? J. Rural Stud. 54, 198–210 (2017).Article 

    Google Scholar 
    45.Carlson, K. M. et al. Effect of oil palm sustainability certification on deforestation and fire in Indonesia. Proc. Natl Acad. Sci. USA 115, 121–126 (2018).CAS 
    Article 

    Google Scholar 
    46.Sahide, M. A. K. & Giessen, L. The fragmented land use administration in Indonesia: analysing bureaucratic responsibilities influencing tropical rainforest transformation systems. Land Use Policy 43, 96–110 (2015).Article 

    Google Scholar 
    47.Presidential Instruction no. 5 (President of the Replublic of Indonesia, 2019).48.REDD+ (UNFCCC, accessed 1 March 2020); https://redd.unfccc.int49.Evans, L. T. Crop Evolution, Adaptation and Yield (Cambridge Univ. Press, 1993).50.van Ittersum, M. K. et al. Yield gap analysis with local to global relevance—A review. F. Crop. Res. 143, 4–17 (2013).Article 

    Google Scholar 
    51.Fairhurst, T. H. & Griffiths, W. Oil Palm: Best Management Practices for Yield Intensification (International Plant Nutrition Institute, Southeast Asia Program, 2015).52.Global Yield Gap Atlas (University of Nebraska, Wageningen University, accessed 1 March 2020); https://www.yieldgap.org53.Hekman, W., Slingerland, M. A., van den Beuken, R., Gerrie, V. & Grassini, P. Estimating yield gaps in oil palm in Indonesia using PALMSIM to inform policy on the scope of intensification. In International Oil Palm Conference (IOPC) (2018).54.Austin, K. G. et al. Shifting patterns of oil palm driven deforestation in Indonesia and implications for zero-deforestation commitments. Land Use Policy 69, 41–48 (2017).Article 

    Google Scholar 
    55.Land Cover Data (Ministry of Environment and Forestry, Indonesia, accessed 1 March 2020).56.Searchinger, T. et al. Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change. Science 319, 1238–1240 (2008).CAS 
    Article 

    Google Scholar 
    57.National Forest References Emission Level for Deforestations and Forest Degradation (Ministry of Environment and Forestry, Indonesia, 2016).58.Khasanah, N., van Noordwijk, M., Ningsih, H. & Wich, S. Aboveground carbon stocks in oil palm plantations and the threshold for carbon-neutral vegetation conversion on mineral soils. Cogent Environ. Sci. 1, 1119964 (2015).Article 
    CAS 

    Google Scholar 
    59.Khasanah, N., van Noordwijk, M., Ningsih, H. & Rahayu, S. Carbon neutral? No change in mineral soil carbon stock under oil palm plantations derived from forest or non-forest in Indonesia. Agric. Ecosyst. Environ. 211, 195–206 (2015).Article 

    Google Scholar 
    60.van Straaten, O. et al. Conversion of lowland tropical forests to tree cash crop plantations loses up to one-half of stored soil organic carbon. Proc. Natl Acad. Sci. USA 112, 9956–9960 (2015).Article 
    CAS 

    Google Scholar 
    61.Quezada, J. C., Etter, A., Ghazoul, J., Buttler, A. & Guillaume, T. Carbon neutral expansion of oil palm plantations in the Neotropics. Sci. Adv. 5, eaaw4418 (2019).CAS 
    Article 

    Google Scholar 
    62.Harsono, S. S., Prochnow, A., Grundmann, P., Hansen, A. & Hallmann, C. Energy balances and greenhouse gas emissions of palm oil biodiesel in Indonesia. GCB Bioenergy 4, 213–228 (2012).CAS 
    Article 

    Google Scholar 
    63.Archer, S. A., Murphy, R. J. & Steinberger-Wilckens, R. Methodological analysis of palm oil biodiesel life cycle studies. Renew. Sustain. Energy Rev. 94, 694–704 (2018).Article 

    Google Scholar 
    64.Brentrup, F., Lammel, J., Stephani, T. & Christensen, B. Updated carbon footprint values for mineral fertilizer from different world regions. In 11th International Conference on Life Cycle Assessment of Food 2018 (LCA Food) (2018).65.Lim, Y. L. et al. An update on oil palm nutrient budgets. In International Oil Palm Conference (IOPC) (2018).66.Tiemann, T. T. et al. Feeding the palm: a review of oil palm nutrition. Adv. Agron. 152, 149–243 (2018).Article 

    Google Scholar 
    67.Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (eds Calvo Buendia, E. et al.) (IPCC, 2019).68.Caliman, J. P. N-Fertiliser losses quantification in term of N2O emission and NH3 volatilisation. In Oil Palm Best Practices Workshop (MOSTA, 2019).69.Meijide, A. et al. Measured greenhouse gas budgets challenge emission savings from palm-oil biodiesel. Nat. Commun. 11, 1089 (2020).CAS 
    Article 

    Google Scholar 
    70.Hassler, E., Corre, M. D., Kurniawan, S. & Veldkamp, E. Soil nitrogen oxide fluxes from lowland forests converted to smallholder rubber and oil palm plantations in Sumatra, Indonesia. Biogeosciences 14, 2781–2798 (2017).CAS 
    Article 

    Google Scholar  More

  • in

    Anthropogenic nutrient loads and season variability drive high atmospheric N2O fluxes in a fragmented mangrove system

    1.Kroeze, C., Dumont, E. & Seitzinger, S. P. New estimates of global emissions of N2O from rivers and estuaries. Environ. Sci. 2(2–3), 159–165. https://doi.org/10.1080/15693430500384671 (2005).Article 

    Google Scholar 
    2.Ciais, P. et al. Carbon and other biogeochemical cycles. In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 465–570 (Cambridge University Press, 2014).3.Forster, P. et al. Changes in atmospheric constituents and in radiative forcing. Chapter 2. In Climate Change 2007. The Physical Science Basis (2007).4.Butterbach-Bahl, K., Baggs, E. M., Dannenmann, M., Kiese, R. & Zechmeister-Boltenstern, S. Nitrous oxide emissions from soils: How well do we understand the processes and their controls?. Philos. Trans. R. Soc. B. https://doi.org/10.1098/rstb.2013.0122 (2013).Article 

    Google Scholar 
    5.Reis, C. R. G., Nardoto, G. B. & Oliveira, R. S. Global overview on nitrogen dynamics in mangroves and consequences of increasing nitrogen availability for these systems. Plant Soil. 410(1–2), 1–19. https://doi.org/10.1007/s11104-016-3123-7#citeas (2017).CAS 
    Article 

    Google Scholar 
    6.Rao, K., Priya, N. & Ramanathan, A. L. Impacts of anthropogenic perturbations on reactive nitrogen dynamics in mangrove ecosystem: Climate change perspective. J. Clim. Change 5(2), 9–21 (2019).Article 

    Google Scholar 
    7.Centre for Coastal Zone Management and Coastal Shelter Belt, Ministry of Environment, Forests and Climate change, Govt. of India http://iomenvis.nic.in/index2.aspx?slid=758&sublinkid=119&langid=1&mid=1 (2017).8.FSI. India State of Forest Report. 2019. Forest Survey of India, Ministry of Environment and Forests, Dehradun (2019).9.Borges, A. V. et al. Effects of agricultural land use on fluvial carbon dioxide, methane and nitrous oxide concentrations in a large European river, the Meuse (Belgium). Sci. Total Environ. 610, 342–355. https://doi.org/10.1016/j.scitotenv.2017.08.047 (2018).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    10.Lin, H. et al. Spatiotemporal variability of nitrous oxide in a large eutrophic estuarine system: The Pearl River Estuary, China. Mar. Chem. 182, 14–24. https://doi.org/10.1016/j.marchem.2016.03.005 (2016).CAS 
    Article 

    Google Scholar 
    11.Reading, M. J. et al. Land use drives nitrous oxide dynamics in estuaries on regional and global scales. Limnol. 65(8), 1903–1920. https://doi.org/10.1002/lno.11426 (2020).CAS 
    Article 

    Google Scholar 
    12.Chauhan, R., Ramanathan, A. L. & Adhya, T. K. Assessment of methane and nitrous oxide flux from mangrove along Eastern coast of India. Geofluids 8, 321332. https://doi.org/10.1111/j.1468-8123.2008.00227.x (2008).CAS 
    Article 

    Google Scholar 
    13.Krithika, K., Purvaja, R. & Ramesh, R. Fluxes of methane and nitrous oxide from an Indian mangrove. Curr. Sci. 94, 218224, https://www.jstor.org/stable/24101861 (2008).14.Fernandes, S. O., LokaBharathi, P. A., Bonin, P. C. & Michotey, V. D. Denitrification: An important pathway for nitrous oxide production in tropical mangrove sediments (Goa, India). J. Environ. Qual. 39, 1507–1516. https://doi.org/10.2134/jeq2009.0477 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean. J. Geophys Res. 97, 7373–7382. https://doi.org/10.4319/lom.2014.12.351 (1992).ADS 
    Article 

    Google Scholar 
    16.Wanninkhof, R. & McGillis, W. M. A cubic relationship between gas transfer and wind speed. Geophys. Res. Lett. 26, 1889–1893. https://doi.org/10.1029/1999GL900363 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Raymond, P. A. & Cole, J. J. Gas exchange in rivers and estuaries: Choosing a gas transfer velocity. Estuaries 24, 312–317. https://doi.org/10.2307/1352954 (2001).CAS 
    Article 

    Google Scholar 
    18.Hershey, R. N., Nandan, S. B. & Vasu, N. K. Trophic status and nutrient regime of Cochin estuarine system, India. Indian J. Mar. Sci. 49(08), 2582–6727 http://nopr.niscair.res.in/handle/123456789/55309 (2020).19.Hershey, R. N. et al. Nitrous oxide flux from a Tropical estuarine system (Cochin estuary, India). Reg. Stud. Mar. Sci. 30, 100725. https://doi.org/10.1016/j.rsma.2019.100725 (2019).Article 

    Google Scholar 
    20.Maher, D. T., Sippo, J. Z., Tait, D. R., Holloway, C. & Santos, I. R. Pristine mangrove creek waters are a sink of nitrous oxide. Sci. Rep. 6, 25701. https://doi.org/10.1038/srep25701 (2016).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    21.Tait, D. R. et al. Greenhouse gas dynamics in a salt-wedge estuary revealed by high resolution cavity ring-down spectroscopy observations. Environ. Sci. Technol. 51(23), 13771–13778. https://doi.org/10.1021/acs.est.7b04627 (2017).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    22.Wells, N. S. et al. Estuaries as sources and sinks of N2O across a land use gradient in subtropical Australia. Glob. Biogeochem. Cycles. 32, 877–894. https://doi.org/10.1029/2017GB005826 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    23.Upstill-Goddard, R. C. Air–sea gas exchange in the coastal zone. Estuar Coast Shelf Sci. 70, 388–404. https://doi.org/10.1016/j.ecss.2006.05.043 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Zappa, C. J., Raymond, P. A., Terray, E. A. & Mcgillis, W. R. Variation in surface turbulence and gas transfer velocity over a tidal cycle in a macro-tidal estuary. Estuaries 26, 1401–1415. https://doi.org/10.1007/BF02803649/citeas (2003).CAS 
    Article 

    Google Scholar 
    25.Borges, A. V. et al. Gas transfer velocities of CO2 in three European estuaries (Randers Fjord, Scheldt, and Thames). Limnol. Oceanogr. 49, 1630–1641. https://doi.org/10.4319/lo.2004.49.5.1630 (2004).ADS 
    CAS 
    Article 

    Google Scholar 
    26.Munoz-Hincapie, M., Morell, J. M. & Corredor, J. E. Increase of nitrous oxide flux to the atmosphere upon nitrogen addition to red mangroves sediments. Mar. Pollut. Bull. 44, 992–996. https://doi.org/10.1016/S0025-326X(02)00132-7 (2002).CAS 
    Article 
    PubMed 

    Google Scholar 
    27.Srinivas, K., Revichandran, P., Maheswaran, P. A., Mohammed Ashraf, T. T. & Nuncio, M. Propagation of tides in the Cochin estuarine system, southwest coast of India. Indian J. Geomar. Sci. 32(1), 14–24 (2003).
    Google Scholar 
    28.Srinivas, K., Revichandran, C. & Dinesh Kumar, P. K. Statistical forecasting of met-ocean parameters in the Cochin estuarine system, southwest coast of India. Indian J. Geomar. Sci. 32(4), 285–293 (2003).
    Google Scholar 
    29.Balachandran, K. K., Joseph, T., Nair, K. K. C., Nair, M. & Joseph, P. S. The complex estuarine formation of six rivers (Cochin backwaters system on westcoast of India)—Sources and distribution of trace metals and nutrients. In:APN/SASCOM/LOICZ Regional Workshop on Assessment of Material Fluxes To the Coastal Zone in South Asia and their Impacts. Sri Lanka National Committee of IGBP, Colombo, Sri Lanka, 359, http://drs.nio.org/drs/handle/2264/1340 (2002).30.Martin, G. D. et al. Freshwater influence on nutrient stoichiometry in a tropical estuary, southwest coast of India. Appl. Ecol. Environ. Res. 6, 57–64 (2008).Article 

    Google Scholar 
    31.Liu, D. et al. N2O fluxes and rates of nitrification and denitrification at the sediment-water interface in Taihu Lake, China. Water 10, 911. https://doi.org/10.3390/w10070911 (2018).CAS 
    Article 

    Google Scholar 
    32.Luijn, F. V., Boers, P. C. M. & Lijklema, L. Comparison of denitrification rates in lake sediments obtained by the N2 flux method, the 15N isotope pairing technique and the mass balance approach. Water Res. 30, 893–900. https://doi.org/10.1016/0043-1354(95)00250-2 (1996).Article 

    Google Scholar 
    33.Pfenning, K. S. & McMahon, P. B. Effect of nitrate, organic carbon, and temperature on potential denitrification rates in nitrate-rich riverbed sediments. J. Hydrol. 187, 283–295. https://doi.org/10.1016/S0022-1694(96)03052-1 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Borges, A. V. et al. Globally significant greenhouse-gas emissions from African inland waters. Nat. Geosci. 8(8), 637–642. https://doi.org/10.1038/ngeo2486 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Marzadri, A., Dee, M. M., Tonina, D., Bellin, A. & Tank, J. L. Role of surface and subsurface processes in scaling N2O emissions along riverine networks. Proc. Natl. Acad. Sci. U. S. A. 114(17), 4330–4335. https://doi.org/10.1073/pnas.1617454114 (2017).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    36.Soued, C., del Giorgio, P. A. & Maranger, R. Nitrous oxide sinks and emissions in boreal aquatic networks in Quebec. Nat. Geosci. 9(2), 116–120, https://www.x-mol.com/paperRedirect/68353 (2016).37.Hu, M. P., Chen, D. J. & Dahlgren, R. A. Modeling nitrous oxide emission from rivers: A global assessment. Glob. Change Biol. 22(11), 3566–3582. https://doi.org/10.1111/gcb.13351 (2016).ADS 
    Article 

    Google Scholar 
    38.Murray, R., Erler, D. V., Rosentreter, J., Wells, N. S. & Eyre, B. D. Seasonal and spatial controls on N2O concentrations and emissions in low-nitrogen estuaries: Evidence from three tropical systems. Mar. Chem. https://doi.org/10.1016/j.marchem.2020.103779 (2020).Article 

    Google Scholar 
    39.Ji, Q. X., Babbin, A. R., Peng, X. F., Bowen, J. L. & Ward, B. B. Nitrogen substrate dependent nitrous oxide cycling in salt marsh sediments. J. Mar. Res. 73(3–4), 71–92. https://doi.org/10.1016/j.marchem.2020.103779 (2015).CAS 
    Article 

    Google Scholar 
    40.Punshon, S. & Moore, R. M. Nitrous oxide production and consumption in a eutrophic coastal embayment. Mar. Chem. 91(1–4), 37–51. https://doi.org/10.1016/j.marchem.2004.04.003 (2004).CAS 
    Article 

    Google Scholar 
    41.Corredor, J. E., Morell, J. M. & Bauza, J. Atmospheric nitrous oxide fluxes from mangrove sediments. Mar. Pollut. Bull. 38, 473–478. https://doi.org/10.1016/S0025-326X(98)00172-6 (1999).CAS 
    Article 

    Google Scholar 
    42.Raymond, P. A. et al. Scaling the gas transfer velocity and hydraulic geometry in streams and small rivers. Limnol. Oceanogr. Fluids Environ. 2, 41–53. https://doi.org/10.1215/21573689-1597669 (2012).Article 

    Google Scholar 
    43.Alongi, D. M. Impact of global change on nutrient dynamics in mangrove forests. Forests. 9(10), 596. https://doi.org/10.3390/f9100596 (2018).Article 

    Google Scholar 
    44.Reef, R., Feller, I. C. & Lovelock, C. E. Nutrition of mangroves. Tree Physiol. 30, 1148–1160. https://doi.org/10.1093/treephys/tpq048 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    45.Muller, D. et al. Nitrous oxide and methane in two tropical estuaries in a peat-dominated region of northwestern Borneo. Biogeosciences 13(8), 2415–2428. https://doi.org/10.5194/bg-13-2415-2016 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    46.Hasegawa, T. & Okino, T. Seasonal variation of denitrification rate in Lake Suwa sediment. Limnology 5(1), 33–39. https://doi.org/10.1007/PL00021725/citeas (2004).CAS 
    Article 

    Google Scholar 
    47.Myrstener, M., Jonsson, A. & Bergström, A. K. The effects of temperature and resource availability on denitrification and relative N2O production in boreal lake sediments. J. Environ. Sci. (China).48.Strickland, J. D. H. & Parsons, T. R. A Practical Handbook of Seawater Analysis. 2nd edn. 310 (Fisheries Research Board of Canada, 1972).49.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of seawater analysis. 2nd edn. 419 (Verlag Chemie, 1983).50.Garcia, H. & Gordon, L. Oxygen solubility in seawater: Better fitting equations. Limnol. Oceanogr. 37, 1307–1312. https://doi.org/10.4319/lo.1992.37.6.1307 (1992).ADS 
    CAS 
    Article 

    Google Scholar 
    51.Grasshoff, K., Ehrhardt, M. & Kremling, K. Methods of Seawater Analysis 3rd edn. (VCH, 1999).
    Google Scholar 
    52.David, A. R. Analysis of Total organic carbon. UMass Environmental Engineering Program (2012).53.Polunin, N. V. et al. Feeding relationships in Mediterranean bathyal assemblages elucidated by stable nitrogen and carbon isotope data. Mar. Ecol. Prog. Ser. 220, 13–23. https://doi.org/10.3354/meps220013 (2001).ADS 
    CAS 
    Article 

    Google Scholar 
    54.McAuliffe, C. GC determination of solutes by multiple phase equilibrations. Chem. Tech. 1, 46–50 (1971).
    Google Scholar 
    55.Liss, P. S. & Merlivat, L. Air-sea exchange rates: Introduction and synthesis, in the role of air-sea exchange in geochemical cycling. In (ed. Buat-Menard, P.) 113–127 (D Reidel, 1986) https://doi.org/10.1007/978-94-009-4738-2_5.56.Weiss, R. F. & Price, B. A. Nitrous oxide solubility in water and seawater. Mar. Chem. 8, 347–359. https://doi.org/10.1016/0304-4203(80)90024-9 (1980).CAS 
    Article 

    Google Scholar 
    57.Rao, G. D., Rao, V. D. & Sarma, V. V. S. S. Distribution and air–sea exchange of Nitrous oxide in the Coastal Bay of Bengal during peak discharge period(southwest monsoon). Mar. Chem. 155, 1–9. https://doi.org/10.1016/j.marchem.2013.04.014 (2013).CAS 
    Article 

    Google Scholar  More

  • in

    Phenolic acid-degrading Paraburkholderia prime decomposition in forest soil

    1.Van Hees, P. A. W., Jones, D. L., Finlay, R., Godbold, D. L. & Lundström, U. S. The carbon we do not see – The impact of low molecular weight compounds on carbon dynamics and respiration in forest soils: a review. Soil Biol. Biochem. 37, 1–13 (2005).Article 
    CAS 

    Google Scholar 
    2.Shindo, H., Ohta, S. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IX. Distribution of phenolic acids in soils of paddy fields and forests. Soil Sci. Plant Nutr. 24, 233–243 (1978).CAS 
    Article 

    Google Scholar 
    3.Katase, T. Distribution of different forms of p-hydroxybenzoic, vanillic, p-coumaric and ferulic acids in forest soil. Soil Sci. Plant Nutr. 27, 365–371 (1981).CAS 
    Article 

    Google Scholar 
    4.Muscolo, A. & Sidari, M. Seasonal fluctuations in soil phenolics of a coniferous forest: effects on seed germination of different coniferous species. Plant Soil. 284, 305–318 (2006).CAS 
    Article 

    Google Scholar 
    5.Whitehead, D. C., Dibb, H. & Hartley, R. D. Bound phenolic compounds in water extracts of soils, plant roots and leaf litter. Soil Biol. Biochem. 15, 133–136 (1983).CAS 
    Article 

    Google Scholar 
    6.Kuiters, A. T. & Sarink, H. M. Leaching of phenolic compounds from leaf and needle litter of several deciduous and coniferous trees. Soil Biol. Biochem. 18, 475–480 (1986).CAS 
    Article 

    Google Scholar 
    7.Gallet, C. & Pellissier, F. Phenolic compounds in natural solutions of a coniferous forest. J. Chem. Ecol. 23, 2401–2412 (1997).CAS 
    Article 

    Google Scholar 
    8.Schofield, J. A., Hagerman, A. E. & Harold, A. Loss of tannins and other phenolics from willow leaf litter. J. Chem. Ecol. 24, 1409–1421 (1998).CAS 
    Article 

    Google Scholar 
    9.Kaiser, K., Guggenberger, G., Haumaier, L. & Zech, W. Seasonal variations in the chemical composition of dissolved organic matter in organic forest floor layer leachates of old-growth Scots pine (Pinus sylvestris L.) and European beech (Fagus sylvatica L.) stands in northeastern Bavaria, German. Biogeochemistry. 55, 103–143 (2001).CAS 
    Article 

    Google Scholar 
    10.Li H. et al. Forest gaps alter the total phenol dynamics in decomposing litter in an alpine fir forest. PLoS ONE. 11, e0148426 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    11.Blagodatskaya, E. & Kuzyakov, Y. Mechanisms of real and apparent priming effects and their dependence on soil microbial biomass and community structure: critical review. Biol. Fertil. Soils. 45, 115–131 (2008).Article 

    Google Scholar 
    12.Nottingham, A. T., Turner, B. L., Chamberlain, P. M., Stott, A. W. & Tanner, E. V. J. Priming and microbial nutrient limitation in lowland tropical forest soils of contrasting fertility. Biogeochemistry. 111, 219–237 (2012).CAS 
    Article 

    Google Scholar 
    13.Stewart, C. E., Moturi, P., Follett, R. F. & Halvorson, A. D. Lignin biochemistry and soil N determine crop residue decomposition and soil priming. Biogeochemistry. 124, 335–351 (2015).CAS 
    Article 

    Google Scholar 
    14.Lonardo, D. P. Di et al. Priming of soil organic matter: chemical structure of added compounds is more important than the energy content. Soil Biol. Biochem. 108, 41–54 (2017).Article 
    CAS 

    Google Scholar 
    15.Zwetsloot, M. J. et al. Prevalent root-derived phenolics drive shifts in microbial community composition and prime decomposition in forest soil. Soil Biol. Biochem. 145, 530–541 (2020).Article 
    CAS 

    Google Scholar 
    16.Tao, X. et al. Winter warming in Alaska accelerates lignin decomposition contributed by Proteobacteria. Microbiome 8, 84 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Wutzler, T. & Reichstein, M. Priming and substrate quality interactions in soil organic matter models. Biogeosciences. 10, 2089–2103 (2013).Article 

    Google Scholar 
    18.Guenet, B. et al. Impact of priming on global soil carbon stocks. Glob Chang Biol. 24, 1873–1883 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Schöning, I. & Kögel-Knabner, I. Chemical composition of young and old carbon pools throughout Cambisol and Luvisol profiles under forests. Soil Biol. Biochem. 38, 2411–2424 (2006).Article 
    CAS 

    Google Scholar 
    20.Kleber, M. et al. Old and stable soil organic matter is not necessarily chemically recalcitrant: implications for modeling concepts and temperature sensitivity. Glob Chang Biol. 17, 1097–1107 (2011).Article 

    Google Scholar 
    21.Northup, R. R., Dahlgren, R. A. & Yu, Z. Intraspecific variation of conifer phenolic concentration on a marine terrace soil acidity gradient; a new interpretation. Plant Soil. 171, 255–262 (1995).CAS 
    Article 

    Google Scholar 
    22.Sanger, L. J., Cox, P., Splatt, P., Whelan, M. J. & Anderson, J. M. Variability in the quality of Pinus sylvestris needles and litter from sites with different soil characteristics: Lignin and phenylpropanoid signature. Soil Biol. Biochem. 28, 829–835 (1996).CAS 
    Article 

    Google Scholar 
    23.Thevenot, M., Dignac, M. F. & Rumpel, C. Fate of lignins in soils: a review. Soil Biol. Biochem. 42, 1200–1211 (2010).CAS 
    Article 

    Google Scholar 
    24.Zwetsloot, M. J. & Bauerle, T. L. Phenolic root exudate and tissue compounds vary widely among temperate forest tree species and have contrasting effects on soil microbial respiration. New Phytol. 218, 530–541 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    25.Burges, N., Hurst, H. & Walkden, B. The phenolic constituents of humic acid and their relation to the lignin of the plant cover. Geochim. Cosmochim. Acta. 28, 1547–1554 (1964).CAS 
    Article 

    Google Scholar 
    26.Kuiters, A. T. & Denneman, C. A. J. Water-soluble phenolic substances in soils under several coniferous and deciduous tree species. Soil Biol. Biochem. 19, 765–769 (1987).CAS 
    Article 

    Google Scholar 
    27.Jalal, M. A. F. & Read, D. J. The organic acid composition of Calluna heathland soil with special reference to phyto- and fungitoxicity. Plant Soil. 70, 273–286 (1983).CAS 
    Article 

    Google Scholar 
    28.Shindo, H., Ohta, S. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IX. Distribution of phenolic acids in soils of paddy fields and forests. Soil Sci. Plant Nutr. 24, 233–243 (1978).CAS 
    Article 

    Google Scholar 
    29.Whitehead, D. C., Dibb, H. & Hartley, R. D. Extractant pH and the release of phenolic compounds from soils, plant roots and leaf litter. Soil Biol. Biochem. 13, 343–348 (1981).CAS 
    Article 

    Google Scholar 
    30.Ed, V., Boyd, S. & Mokma, D. Extraction of phenolic compounds from a spodsol profile. Soil Sci. 140, 412–420 (1985).Article 

    Google Scholar 
    31.Wang, Y. et al. Environmental behaviors of phenolic acids dominated their rhizodeposition in boreal poplar plantation forest soils. J. Soils Sediments. 16, 1858–1870 (2016).CAS 
    Article 

    Google Scholar 
    32.Phillips, R. P. et al. Tree species and mycorrhizal associations influence the magnitude of rhizosphere effects. Ecology. 87, 1302–1313 (2006).PubMed 
    Article 

    Google Scholar 
    33.Blum, U. & Shafer, S. R. Microbial populations and phenolic acids in soil. Soil Biol. Biochem. 20, 793–800 (1988).CAS 
    Article 

    Google Scholar 
    34.Shafer, S. R. & Blum, U. Influence of Phenolic acids on microbial populations in the rhizosphere of cucumber. J. Chem. Ecol. 17, 369–389 (1991).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Eilers, K. G., Lauber, C. L., Knight, R. & Fierer, N. Shifts in bacterial community structure associated with inputs of low molecular weight carbon compounds to soil. Soil Biol. Biochem. 42, 896–903 (2010).CAS 
    Article 

    Google Scholar 
    36.Morrissey, E. M. et al. Phylogenetic organization of bacterial activity. ISME J. 10, 2336–2340 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Huang P., Wang T., Wang M., Wu M., Hsu N. Retention of phenolic acids by noncrystalline hydroxy-aluminum and-iron compounds and clay minerals of soils. Soil Sci. 123, 213–219 (1977).CAS 
    Article 

    Google Scholar 
    38.Cecchi, A. M., Koskinen, W. C., Cheng, H. H. & Haider, K. Sorption-desorption of phenolic acids as affected by soil properties. Biol. Fertil. Soils. 39, 235–242 (2004).CAS 
    Article 

    Google Scholar 
    39.Shindo, H. & Kuwatsuka, S. Behavior of phenolic substances in the decaying process of plants: IV adsorption and movement of phenolic acids in soils. Soil Sci. Plant Nutr. 22, 23–33 (1976).CAS 
    Article 

    Google Scholar 
    40.DeAngelis K. M. et al. Characterization of trapped lignin-degrading microbes in tropical forest soil. PLoS ONE. 6, e19306 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Pold G., Melillo J. M., DeAngelis K. M. Two decades of warming increases diversity of a potentially lignolytic bacterial community. Front Microbiol. 6, 480 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Wilhelm, R. C., Singh, R., Eltis, L. D. & Mohn, W. W. Bacterial contributions to delignification and lignocellulose degradation in forest soils with metagenomic and quantitative stable isotope probing. ISME J. 13, 413–429 (2019).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Folman, L. B., Klein Gunnewiek, P. J. A., Boddy, L., De & Boer, W. Impact of white-rot fungi on numbers and community composition of bacteria colonizing beech wood from forest soil. FEMS Microbiol. Ecol. 63, 181–191 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Valášková, V. et al. Phylogenetic composition and properties of bacteria coexisting with the fungus Hypholoma fasciculare in decaying wood. ISME J. 3, 1218–1221 (2009).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    45.Mandal, S. M., Chakraborty, D. & Dey, S. Phenolic acids act as signaling molecules in plant-microbe symbioses. Plant Signal Behav. 5, 359–368 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Munoz Aguilar, M. et al. Chemotaxis of rhizobium leguminosarum biovar phaseoli towards flavonoid inducers of the symbiotic nodulation genes. J. Gen. Microbiol. 134, 2741–2746 (1988).CAS 

    Google Scholar 
    47.Morrissey, E. M. et al. Bacterial carbon use plasticity, phylogenetic diversity and the priming of soil organic matter. ISME J. 11, 1890–1899 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Fontaine, S., Mariotti, A. & Abbadie, L. The priming effect of organic matter: a question of microbial competition? Soil Biol. Biochem. 35, 837–843 (2003).CAS 
    Article 

    Google Scholar 
    49.Liu, X. J. A. et al. The soil priming effect: consistent across ecosystems, elusive mechanisms. Soil Biol Biochem. 140, 107617 (2020).CAS 
    Article 

    Google Scholar 
    50.Fanin, N., Alavoine, G. & Bertrand, I. Temporal dynamics of litter quality, soil properties and microbial strategies as main drivers of the priming effect. Geoderma [Internet]. 377, 114576 (2020).CAS 
    Article 

    Google Scholar 
    51.Fuchs, G., Boll, M. & Heider, J. Microbial degradation of aromatic compounds – from one strategy to four. Nat. Rev. Microbiol. 9, 803–816 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Yang, Z. H. & Ji, G. D. Quantitative response relationships between degradation rates and functional genes during the degradation of beta-cypermethrin in soil. J. Hazard Mater. 299, 719–724 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    53.Nishiyama, E., Ohtsubo, Y., Nagata, Y. & Tsuda, M. Identification of Burkholderia multivorans ATCC 17616 genes induced in soil environment by in vivo expression technology. Environ. Microbiol. 12, 2539–2558 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    54.Wilhelm et al. Paraburkholderia madseniana sp. nov., a phenolic acid-degrading bacterium isolated from acidic forest soil. Int. J. Syst. Evol. Microbiol. 70, (2020). https://doi.org/10.1099/ijsem.0.004029.55.Pallant, E. & Riha, S. J. Surface soil acidification under red pine and Norway spruce. Soil Sci. Soc. Am. J. 54, 1124–1130 (1990).CAS 
    Article 

    Google Scholar 
    56.Fahey T. J. et al. Earthworm effects on the incorporation of litter C and N into soil organic matter in a sugar maple forest. Ecol. Appl. 23, 1185–1201 (2013).PubMed 
    Article 

    Google Scholar 
    57.Melvin, A. M. & Goodale, C. L. Tree species and earthworm effects on soil nutrient distribution and turnover in a northeastern United States common garden. Can. J. For. Res. 43, 180–187 (2013).CAS 
    Article 

    Google Scholar 
    58.Suarez E. Invasion of Northern Hardwood Forests by Exotic Earthworm Communities in South-Central New York. Cornell; 2004.59.Greweling T., Peech M. Chemical soil tests. Ithaca; 1960.60.Griffiths, R. I., Whiteley, A. S., O’Donnell, A. G. & Bailey, M. J. Rapid method for coextraction of DNA and RNA from natural environments for analysis of ribosomal DNA- and rRNA-based microbial community composition. Appl. Environ. Microbiol. 66, 5488–5491 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Neufeld, J. D. et al. DNA stable-isotope probing. Nat. Protoc. 2, 860–866 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Wilhelm, R., Szeitz, A., Klassen, T. L. & Mohn, W. W. Sensitive, efficient quantitation of 13C-enriched nucleic acids via ultrahigh-performance liquid chromatography-tandem mass spectrometry for applications in stable isotope probing. Appl. Environ. Microbiol. 80, 7206–7211 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    63.Kozich, J. J., Westcott, S. L., Baxter, N. T., Highlander, S. K. & Schloss, P. D. Development of a dual-index sequencing strategy and curation pipeline for analyzing amplicon sequence data on the miseq illumina sequencing platform. Appl. Environ. Microbiol. 79, 5112–5120 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 1–21 (2014).Article 
    CAS 

    Google Scholar 
    65.De Caceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology, Ecology. 90, 3566–3574 (2009). http://sites.google.com/site/miqueldecaceres/.PubMed 
    Article 

    Google Scholar 
    66.Wilhelm, R. C., Niederberger, T. D., Greer, C. & Whyte, L. G. Microbial diversity of active layer and permafrost in an acidic wetland from the Canadian High Arctic. Can. J. Microbiol. 57, 303–315 (2011).CAS 
    PubMed 
    Article 

    Google Scholar 
    67.Ye, J. et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain reaction. BMC Bioinformatics. 13, 134 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.López-Gutiérrez, J. C. et al. Quantification of a novel group of nitrate-reducing bacteria in the environment by real-time PCR. J. Microbiol. Methods. 57, 399–407 (2004).PubMed 
    Article 
    CAS 

    Google Scholar 
    69.Markowitz, V. M. et al. IMG/M: A data management and analysis system for metagenomes. Nucleic Acids Res. 36(Suppl. 1), 534–538 (2008).
    Google Scholar 
    70.Martiny, A. C., Treseder, K. & Pusch, G. Phylogenetic conservatism of functional traits in microorganisms. ISME J. 7, 830–838 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    72.Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41(D1), 590–596 (2013).Article 
    CAS 

    Google Scholar 
    74.Wickham, H. Elegant graphics for data analysis. Media. 35, 211 (2009).
    Google Scholar 
    75.McMurdie P. J., Holmes S. Phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 8, e61217 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Oksanen J. et al. Vegan: community ecology package. R Packag. 2015;77.Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Price M. N., Dehal P. S., Arkin A. P. FastTree 2 – Approximately maximum-likelihood trees for large alignments. PLoS ONE. 5, e9490 (2010).Article 
    CAS 

    Google Scholar 
    79.Wilhelm R. C. et al. Paraburkholderia solitsugae sp. nov. and Paraburkholderia elongata sp. nov., phenolic acid-degrading bacteria isolated from forest soil and emended description of Paraburkholderia madseniana. Int. J. Syst. Evol. Microbiol. 70, 5093–5105 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Chain, P. S. G. et al. Burkholderia xenovorans LB400 harbors a multi-replicon, 9.73-Mbp genome shaped for versatility. PNAS. 103, 15280–15287 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Mason-Jones, K. & Kuzyakov, Y. “Non-metabolizable” glucose analogue shines new light on priming mechanisms: Triggering of microbial metabolism. Soil Biol. Biochem. 107, 68–76 (2017).CAS 
    Article 

    Google Scholar 
    82.Yuan, Y. et al. Exudate components exert different influences on microbially mediated C losses in simulated rhizosphere soils of a spruce plantation. Plant Soil. 419, 127–140 (2017).CAS 
    Article 

    Google Scholar 
    83.Liu, X. J. A. et al. Labile carbon input determines the direction and magnitude of the priming effect. Appl. Soil Ecol. 109, 7–13 (2017).Article 

    Google Scholar 
    84.Sugai, S. F. & Schimel, J. P. Decomposition and biomass incorporation of 14C-labeled glucose and phenolics in taiga forest floor: effect of substrate quality, successional state, and season. Soil Biol. Biochem. 25, 1379–1389 (1993).CAS 
    Article 

    Google Scholar 
    85.Fontaine, S. et al. Fungi mediate long term sequestration of carbon and nitrogen in soil through their priming effect. Soil Biol. Biochem. 43, 86–96 (2011).CAS 
    Article 

    Google Scholar 
    86.Zhu, Z. et al. Microbial stoichiometric flexibility regulates rice straw mineralization and its priming effect in paddy soil. Soil Biol. Biochem. 121, 67–76 (2018).CAS 
    Article 

    Google Scholar 
    87.Roller, B. R. K., Stoddard, S. F. & Schmidt, T. M. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat. Microbiol. 1, 1–7 (2016).Article 
    CAS 

    Google Scholar 
    88.Smirnova, G. V. & Oktyabrsky, O. N. Relationship between Escherichia coli growth rate and bacterial susceptibility to ciprofloxacin. FEMS Microbiol. Lett. 365, 1–6 (2018).Article 
    CAS 

    Google Scholar 
    89.Klappenbach, J. A., Dunbar, J. M. & Schmidt, T. M. rRNA operon copy number reflects ecological strategies of bacteria. Appl. Environ. Microbiol. 66, 1328–1333 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    90.Méndez V., Agulló L., González M., Seeger M. The homogentisate and homoprotocatechuate central pathways are involved in 3- and 4-hydroxyphenylacetate degradation by Burkholderia xenovorans LB400. PLoS ONE. 6, e17583 (2011).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    91.Johnson, G. R. & Olsen, R. H. Multiple pathways for toluene degradation in Burkholderia sp. strain JS150. Appl. Environ. Microbiol. 63, 4047–4052 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    92.Andreolli, M. et al. Endophytic Burkholderia fungorum DBT1 can improve phytoremediation efficiency of polycyclic aromatic hydrocarbons. Chemosphere. 92, 688–694 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Somtrakoon, K. et al. Phenanthrene stimulates the degradation of pyrene and fluoranthene by Burkholderia sp. VUN10013. World J. Microbiol. Biotechnol. 24, 523–531 (2008).CAS 
    Article 

    Google Scholar 
    94.Chain, P. S. G. et al. Burkholderia xenovorans LB400 harbors a multi-replicon, 9.73-Mbp genome shaped for versatility. Proc. Natl. Acad. Sci. 103, 15280–15287 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    95.Raj, A., Krishna Reddy, M. M. & Chandra, R. Identification of low molecular weight aromatic compounds by gas chromatography-mass spectrometry (GC-MS) from kraft lignin degradation by three Bacillus sp. Int. Biodeterior Biodegrad. 59, 292–296 (2007).CAS 
    Article 

    Google Scholar 
    96.Shi, Y. et al. Biochemical investigation of kraft lignin degradation by Pandoraea sp. B-6 isolated from bamboo slips. Bioprocess Biosyst. Eng. 36, 1957–1965 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    97.Moraes, E. C. et al. Lignolytic-consortium omics analyses reveal novel genomes and pathways involved in lignin modification and valorization. Biotechnol. Biofuels. 11, 1–16 (2018).CAS 
    Article 

    Google Scholar 
    98.Coenye, T. et al. Burkholderia fungorum sp. nov. and Burkholderia caledonica sp. nov., two new species isolated from the environment, animals and human clinical samples. Int. J. Syst. Evol. Microbiol. 51, 1099–1107 (2001).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    99.Lim, Y. W., Baik, K. S., Han, S. K., Kim, S. B. & Bae, K. S. Burkholderia sordidicola sp. nov., isolated from the white-rot fungus Phanerochaete sordida. Int. J. Syst. Evol. Microbiol. 53, 1631–1636 (2003).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    100.Herzog C. et al. Microbial succession on decomposing root litter in a drought-prone Scots pine forest. ISME J. 13, 2346–2362 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    101.Yeoh Y. K. et al. Evolutionary conservation of a core root microbiome across plant phyla along a tropical soil chronosequence. Nat. Commun. 8, 215 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    102.Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 1–11 (2018).Article 
    CAS 

    Google Scholar 
    103.Badri, D. V., Chaparro, J. M., Zhang, R., Shen, Q. & Vivanco, J. M. Application of natural blends of phytochemicals derived from the root exudates of arabidopsis to the soil reveal that phenolic-related compounds predominantly modulate the soil microbiome. J. Biol. Chem. 288, 4502–4512 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    104.Sinsabaugh, R. L. Phenol oxidase, peroxidase and organic matter dynamics of soil. Soil Biol. Biochem. 42, 391–404 (2010).CAS 
    Article 

    Google Scholar 
    105.Henning, J. A. et al. Root bacterial endophytes alter plant phenotype, but not physiology. PeerJ. 2016, 1–20 (2016).
    Google Scholar 
    106.Caballero-Mellado, J., Martínez-Aguilar, L., Paredes-Valdez, G., & Estrada-de los Santos, P. Burkholderia unamae sp. nov., an N2-fixing rhizospheric and endophytic species. Int. J. Syst. Evol. Microbiol. 54, 1165–1172 (2004).CAS 
    PubMed 
    Article 

    Google Scholar 
    107.Martínez-Aguilar, L. et al. Burkholderia caballeronis sp. nov., a nitrogen fixing species isolated from tomato (Lycopersicon esculentum) with the ability to effectively nodulate Phaseolus vulgaris. Antonie van Leeuwenhoek. Int. J. Gen. Mol. Microbiol. 104, 1063–1071 (2013).
    Google Scholar 
    108.De Meyer, S. E. et al. Symbiotic and non-symbiotic Paraburkholderia isolated from South African Lebeckia ambigua root nodules and the description of Paraburkholderia fynbosensis sp. Nov. Int. J. Syst. Evol. Microbiol. 68, 2607–2614 (2018).PubMed 
    Article 
    CAS 

    Google Scholar 
    109.Peeters, C. et al. Phylogenomic study of Burkholderia glathei-like organisms, proposal of 13 novel Burkholderia species and emended descriptions of Burkholderia sordidicola, Burkholderia zhejiangensis, and Burkholderia grimmiae. Front Microbiol. 7, 1–19 (2016).
    Google Scholar 
    110.Vandamme, P. et al. Burkholderia humi sp. nov., Burkholderia choica sp. nov., Burkholderia telluris sp. nov., Burkholderia terrestris sp. nov. and Burkholderia udeis sp. nov.: Burkholderia glathei-like bacteria from soil and rhizosph. Int. J. Syst. Evol. Microbiol. 63(PART 12), 4707–4718 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    111.Shiraishi, A., Matsushita, N. & Hougetsu, T. Nodulation in black locust by the Gammaproteobacteria Pseudomonas sp. and the Betaproteobacteria Burkholderia sp. Syst. Appl. Microbiol. 33, 269–274 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    112.Thijs, S. et al. Exploring the rhizospheric and endophytic bacterial communities of Acer pseudoplatanus growing on a TNT-contaminated soil: towards the development of a rhizocompetent TNT-detoxifying plant growth promoting consortium. Plant Soil. 385, 15–36 (2014).CAS 
    Article 

    Google Scholar 
    113.Mavengere, N. R., Ellis, A. G. & Le Roux, J. J. Burkholderia aspalathi sp. nov., isolated from root nodules of the South African legume Aspalathus abietina Thunb. Int. J. Syst. Evol. Microbiol. 64(PART 6), 1906–1912 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    114.Blair, P. M. et al. Exploration of the biosynthetic potential of the populus microbiome. mSystems. 3, 1–17 (2018).Article 

    Google Scholar 
    115.Peters, N. K. & Verma, D. P. S. Phenolic compounds as regulators of gene expression in plant-microbe interactions. Mol. Plant-Microbe Interact. 3, 4–8 (1990).CAS 
    PubMed 
    Article 
    PubMed Central 

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

    Google Scholar  More

  • in

    Successful extraction of insect DNA from recent copal inclusions: limits and perspectives

    1.Higuchi, R. et al. DNA sequences from the quagga, an extinct member of the horse family. Nature 312, 282–284 (1984).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    2.Dabney, J. et al. Complete mitochondrial genome sequence of a Middle Pleistocene cave bear reconstructed from ultrashort DNA fragments. Proc. Natl. Acad. Sci. U. S. A. 110, 15758–15763 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    3.Hansen, H. B. et al. Comparing ancient DNA preservation in petrous bone and tooth cementum. PLoS ONE 12, e0170940 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    4.Hagan, R. W. et al. Comparison of extraction methods for recovering ancient microbial DNA from paleofeces. Am. J. Phys. Anthropol. 171, 275–284 (2020).PubMed 
    Article 

    Google Scholar 
    5.Epp, L. S., Zimmermann, H. H. & Stoof-Leichsenring, K. R. Sampling and extraction of ancient DNA from sediments. In Ancient DNA. Methods in Molecular Biology (eds Shapiro, B. et al.) 31–44 (Humana Press, 2019).
    Google Scholar 
    6.Modi, A. et al. Combined methodologies for gaining much information from ancient dental calculus: testing experimental strategies for simultaneously analysing DNA and food residues. Archaeol. Anthropol. Sci. 12, 10 (2020).Article 

    Google Scholar 
    7.Campos, P. F. & Gilbert, M. T. P. DNA extraction from keratin and chitin. In Ancient DNA. Methods in Molecular Biology (eds Shapiro, B. et al.) 57–63 (Humana Press, 2019).
    Google Scholar 
    8.Adler, C. J. et al. Sequencing ancient calcified dental plaque shows changes in oral microbiota with dietary shifts of the neolithic and industrial revolutions. Nat. Genet. 45, 450-455e1 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    9.Warinner, C. et al. Pathogens and host immunity in the ancient human oral cavity. Nat. Genet. 46, 336–344 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Weyrich, L. S. et al. Neanderthal behaviour, diet, and disease inferred from ancient DNA in dental calculus. Nature 544, 357–361 (2017).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    11.Slon, V. et al. Neandertal and Denisovan DNA from Pleistocene sediments. Science 356, 605–608 (2017).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    12.Teasdale, M. D. et al. The York Gospels: a 1000-year biological palimpsest. R. Soc. Open. Sci. 4, 170988 (2017).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    13.Boast, A. et al. Coprolites reveal ecological interactions lost with the extinction of New Zealand birds. Proc. Natl. Acad. Sci. U. S. A. 115, 1546–1551 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Zarrillo, S. et al. The use and domestication of Theobroma cacao during the mid-Holocene in the upper Amazon. Nat. Ecol. Evol. 2, 1879–1888 (2018).PubMed 
    Article 

    Google Scholar 
    15.Cano, R. J. et al. Amplification and sequencing of DNA from a 120–135 million-year-old weevil. Nature 363, 536–538 (1993).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    16.DeSalle, R. et al. DNA sequences from a fossil termite in Oligo-Miocene amber and their phylogenetic implications. Science 257, 1933–1936 (1992).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    17.Sherratt, E. et al. Amber fossils demonstrate deep-time stability of Caribbean lizard communities. Proc. Natl. Acad. Sci. U. S. A. 112, 9961–9966 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    18.Sadowski, E. M. et al. Carnivorous leaves from Baltic amber. Proc. Natl. Acad. Sci. U. S. A. 112, 190–195 (2015).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    19.Xing, L. et al. A feathered dinosaur tail with primitive plumage trapped in mid-Cretaceous amber. Curr. Biol. 26, 3352–3360 (2016).CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Rikkinen, J., Grimaldi, D. A. & Schmidt, A. R. Morphological stasis in the first myxomycete from the Mesozoic, and the likely role of cryptobiosis. Sci. Rep. 9, 19730 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    21.Peñalver, E. et al. Thrips pollination of Mesozoic gymnosperms. Proc. Natl. Acad. Sci. U. S. A. 109, 8623–8628 (2012).PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    22.Cai, C. et al. Beetle pollination of cycads in the mesozoic. Curr. Biol. 28, 2806-2812.e1 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Bao, T., Wang, B., Li, J. & Dilcher, D. Pollination of Cretaceous flowers. Proc. Natl. Acad. Sci. U. S. A. 116, 24707–24711 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Labandeira, C. Amber. Paleont. Soc. Pap. 20, 163–215 (2014).Article 

    Google Scholar 
    25.Solórzano-Kraemer, M. M. et al. A revised definition for copal and its significance for palaeontological and Anthropocene biodiversity-loss studies. Sci. Rep. 10, 19904 (2020).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    26.Clifford, D. J. & Hatcher, P. G. Structural transformations of polylabdanoid resinites during maturation. Org. Geochem. 23, 407–418 (1995).CAS 
    Article 

    Google Scholar 
    27.Lambert, J. B., Santiago-Blay, J. A., Wu, Y. & Levy, A. J. Examination of amber and 490 related materials by NMR spectroscopy. Magn. Reson. Chem. 53, 2–8 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    28.Stankiewicz, B. A. et al. Chemical preservation of plants and insects in natural resins. Proc. Biol. Sci. 265, 641–647 (1998).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    29.McCoy, V. E. et al. Ancient amino acids from fossil feathers in amber. Sci. Rep. 9, 6420 (2019).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    30.Bada, J. L. et al. Amino acid racemization in amber-entombed insects: implications for DNA preservation. Geochim. Cosmochim. Acta. 58, 3131–3135 (1994).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    31.Collins, M. J. et al. Is amino acid racemization a useful tool for screening for ancient DNA in bone?. Proc. R. Soc. B 276, 2971–2977 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Allentoft, M. E. et al. The half-life of DNA in bone: measuring decay kinetics in 158 dated fossils. Proc. R. Soc. B 279, 4724–4733 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    33.Kistler, L. et al. A new model for ancient DNA decay based on paleogenomic meta-analysis. Nucleic Acids Res. 45, 6310–6320 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    34.DeSalle, R., Barcia, M. & Wray, C. PCR jumping in clones of 30-million-year-old DNA fragments from amber preserved termites (Mastotermes electrodominicus). Experientia 49, 906–909 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    35.Poinar, G. O., Poinar, H. N. & Cano, R. J. DNA from amber inclusions. In Ancient DNA (eds Herrmann, B. & Hummel, S.) 92–103 (Springer, 1994).
    Google Scholar 
    36.Austin, J. J. et al. Problems of reproducibility: does geologically ancient DNA survive in amber-preserved insects?. Proc. Roy. Soc. Lond. Ser. B 264, 467–474 (1997).CAS 
    Article 
    ADS 

    Google Scholar 
    37.Penney, D. et al. Absence of ancient DNA in sub-fossil insect inclusions preserved in ‘Anthropocene’ Colombian copal. PLoS ONE 8, e73150 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    38.Peris, D. et al. DNA from resin-embedded organisms: past, present and future. PLoS ONE 15, e0239521 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Reich, D. et al. Genetic history of an archaic hominin group from Denisova Cave in Siberia. Nature 468, 1053–1060 (2010).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    40.Meyer, M. et al. A high-coverage genome sequence from an archaic Denisovan individual. Science 338, 222–226 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    41.Prüfer, K. et al. The complete genome sequence of a Neandertal from the Altai Mountains. Nature 505, 43–49 (2014).PubMed 
    Article 
    ADS 
    CAS 

    Google Scholar 
    42.Prüfer, K. et al. A high-coverage Neandertal genome from Vindija Cave in Croatia. Science 358, 655–658 (2017).PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 
    43.Meyer, M. et al. Nuclear DNA sequences from the Middle Pleistocene Sima de los Huesos hominins. Nature 531, 504–507 (2016).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    44.Gilbert, M. T., Bandelt, H. J., Hofreiter, M. & Barnes, I. Assessing ancient DNA studies. Trends Ecol. Evol. 20, 541–544 (2005).PubMed 
    Article 

    Google Scholar 
    45.Willerslev, E. & Cooper, A. Ancient DNA. Proc. Biol. Sci. 272, 3–16 (2005).CAS 
    PubMed 

    Google Scholar 
    46.Penney, D., Wadsworth, C. & Green, D. I. Extraction of inclusions from (sub)fossil resins, with description of a new species of stingless bee (Hymenoptera: Apidae: Meliponini), in quaternary Colombian copal. Paleontol. Contrib. 2013, 7:1–6 (2013).
    Google Scholar 
    47.Meyer, M. & Kircher, M. Illumina sequencing library preparation for highly multiplexed target capture and sequencing. Cold Spring Harb. Protoc. 6, pdb.prot5448 (2010).Article 

    Google Scholar 
    48.Modi, A. et al. Complete mitochondrial sequences from Mesolithic Sardinia. Sci. Rep. 7, 42869 (2017).PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    49.Peltzer, A. et al. EAGER: efficient ancient genome reconstruction. Genome Biol. 17, 60 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    50.Andrews, S. FastQC: a quality control tool for high throughput sequence data (2010). Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc51.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Schubert, M. et al. Improving ancient DNA read mapping against modern reference genomes. BMC Genomics 13, 178 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Li, H. et al. The Sequence alignment/map (SAM) format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    54.Altschul, S. et al. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Jackman, S. D. et al. ABySS 2.0: resource-efficient assembly of large genomes using a Bloom filter. Genome Res. 27, 768–777 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    56.Huson, D. H. et al. MEGAN community edition: interactive exploration and analysis of large-scale microbiome sequencing data. PLoS Comput. Biol. 12, e1004957 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    57.Hofreiter, M., Jaenicke, V., Serre, D., Haeseler, A. & Pääbo, S. DNA sequences from multiple amplifications reveal artifacts induced by cytosine deamination in ancient DNA. Nucleic Acids Res. 9, 4793–4799 (2011).
    Google Scholar 
    58.Briggs, A. W. et al. Patterns of damage in genomic DNA sequences from a Neandertal. Proc. Natl. Acad. Sci. U. S. A. 104, 14616–14621 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    59.Sawyer, S., Krause, J., Guschanski, K., Savolainen, V. & Pääbo, S. Temporal patterns of nucleotide misincorporations and DNA fragmentation in ancient DNA. PLoS ONE 7, e34131 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    60.Jonsson, H., Ginolhac, A., Schubert, M., Johnson, P. L. & Orlando, L. mapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 29, 1682–1684 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Kumar, S. et al. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Noonan, J. P. et al. Genomic sequencing of pleistocene cave bears. Science 309, 597–599 (2005).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    64.Green, R. E. et al. Analysis of one million base pairs of Neanderthal DNA. Nature 444, 330–336 (2006).CAS 
    PubMed 
    Article 
    ADS 

    Google Scholar 
    65.Garcia-Garcera, M. et al. Fragmentation of contaminant and endogenous DNA in ancient samples determined by shotgun sequencing; prospects for human palaeogenomics. PLoS ONE 6, e24161 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    66.Llamas, B. et al. From the field to the laboratory: Controlling DNA contamination in human ancient DNA research in the high-throughput sequencing era. STAR 3, 1–14 (2017).Article 

    Google Scholar 
    67.Wintertona, S. L., Brian, M. W. & Evert, I. S. Phylogeny and Bayesian divergence time estimations of small-headed Xies (Diptera: Acroceridae) using multiple molecular markers. Mol. Phylogenet. Evol. 43, 808–832 (2007).Article 
    CAS 

    Google Scholar 
    68.Gillung, J. P. & Wintertona, S. L. Evolution of fossil and living spider flies based onmorphological and molecular data (Diptera, Acroceridae). Syst. Entomol. 44, 820–841 (2019).Article 

    Google Scholar 
    69.Klasson, L. et al. The mosaic genome structure of the Wolbachia wRi strain infecting Drosophila simulans. Proc. Natl. Acad. Sci. U. S. A. 106, 5725–5730 (2009).CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 
    70.Daley, T. & Smith, A. D. Predicting the molecular complexity of sequencing libraries. Nat. Methods 10, 325–327 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Detection and monitoring of Drosophila suzukii in raspberry and cherry orchards with volatile organic compounds in the USA and Europe

    Spotted wing drosophila captures within the United StatesComparison between the raspberry field and wooded area, during pre-harvest and harvest periods to account for presence of developing and fully ripened fruit, SWD captures and selectivity per QB dry sticky trap is found in Fig. 1A,B. No difference was found in average capture per trap between either area during the pre-harvest period, nor was there a difference between these and the field during the harvest period. The wooded area during the harvest period captured the greatest amount of SWD/trap (F1,209 = 7.335, P = 0.007) (Fig. 1A). Dry sticky traps baited with QB had a significantly higher selectivity during the pre-harvest period in the raspberry field than in the wooded area but was not significantly different from the trap selectivity in the wooded area during the harvest period. The pre-harvest wooded area trap selectivity was not different from the harvest field trap selectivity. While the harvest field trap selectivity was lower than that of the wooded area trap selectivity during the same period (F1,203 = 23.6, P  More

  • in

    Cold-water species need warm water too

    1.Root, T. L. et al. Nature 421, 57–60 (2003).CAS 
    Article 

    Google Scholar 
    2.Morelli, T. L. et al. Front. Ecol. Environ. 18, 228–234 (2020).Article 

    Google Scholar 
    3.Armstrong, J. B. et al. Nat. Clim. Change https://doi.org/10.1038/s41558-021-00994-y (2021).4.Northcote, T. G. N. Am. J. Fish. Manage. 17, 1029–1045 (1997).Article 

    Google Scholar 
    5.Xu, C., Letcher, B. H. & Nislow, K. H. Freshwater Biol. 55, 2253–2264 (2010).Article 

    Google Scholar 
    6.Schlosser, I. J. BioScience 41, 704–712 (1991).Article 

    Google Scholar 
    7.Al-Chokhachy, R., Alder, J., Hostetler, S., Gresswell, R. & Shepard, B. Glob. Change Biol. 19, 3069–3081 (2013).Article 

    Google Scholar 
    8.Fausch, K. D., Torgersen, C. E., Baxter, C. V. & Li, H. W. BioScience 52, 483–498 (2002).Article 

    Google Scholar 
    9.Muhlfeld, C. C. et al. Science 360, 866–867 (2018).CAS 

    Google Scholar 
    10.Kovach, R. P. et al. Rev. Fish Biol. Fisher. 26, 135–151 (2016).Article 

    Google Scholar 
    11.Isaak, D. J., Young, M. K., Nagel, D. E., Horan, D. L. & Groce, M. C. Glob. Change Biol. 21, 2540–2553 (2015).Article 

    Google Scholar 
    12.Isaak, D. J. et al. Water Resour. Res. 53, 9181–9205 (2017).Article 

    Google Scholar 
    13.Small-Lorenz, S. L., Culp, L. A., Ryder, T. B., Will, T. C. & Marra, P. P. Nat. Clim. Change 3, 91–93 (2013).Article 

    Google Scholar 
    14.Rieman, B. E. & Dunham, J. B. Ecol. Freshw. Fish 9, 51–64 (2000).Article 

    Google Scholar 
    15.Guzzo, M. M., Blanchfield, P. J. & Rennie, M. D. Proc. Natl Acad. Sci. USA 114, 9912–9917 (2017).CAS 
    Article 

    Google Scholar 
    16.Brennan, S. R. et al. Science 364, 783–786 (2019).CAS 
    Article 

    Google Scholar 
    17.Hauer, F. R. et al. Sci. Adv. 2, e1600026 (2016).Article 

    Google Scholar  More

  • in

    What are the traits of a social-ecological system: towards a framework in support of urban sustainability

    Traits are attributes that speak to biophysical limitations, pressure on species, ecological functionality, and interactions. They have found their way to the forefront of many discussions and debates about ecosystem dynamics and, with a slight time lag, social-ecological systems1,2,3. The promise is that a traits framework can further our understanding of patterns, dynamics, interactions, and tipping points within and across complex social-ecological systems. But what will it take to make good on this promise, in particular for our cities, where change is fast and—being the places where the majority of humans live—human perceptions are particularly diverse? What kind of framing, what research, would allow traits—classically understood as a different representation and interpretation of well-established and known properties of the social-ecological system―to fully work as “mediators” for understanding the behavior, functions, and needs of urban systems under pressure?This perspective aims to contribute to the current wide-ranging discussion about traits in both theoretical and applied ecology, and parallel work on better understanding human connections to nature. To this end, we explore the potential of using an expanded conceptualization of traits as a platform for integrated approaches to understanding the different facets of people-in-nature relationships and dynamics4,5.Expanding from the original “characteristics which have demonstrable links to the organism’s function”6, we see traits as a nexus where different theories and conceptualisations about social-ecological systems can connect, intertwine and comprehensively allow us to assess the current state of a system—and even more importantly, evaluate the implications of change (Box 1 and Fig. 1). To make it an integrative and useful framework for urban studies and policy/practice, traits need to be easy to recognise and relevant to decision makers across scales and in different contexts. In addition, information on trait profiles—generic as well as site specific—need to be easily available through monitoring or in databases.Fig. 1: Traits within social ecological systems.Theoretical flow chart linking the entities of a social-ecological system to its traits, demonstrating how a traits framework—as outlined in this article—might be positioned to support the analysis, interpretation and governance of urban systems.Full size imageOur argument is threefold: The first dimension focuses on how to assess and anticipate change by establishing chains of interconnected traits that describe and causally connect sensitivity and response to different urban pressures such as heat, soil compaction, environmental toxicants, and stormwater runoff, understood through “response” traits7,8,9,10 to their functional consequences11, mediated by “effect traits”. The second dimension is grounded in human perceptions and appraisal of diversity to highlight the different cues and characteristics people use to detect change or articulate value narratives, and it is linked to the role of traits in ecological literacy. Here, we propose traits be viewed as boundary objects, i.e., features that carry meaning across society (although the meanings might be diverse and sometimes conflicting), and that this second dimension is essential for understanding the role of society and humans in a traits framework. The third dimension outlines how the first two dimensions connect to inform and support decision making and management at different scales, for example in different, multilayer, and multiactor governance processes12 (Fig. 2).Fig. 2: A traits framework for scientific study and practical application.The three dimensions of a social-ecological traits framework for understanding and governing urban systems. The first dimension is represented by observable traits of the urban environment, e.g., features of humans and other co-inhabiting species and their differing responses to pressures and selection, leading to functional consequences and finally, altered characters of an urban social-ecological system. The second dimension is characterized by feedback loops between those effect outcomes and individual and collective perceptions and decision making. Lastly, the third dimension is represented by urban ecosystem planning and management embedded in governance processes and instruments. Through its ability to connect different spheres and discourses, an expanded traits framework can aim for effective and inclusive decision support that is responsive and place-adapted. By expanding and bridging these three dimensions, we can connect different insights and knowledge about ecosystem function and human perceptions, values and interactions with the environment. This will support the development of a (meta-) theoretically grounded, practically applicable traits framework to interrogate reciprocal feedback linkages and nature-human relationships. The figure includes resources from Freepik.com.Full size imageBox 1 definitionsFunctional trait: A feature of an organism which has demonstrable links to the organism’s function69, and, as such, “determines the organism’s response to pressures (response trait), and/or its effects on ecosystem processes or services (effect trait). In plants, functional traits include morphological, ecophysiological, biochemical and regeneration traits, including demographic traits (at population level). In animals, these traits are combined with life history and behavioral traits (e.g., guilds, organisms that use similar resources/habitats)”70, p. 2779.Boundary object: “[…] those […] objects which both inhabit several intersecting social worlds and satisfy the information requirements of each of them. Boundary objects are objects, which are both plastic enough to adapt to local needs and the constraints of the several parties involving them, yet robust enough to maintain a common identity across sites. […] They have different meanings in different social worlds [and across cultures] but their structure is common enough to more than one world to make them recognizable, a means of translation.”71 p. 393, see also72.Social-ecological traits (expanded definition): An ecologically or socially (inter)active and demonstrable feature of the environment at any level or scale. A social-ecological trait either mediates reactions to selective social-ecological filtering (response trait) or determines effects on ecosystem processes or services (effect trait), or both. The aggregate trait profile of a given entity should ideally speak both to ecological functioning and socio-cultural meaning.The first dimension: response and its effect outcomesTrait-based approaches have been used for descriptive purposes13 to enable broader global comparisons that transcend the constraints of regional taxonomic diversity (e.g., see refs. 6,14) and allow for the types of generalizations sought in ecology15,16. Traits offer a way of looking at causality and change, and trait profiles can indicate whether emergent communities are functionally different from historic communities. To this end, traits can be divided into those that determine an organism’s sensitivity and response to environmental factors, and those that relate to its effect on the environment4,17. When combined, the two categories of traits can be used to detect, identify and monitor the current state of ecosystems, and to anticipate the outcomes of change8,10,17,18,19.An environment described through traits: The urban bio-physical environment includes hydrology and soils, as well as biotic elements (flora and fauna), and understanding the relationships among those components is necessary to measure and anticipate the profound effects of urbanisation. Currently, knowledge of plant traits is most developed4,20, although there is work emerging on traits for animals or other taxonomic groups8,21 as well as for soil and geodiversity22. Animal studies so far tend to focus on habitat modelling for birds, insects, invertebrates and a few on mammals (e.g., see refs. 3,8,16,23). Many studies have looked at the impact of different community assemblages on ecological functions through effect traits and, in particular, how altered or dynamically changing communities will affect ecosystem process through changes in representation of effect traits (but e.g., see ref. 23). However, the link between traits and ecosystem functions has largely been inferred (ibid.), and is, according to Cadotte et al.24, rudimentary (see also25 and26). As we indicated with our definition of traits (Box 1), we see a value in including soil properties as traits and not to leave them as “environmental filters”, as this may offer a more dynamic way of understanding one of the major urban processes of change—soil sealing and compaction—and thus help guide urban development.Traits at different levels and scales: Traits at the species level are by far the best known and most explored, but there are also studies that use traits from other ecological levels—gene, community, ecosystem and landscape—as indicators for tracking response to stress27 and calculating functional “performance”. A common approach to scale is to aggregate species level information. For example, the average values of aggregations of plant species traits at the ecosystem level provide a basis for calculating overall sensitivity to pressures28. This in turn, and drawing on different sets of traits, allows for estimations of changes in ecosystem function (e.g., see ref. 29). However, there are other characteristics that could also be understood as traits. At the landscape level the mosaic of ecosystems and the location and combination of patches are used to assess flows and exchange across larger areas (e.g., see ref. 30). A good example is a city in a river valley, where water flows and exact location within the drainage basin affect urban green spaces and their aggregated matter production, CO2 absorption or carbon sub-section31. Aggregate, or higher-level traits, such as structural composition and functional diversity of vegetation, matter flows, or species migration, are the most common traits analysed through remote sensing in order to track trends25. More work needs to be done to explore relevant traits at different levels of organisation to match the scale and nature of disturbances and the spatial and temporal scale at which different functions are most relevant. Being explicit about scale, and ensuring traits at different levels are nested, allows for tracking of processes across scales.Individual traits, trait combinations, and interlinked suites of traits: A key promise of traits is to provide mechanistic explanations of observed structure, patterns and functionality, which is usually demonstrated through statistical correlations. Further developing suites of response and effect traits could provide valuable input and indicators for assessment and monitoring frameworks. For example, traits could inform DPSIR (drivers, pressures, state, impact, and response) models by anticipating or measuring response to a pressure and the direct and indirect impact this response could have. At a more fundamental level, traits explain whether impacts may be causing a change in the functional state of the system. Interlinked traits, from those determining sensitivity, to those mediating response elicited by sensitivity, could improve mechanistic understanding by supporting the development of stepwise response-effect pathways17. For example, land conversion—like the soil sealing and compaction typical in cities—fundamentally alters soil properties, which in turn affects vegetation. Soil properties influence the growth and composition of plant communities. This translates into trait-mediated effects like reduction of total leaf area, which leads to cascading effects of early leaf senescence and limitation of stomatal transpiration. This reduces water exchange capacity, which in turn is key for mediating air cooling or shading and other functions/services plants may offer to humans.For this first dimension, trait databases, classical field inventories, and experiments, remote sensing data, and GIS-based information are crucial15,32. We see valuable developments from the past two decades of research towards achieving a traits response-effect library in both the ecology and remote sensing communities33,34, even if recent advances from remote sensing studies still rarely find entrance into urban planners’ work and policy decision-making35. In particular, the development in the technical dimensions of detecting traits and trait variation20,34, and tracking these over time, has recently rapidly developed. The progress in application of high-resolution hyperspectral data, light detection, and ranging (LiDAR) or the possibility of mounting the recently developed sensors on unmanned aerial vehicles (UAVs) equip the researchers with addditional tools that can not only expand the range of measurable traits but also allow easy access to data. This provides a powerful support for urban planning and, ultimately, urban governance. Moreover, applications for tablets or smartphones offer alternative ways to directly involve citizens in ecosystem monitoring and further develop citizen science (e.g., see refs. 22,36).The second dimension: traits as an interdisciplinary bridgeThe literature explicitly using the term traits tends to focus on soil, geodiversity, plant, and community trait profiles as an outcome of social-ecological selection through environmental conditions, species interactions, human preferences, management regimes etc. (e.g., see refs. 4,37). This approach has started to address not just how people filter traits (e.g., see ref. 38), but the reason(s) behind either individual or group decisions that lead to filtering (e.g., see refs. 39,40). Here, we propose that the environment, described through traits, could be considered a boundary object (Box 1), allowing for a multiplicity of views, disciplinary connections, engagements, and perceptions, and that speaks to the complexity of social-ecological systems. This will expand the range of functions used to describe a system, and the types of traits required to capture them.Ecological functions relative to ecosystem services: The plant and animal traits that people respond to may not be the same ones that mediate responses to environmental change. For example, seed mass and specific leaf area are important plant functional traits41 but are less likely to influence people’s preferences for urban vegetation (e.g., see ref. 42). Indeed, some esthetic traits promoted by human decision-making and management, such as selection for leaf variation and predominantly deciduous plants, may also lead to the predominance of woody plants that are strongly affected by water stress, fungal attack or insect infestation or trimmed canopies, and thus promote reduced fitness of individual organisms and communities43. On the other hand, a successful reproductive strategy such as the emission of high quantities of pollen might limit the suitability to human-dominated environments (including cities) due to allergenic potential44. Do we need more, or different traits to link ecosystem dynamics more strongly to the lived reality of people? Are traits too simplistic proxies, or perhaps too specific features, to express and understand people–nature interactions? Introducing humans and human appraisal into our trait framework encourages a broader definition of what might be relevant traits. Traits used in this way provide a specific link to interactions and feedback mechanisms between human wellbeing and functional ecology (and respective proxies that serve multiple relational (feedback) purposes).Traits as relational features: Trait lists already include features which are easy to understand and readily detected by human sensory organs, and thus find traction in society or connect to existing ethno-biological narrations39. Traits such as flower colour, leaf shape, and canopy density, which may not necessarily be considered central functional traits, are important drivers of people’s preferences37,39,45,46. Both size and colour of the flowers are plant traits affecting people’s perception47 and can thus be an important factor for gaining societal approval for more urban greenery48. Seasonality is another relevant trait; for example, an extended flowering season49. At the same time, there is a growing interest in flowers and blooming meadows among gardeners worldwide also to support insects in urban landscapes to counteract global biodiversity decline37,39.In this vein, we argue that traits are a formative force influencing human wellbeing and world views, giving shape to ecological systems and linked human affordances (through, e.g., shade and sensory stimuli), and social systems by shaping the context of human activities and experiences. For example, we know that people recognize and value a wide range of plant traits, and that this has even been identified as a useful way to speak about the state of nature and large scale change50. There is evidently a role for traits and trait composition as language for more “functional” ecological literacy36,50. This position as a boundary object needs to be further explored and linked to the responses of social-ecological urban systems, which are subject to a multitude of pressures, including climate change and soil sealing.Traits as boundary objects and connectors between knowledge systems: What is needed to better position and connect the concept of traits to multiple different literatures and disciplines and enable traits to be used as a useful boundary object? Many disciplines outside the ecological and environmental sciences have an interest in understanding ecosystem function and biodiversity, and how people relate to these ideas. Traits, and deeper meanings of some traits, can be found within environmental psychology, ethno-botany/zoology and environmental anthropology. Trait-based approaches may also be well suited to engage with other ways of knowing, such as traditional ecological knowledge and religious systems. This disciplinary and trans-disciplinary knowledge is needed if traits are to connect social-ecological attributes to diverse human values and wellbeing dimensions, and to ensure we do not produce trivial and culturally biased conclusions51,52. Based on the diverse use and potential meanings of the word “traits”, we argue that a traits framework, and traits-focused interdisciplinary discussions and projects, could support a dual ontological stance where some connections are more universal, while others are inherently interpretational or simply individual. Hence, this may help to effectively connect the social and cultural dimensions of traits to a deep ecological understanding of change and its multiple consequences. This would be an important development that allows for critical engagement with concepts like tipping points and system states and what they actually mean in a complex social–ecological urban system.The third dimension: traits for decision supportThe major purpose of the traits concept, as we present it here, is to develop an ontologically inclusive traits framework capable of addressing both the resilience of ecological functions and the experiential and relational aspects of human interactions with nature. On the applied side, this would be relevant to a wide range of decision-making processes, not least urban planning. Clearly visible and easy-to-map traits are well-suited as indicators to describe the state of urban landscapes relevant for biodiversity and society alike. To this end, there are still many questions that need answers. For example, how can the understanding of trait profiles help improve species selection in times of climate change, to inform management priorities and strengthen cross-community stewardship, especially where the diversity of response traits may be low? And which traits are incompatible and how are they best kept separate, a question particularly relevant in the light of zoonosis like the COVID-19 pandemic in 2020? And finally, what traits could best serve as reasonable proxies or indicators to provide either cues or early signals of species responses to (fundamental) change in urban environments?Supporting holistic decisions: Already now we see increasing use of traits in modelling and decision support tools like CiTree and iTree53,54. As cities strive to adapt to climate change by, for example, revising tree species selection (e.g., see ref. 55), an improved understanding of the relationship between detectable functional traits and the provision of ecosystem services can help avoid maladaptation56. For example, replacing shade trees with fine-foliaged trees may improve adaptation to future climates but would not provide the same levels of climate mitigation57. From a decision-making point of view, key traits are those determining the response of ecosystems to human-induced pressures such as air pollution, soil sealing, or urban heat islands, as well as those mediating the effects of these changes on ecosystem services and related benefits as perceived by people8,58.A traits framework that uses our social-ecological definition of traits might support informed decisions on trade-offs. For example, invasive or non-native plants are often seen as ecologically problematic, but certain traits such as high leaf coverage or flower colour and shape make them socially desirable48. Traits connected to more social-ecological dimensions will allow for a more holistic assessment of options and the potential trade-off implications of different choices. While decisions are often grounded, implicitly or explicitly, in considerations of multiple traits (e.g., see ref. 53), we need to ensure that traits considered in the plant selection include both traits related to broad and diverse preferences and desires for ecosystem services and traits, that ensure a resilient response to drivers of change that may impact their ability to provide these services (see, e.g., the scoring system for urban vegetation species proposed by Tiwary et al.59).Urban planning informed by an expanded traits framework and spatial-temporal patterns of trait profiles has the promise to be adaptive in the best sense and thus, resilient. More city and regional comparisons are needed to make target setting and threshold discussions grounded and allow for global discussion. This requires a targeted effort at broader inclusion of cases and trait data from different climates, biomes, multiple ecological levels but also cultures, and would move traits studies towards a truly transdisciplinary venture with real impact on how we plan and manage our cities.Feasible and easy to use: Indicator traits need to be robust, easy to measure and low-cost to assess, and have a causal link to relevant social-ecological processes and patterns (such as ecosystem services for recreation, cooling or food4,60). The potential use in planning and decision-making at multiple levels again point to the need to discuss the scales and levels for traits studies to make sure trait levels are nested and logically commensurable. Higher-level, larger-scale properties such as landscape morphology and water availability, the profile of pest communities or potential invasions can be further informed by the development of more detailed traits frameworks. This makes traits frameworks highly relevant also from an economic, social and health perspective, especially in intensely managed environments like cities, where combinations of multiple stressors and external factors create small scale heterogeneity and fast temporal change in pressures61,62.Trait selection can play that important role for assisting in the planning and design and then evaluation of the functionality of high-biodiversity green spaces63, and for trait-informed assessment of “performance”, e.g., of ecologically protected areas. A relevant example to this point is the ongoing debate about how to evaluate ex-ante, and then monitor, the implementation of nature-based solutions62,64, which remains a challenge65. Could this be done using traits instead of commonly used area-based indicators? Could traits become the basis to design and assess the impacts of offsets and compensation measures, thus increasing their efficacy? From this perspective, we see in a traits framework the potential to support a shift towards more flexible and effective planning approaches, more suitable to address today’s urban challenges and to promote greater well-being, sustainability and resilience of present and future cities.Conclusion and looking aheadThrough their direct relation to ecosystem services such as cooling and fresh air, easy-to-understand traits can be an entry-point for nature awareness and, subsequently, ecological knowledge in decision-making both at the citizen and the societal level66. However, to make traits successful indicators of global, regional, or local environmental changes, it is vital that urban society is understood as diverse across characteristics such as cultural background, physical mobility, gender, age, degree of formal or informal education, access to information and communication, purchasing power, and political influence67. All these factors affect the needs, preferences, and values of individuals and groups, and the way each interpret human-nature relationships. Only by taking these factors into account, planning for spatial-temporal diversity in traits across an urban landscape will create more inclusive urban systems that foster multiple benefits for both people and biodiversity68.The expansion and implementation of a traits-based approach for urban systems is impeded by availability of traits data. For example, trait databases are usually a primary data source in studies on urban ecology, however, these data have mainly been collected in natural areas or controlled environments such as laboratories, where organisms may display different trait values than those in urban environments. Studies have also been concentrated in the global north, and there are major challenges with potentially transferring and adapting thinking mostly developed in the Global North to rapidly urbanising areas in Africa, Asia and South America.To enable a social-ecological traits framework for interdisciplinary discussion and for guiding urban planning and decision making, we suggest a three-pronged approach for building a social-ecological understanding of trait mediated interactions and their implications, and make this understanding useful to practice (Table 1). Large-scale monitoring needs to be coupled with in-depth understanding of response mechanisms and their impact on ecosystem functions as well as services, and a deeper connection between traits and human perception as well as sense-making of the world we live in. Application to human perception and sense-making requires more data, theory and empirical work, and especially the way people relate to traits will likely vary considerably across cities and contexts across the globe. All branches of investigation need to be embedded in an interdisciplinary discussion about the role that traits play for social-ecological interactions and mutual exchange. Drawing on this broad evidence base, synthesized knowledge will offer a more comprehensive support for urban decision making, not least in anticipation of future change.Table 1 Research agenda.Full size table More