Ecology

Bromley, R. G. & Gale, A. S. The lithostratigraphy of the English Chalk Rock. Cretac. Res. 3, 273–306 (1982).Article 

Google Scholar 
Scholle, P. A., Arthur, M. A. & Ekdale, A. A. Pelagic environment. In Carbonate Depositional Environments (eds Scholle, P. A. et al.) 619–691 (Am. Ass. Petrol. Geol. Mem. 33, 1983).Chapter 

Google Scholar 
Gealy, E. L., Winterer, E. L. & Moberly, R. Methods, conventions, and general observations. Initial Rep. Deep Sea Drill. Proj. 7, 9–26 (1971).
Google Scholar 
Kroenke, L. W. et al. Ocean Drilling Program. Proc. ODP, Init. Repts. 130, College Station (1991).Dunham, R. L. Classification of carbonate rocks according to depositional texture. Mem. Am. Assoc. Petrol. Geol. 1, 108–121 (1962).
Google Scholar 
Quine, M. & Bosence, D. Stratal geometries, facies and sea-floor erosion in Upper Cretaceous chalk, Normandy, France. Sedimentology 38, 1113–1152 (1991).ADS 
Article 

Google Scholar 
Røgen, B., Gommesen, L. & Fabricius, I. L. Grain size distributions of Chalk from Image analysis of electron micrographs. Comput. Geosci. 27, 1071–1080 (2001).ADS 
Article 

Google Scholar 
Saïag, J. et al. Classifying chalk microtextures: Sedimentary versus diagenetic origin (Cenomanian–Santonian, Paris Basin, France). Sedimentology 66, 2976–3007 (2019).Article 
CAS 

Google Scholar 
Scholle, P. A. Chalk diagenesis and its relation to petroleum exploration: Oil from chalks, a modern miracle?. Bull. Am. Assoc. Petrol. Geol. 61, 982–1009 (1977).CAS 

Google Scholar 
Tagliavento, M., John, C. M., Anderskouv, K. & Stemmerik, L. Towards a new understanding of the genesis of chalk: Diagenetic origin of micarbs confirmed by clumped isotope analysis. Sedimentology 68, 513–530 (2021).CAS 
Article 

Google Scholar 
Bramlette, M. N. Significance of coccolithophorids in calcium-carbonate deposition. Bull. Geol. Soc. Am. 69, 121–126 (1958).Article 

Google Scholar 
Hattin, D. E. & Darko, D. A. Technique for determining coccolith abundance in shaly chalk of Greenhorn Limestone (Upper Cretaceous) of Kansas. Kansas Geol. Surv. Bull. 202, 1–11 (1971).
Google Scholar 
Houghton, S. D. Calcareous nannofossils. In Calcareous algae and Stromatolites (ed. Riding, R.) 217–266 (Springer, 1991).Chapter 

Google Scholar 
Bown, P. R., Lees, J. A. & Young, J. R. Calcareous nannoplankton evolution and diversity through time. In Coccolithophores—From Molecular Processes to Global Impact (eds Thierstein, H. R. & Young, J. R.) 481–508 (Springer, 2004).
Google Scholar 
Roth, P. H. Mesozoic paleoceanography of the North Atlantic and Tethys Oceans. In North Atlantic Paleoceanography (eds Summerhayes, C. P. & Shackleton, N. J.) 299–320 (Geological Society Special Publications, 1986).
Google Scholar 
Baumann, K.-H., Andruleit, H., Böckel, B., Geisen, M. & Kinkel, H. The significance of extant coccolithophores as indicators of ocean water masses, surface water temperature, and paleoproductivity: A review. Paläontol. Z. 79, 93–112 (2005).Article 

Google Scholar 
Miller, K. G. et al. The phanerozoic record of global sea-level change. Science 310, 1293–1298 (2005).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Ando, A., Huber, B. T., MacLeod, K. G. & Watkins, D. K. Early Cenomanian “hot greenhouse” revealed by oxygen isotope record of exceptionally well-preserved foraminifera from Tanzania. Paleoceanography 30, 1556–1572 (2015).ADS 
Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Comparative ichnology of shelf-sea and deep-sea chalk. J. Paleontol. 58, 322–332 (1984).
Google Scholar 
Savrda, C. E. Chalk and related deep-marine carbonates. In Trace Fossils as Indicators of Sedimentary Environments (eds Knaust, D. & Bromley, R. G.) 777–806 (Elsevier, 2012).Chapter 

Google Scholar 
Savrda, C. E., Foster, C. & Fluegeman, R. A unique Lower Paleocene shelf-sea chalk in the eastern U.S. Gulf coastal plain (Clayton Formation, western Alabama): Implications for depositional environment, sea-level dynamics and paleogeography. Palaeogeogr. Palaeoclimatol. Palaeoecol. 538, 109439 (2020).Article 

Google Scholar 
Erba, E., Watkins, D. & Mutterlose, J. Campanian dwarf calcareous nannofossils from Wodejebato Guyot. In Proc. Ocean Drill. Program Sci. Results (eds Haggerty, J. A. et al.) 141–155 (Ocean Drilling Program, 1995).
Google Scholar 
Hancock, J. M. The petrology of chalk. Proc. Geol. Assoc. 86, 499–535 (1975).Article 

Google Scholar 
Stanley, S. M. & Hardie, L. A. Secular oscillations in the carbonate mineralogy of reef-building and sediment-producing organisms driven by tectonically forced shifts in seawater chemistry. Palaeogeogr. Palaeoclimatol. Palaeoecol. 144, 3–19 (1998).Article 

Google Scholar 
Stanley, S. M., Ries, J. B. & Hardie, L. A. Seawater chemistry, coccolithophore population growth, and the origin of Cretaceous chalk. Geology 33, 593–596 (2005).ADS 
CAS 
Article 

Google Scholar 
Pemberton, S. G. et al. Ichnology and sedimentology of shallow to marginal marine systems: Ben Nevis and Avalon Reservoirs, Jeanne d’Arc Basin. Geol. Assoc. Can. Short Course Notes 15, 1–343 (2001).
Google Scholar 
Buatois, L. A. & Mángano, M. G. Ichnology: Organism–Substrate Interactions in Space and Time (Cambridge Press University, 2011).Book 

Google Scholar 
Frey, R. W. & Bromley, R. G. Ichnology of American chalks: The Selma Group (Upper Cretaceous), western Alabama. Can. J. Earth Sci. 22, 801–828 (1985).ADS 
Article 

Google Scholar 
Savrda, C. E. & Bottjer, D. Trace-fossil model for reconstructing oxygenation histories of ancient marine bottom waters: Application to Upper Cretaceous Niobrara Formation, Colorado. Palaeogeogr. Palaeoclimatol. Palaeoecol. 74, 49–74 (1989).Article 

Google Scholar 
Kennedy, W. J. Trace fossils in carbonate rocks. In The Study of Trace Fossils (ed. Frey, R. W.) 377–398 (Springer, 1975).Chapter 

Google Scholar 
Loucks, R. G., Gates, B. G. & Zahm, C. K. Depositional systems, lithofacies, nanopore to micropore matrix network, and reservoir quality of the Upper Cretaceous (Cenomanian) Buda Limestone in Dimmit County, southwestern Texas. Gulf Coast Assoc. Geol. Soc. 8, 281–300 (2019).
Google Scholar 
Valencia, F. L. et al. Depositional environments and controls on the stratigraphic architecture of the Cenomanian Buda Limestone in west Texas, U.S.A. Mar. Petrol. Geol. 133, 105275 (2021).Article 

Google Scholar 
Valencia, F. L., Laya, J. C., Buatois, L. A., Mángano, M. G. & Valencia, G. L. Sedimentology and stratigraphy of the Cenomanian Buda Limestone in central Texas, U.S.A.: Implications on regional and global depositional controls. Cretac. Res. 137, 105231 (2022).Article 

Google Scholar 
Martin, K. G. Stratigraphy of the Buda Limestone, south-central Texas. In Comanchean (Lower Cretaceous) Stratigraphy and Paleontology of Texas (ed. Hendricks, L.) 287–299 (Permian Basin Section SEPM 67 (8), 1967).
Google Scholar 
Mallon, A. J. & Swarbrick, R. E. Diagenetic characteristics of low permeability, non-reservoir chalks from the Central North Sea. Mar. Petrol. Geol. 25, 1097–1108 (2008).CAS 
Article 

Google Scholar 
Brasher, J. E. & Vagle, K. R. Influence of lithofacies and diagenesis on Norwegian North Sea chalk reservoirs. Am. Assoc. Petrol. Geol. Bull. 80, 746–769 (1996).CAS 

Google Scholar 
Hentz, T. F. & Ruppel, S. C. Regional stratigraphic and rock characteristics of eagle ford shale in its play area: Maverick Basin to East Texas Basin. Am. Ass. Petrol. Geol. Search and Discovery 10325 (2011).Robinson, W. C. Petrography and depositional environments of the Buda Limestone, northern Coahuila, Mexico. MS Thesis. The University of Texas, 156 (1982).Reaser, D. F. & Robinson, W. C. Cretaceous Buda Limestone in west Texas and northern Mexico. In Cretaceous Stratigraphy and Paleoecology, Texas and Mexico (ed. Scott, R. W.) 337–373 (Perkins Memorial volume, GCSSEPM Foundation, Special Publications in Geology 1, 2003).
Google Scholar 
Young, K. P. Cretaceous paleogeography: Implications of endemic ammonite faunas. Geol. Circ. (University of Texas at Austin, Bureau of Economic Geology) 72, 1–13 (1972).
Google Scholar 
Buatois, L. A. & Mángano, M. G. Ichnodiversity and ichnodisparity: Significance and caveats. Lethaia 46, 281–292 (2013).Article 

Google Scholar 
Buatois, L. A., Wisshak, M., Wilson, M. A. & Mángano, M. G. Categories of architectural designs in trace fossils: A measure of ichnodisparity. Earth Sci. Rev. 164, 102–181 (2017).ADS 
CAS 
Article 

Google Scholar 
Swinbanks, D. D. & Luternauer, J. L. Burrow distribution of thalassinidean shrimp on a Fraser Delta tidal flat, British Columbia. J. Paleontol. 61, 315–333 (1987).Article 

Google Scholar 
Carmona, N. B., Buatois, L. A. & Mángano, M. G. The trace fossil record of burrowing decapod crustaceans: Evaluating evolutionary radiations and behavioural convergence. In Trace Fossils in Evolutionary Palaeoecology (eds Webby, B. D. et al.) 141–153 (Wiley, 2004).
Google Scholar 
Baucon, A. et al. Ethology of the trace fossil Chondrites: Form, function and environment. Earth Sci. Rev. 202, 102989 (2020).CAS 
Article 

Google Scholar 
Pemberton, S. G. & Frey, R. W. Trace fossil nomenclature and the Planolites–Palaeophycus dilemma. J. Paleontol. 56, 843–881 (1982).
Google Scholar 
Rodríguez-Tovar, F. J. & Pérez-Valera, F. Trace fossil Rhizocorallium from the Middle Triassic of the Betic Cordillera, Southern Spain: Characterization and environmental implications. Palaios 23, 78–86 (2008).ADS 
Article 

Google Scholar 
Bown, T. M. & Kraus, M. J. Ichnofossils of the alluvial Willwood Formation (lower Eocene), Bighorn Basin, northwest Wyoming, USA. Palaeogeogr. Palaeoclimatol. Palaeoecol 43, 95–128 (1983).Article 

Google Scholar 
Uchman, A. Taxonomy and palaeoecology of flysch trace fossils: The Marnoso-arenacea Formation and associated facies (Miocene, Northern Apennines, Italy). Beringeria 15, 3–115 (1995).
Google Scholar 
Demírcan, H. & Uchman, A. The miniature trace fossil Bichordites kuzunensis isp. Nov., from early Oligocene prodelta sediments of the Mezardere Formation, Gökçeada Island, NW Turkey. Acta Geol. Pol. 62, 205–215 (2012).
Google Scholar 
Plaziat, J.-C. & Mahmoudi, M. Trace fossils attributed to burrowing echinoids: A revision including new ichnogenus and ichnospecies. Geobios 21, 209–233 (1988).Article 

Google Scholar 
Chamberlain, C. K. Morphology and ethology of trace fossils from the Ouachita Mountains, southeast Oklahoma. J. Paleontol. 45, 212–246 (1971).
Google Scholar 
Farrow, G. E. Bathymetric zonation of Jurassic trace fossils from the coast of Yorkshire, England. Palaeogeogr. Palaeoclimatol. Palaeoecol. 2, 103–151 (1966).Article 

Google Scholar 
Mángano, M. G., Buatois, L. A., West, R. R. & Maples, C. G. Contrasting behavioral and feeding strategies recorded by tidal-flat bivalve trace fossils from the upper carboniferous of eastern Kansas. Palaios 13, 335–351 (1998).ADS 
Article 

Google Scholar 
Pemberton, S. G., Frey, R. W. & Bromley, R. G. The ichnotaxonomy of Conostichus and other plug-shaped ichnofossils. Can. J. Earth Sci. 25, 866–892 (1988).ADS 
Article 

Google Scholar 
Nara, M. Rosselia socialis: A dwelling structure of a probable terebellid polychaete. Lethaia 28, 171–178 (1995).Article 

Google Scholar 
Wilson, M. A., Curran, H. A. & White, B. Paleontological evidence of a brief global sea-level event during the last interglacial. Lethaia 31, 241–250 (1998).Article 

Google Scholar 
Santos, A., Mayoral, E., Marques da Silva, C., Cachão, M. & Kullberg, J. C. Trypanites ichnofacies: Palaeoenvironmental and tectonic implications. A case study from the Miocene disconformity at Foz da Fonte (Lower Tagus Basin, Portugal). Palaeogeogr. Palaeoclimatol. Palaeoecol. 292, 35–43 (2010).Article 

Google Scholar 
Wilson, J. L. Carbonate Facies in Geological History (Springer, 1975).Book 

Google Scholar 
Tucker, M. E. & Wright, V. P. Carbonate Sedimentology (Blackwell Science, 1990).Book 

Google Scholar 
MacEachern, J. A. & Gingras, M. K. Recognition of brackish-water trace fossil assemblages in the Cretaceous western interior seaway of Alberta. In Sediment-Organism Interactions: A Multifaceted Ichnology (eds Bromley, R. G. et al.) 149–194 (Society for Sedimentary Geology Special Publication, 2007).
Google Scholar 
MacEachern, J. A., Zaitlin, B. A. & Pemberton, S. G. High-resolution sequence stratigraphy of early transgressive deposits, Viking Formation, Joffre Field, Alberta, Canada. Bull. Am. Assoc. Petrol. Geol. 82, 729–756 (1998).
Google Scholar 
Buatois, L. A., Netto, R. G. & Mángano, M. G. Ichnology of Permian marginal-marine to shallow-marine coal-bearing successions: Rio Bonito and Palermo formations, Parana Basin, Brazil. In Applied Ichnology (eds MacEachern, J. A. et al.) 167–177 (Society for Sedimentary Geology Short Course Notes, 2007).
Google Scholar 
Buatois, L. A. et al. Colonization of brackish-water systems through time: Evidence from the trace-fossil record. Palaios 20, 321–347 (2005).ADS 
Article 

Google Scholar 
Pemberton, S. G. & Wightman, D. M. Ichnological characteristics of brackish water deposits. In Applications of Ichnology to Petroleum Exploration: A Core Work-shop (ed. Pemberton, S. G.) 141–167 (Society of Economic Paleontologists and Mineralogists Core Workshop, 1992).Chapter 

Google Scholar 
Anderson, B. G. & Droser, M. L. Ichnofabrics and geometric configurations of Ophiomorpha within a sequence stratigraphic framework: An example from the Upper Cretaceous US western interior. Sedimentology 45, 379–396 (1998).ADS 
Article 

Google Scholar 
Buatois, L. A., Mángano, M. G. & Pattison, S. A. J. Ichnology of prodeltaic hyperpycnite–turbidite channel complexes and lobes from the Upper Cretaceous Prairie Canyon Member of the Mancos Shale, Book Cliffs, Utah, USA. Sedimentology 66, 1825–1860 (2019).Article 

Google Scholar 
Bhattacharya, J. P. & MacEachern, J. A. Hyperpycnal rivers and prodeltaic shelves in the Cretaceous seaway of North America. J. Sediment. Res. 79, 184–209 (2009).ADS 
Article 

Google Scholar 
Savrda, C. E. Ichnosedimentologic evidence for a noncatastrophic origin of Cretaceous-Tertiary boundary sand in Alabama. Geology 21, 1075–1078 (1993).ADS 
Article 

Google Scholar 
Schlager, W. Accommodation and supply-a dual control on stratigraphic sequences. Sediment. Geol. 86, 111–136 (1993).ADS 
Article 

Google Scholar 
Strasser, A. & Samankassou, E. Carbonate sedimentation rates today and in the past: Holocene of Florida Bay, Bahamas, and Bermuda vs. Upper Jurassic and Lower Cretaceous of the Jura Mountains (Switzerland and France). Geol. Croat. 56, 1–18 (2003).Article 

Google Scholar 
Moyano-Paz, D., Richiano, S., Varela, A. N., Gómez-Dacal, A. R. & Poire, D. G. Ichnological signatures from wave- and fluvial-dominated deltas: The La Anita Fromation, Upper Cretaceous, Austral-Magallanes Basin, Patagonia. Mar. Pet. Geol. 114, 104168 (2020).CAS 
Article 

Google Scholar 
De Gibert, J. M. & Ekdale, A. A. Trace fossil assemblages reflecting stressed environments in the Middle Jurassic Carmel Seaway of Central Utah. J. Paleontol. 73, 711–720 (1999).Article 

Google Scholar 
Gingras, M. K., MacEachern, J. A. & Dashtgard, S. E. Process ichnology and the elucidation of physico-chemical stress. Sediment. Geol. 237, 115–134 (2011).ADS 
CAS 
Article 

Google Scholar 
Smith, C. R., Levin, L. A., Hoover, D. J., McMurty, G. & Gage, J. D. Variations in bioturbation across the oxygen minimum zone in the northwest Arabian Sea. Deep-Sea Res. II 47, 227–257 (2000).ADS 
CAS 
Article 

Google Scholar 
Wignall, P. B., Newton, R. & Brookfield, M. E. Pyrite framboid evidence for oxygen-poor deposition during the Permian-Triassic crisis in Kashmir. Palaeogeogr. Palaeoclimatol. Palaeoecol. 216, 183–188 (2005).Article 

Google Scholar 
Kennedy, W. J. Burrows and surface traces from the Lower Chalk of southern England. Bull. Br. Mus. Nat. Hist. Geol. 15, 127–167 (1967).
Google Scholar 
Kennedy, W. J. & Garrison, R. E. Morphology and genesis of nodular chalks and hardgrounds in the Upper Cretaceous of southern England. Sedimentology 22, 311–386 (1975).ADS 
CAS 
Article 

Google Scholar 
Bromley, R. G. Some observations on burrows of thalassinidean Crustacea in chalk hardgrounds. Geol. Soc. Lond. Q. J. 123, 157–182 (1967).Article 

Google Scholar 
Bromley, R. G. Trace fossils at omission surfaces. In The Study of Trace Fossils (ed. Frey, R. W.) 399–428 (Springer, 1975).Chapter 

Google Scholar 
Hart, M. B., Harries, P. J. & Cárdenas, A. L. The Cretaceous/Paleogene boundary events in the Gulf Coast: Comparisons between Alabama and Texas. Gulf Coast Assoc. Geol. Trans. 63, 235–255 (2013).
Google Scholar 
Al Balushi, S. A. K. & Macquaker, J. H. S. Sedimentological evidence for bottom-water oxygenation during deposition of the Natih-B Member intrashelf-basinal sediments: Upper Cretaceous carbonate source rock, Natih Formation, North Sultanate of Oman. GeoArabia 16, 47–84 (2011).Article 

Google Scholar 
Lasseur, E. et al. A relative water-depth model for the Normandy Chalk (Cenomanian–Middle Coniacian, Paris Basin, France) based on facies patterns of metre-scale cycles. Sediment. Geol. 213, 1–26 (2009).ADS 
Article 

Google Scholar 
Dawson, W. C. & Reaser, D. F. Rhizocorallium in the upper Austin Chalk, Ellis County, Texas. Texas J. of Sci. 23, 207–214 (1980).
Google Scholar 
Dawson, W. C. & Reaser, D. F. Ichnology and paleoenvironments of the middle and upper Austin Chalk (Upper Cretaceous), northeastern Texas. Trans. Am. Assoc. Pet. Geol. Southwest Sec. 1985, 47–67 (1985).
Google Scholar 
Dawson, W. C. & Reaser, D. F. Trace fossils and paleoenvironments of lower and middle Austin Chalk (Upper Cretaceous), north-central Texas. Trans. Gulf Coast Assoc. Geol. Soc. 40, 161–173 (1990).
Google Scholar 
Dawson, W. C. & Reaser, D. F. Ichnology and Paleosubstrates of Austin Chalk (Cretaceous) Outcrops: Southern Dallas and Ellis Counties, Texas. Am. Assoc. Pet. Geol. Search Discovery Article #91004 (1991).Fürsich, F. T., Kennedy, W. J. & Palmer, T. J. Trace fossils at a regional discontinuity surface: The Austin/Taylor (Upper Cretaceous) contact in central Texas. J. Paleontol. 55, 537–551 (1981).
Google Scholar 
Morgan, R. F. A new ichnospecies of Gyrolithes from the Austin Chalk, Upper Cretaceous, Texas, USA. Ichnos 26, 1–7 (2018).Article 

Google Scholar 
Cooper, J. R., Godet, A. & Pope, M. C. Tectonic and eustatic impact on depositional features in the upper Cretaceous Austin Chalk Group of south-central Texas, USA. Sediment. Geol. 401, 105632 (2020).Article 

Google Scholar 
Loucks, R. G. et al. Geologic characterization of the type cored section for the Upper Cretaceous Austin Chalk Group in southern Texas: A combination fractured and unconventional reservoir. Am. Assoc. Pet. Geol. Bull. 104, 2209–2245 (2020).
Google Scholar 
Loucks, R. G., Reed, R. M., Ko, L. T., Zahm, C. K. & Larson, T. E. Micropetrographic characterization of a siliciclastic-rich chalk; Upper Cretaceous Austin Chalk Group along the onshore northern Gulf of Mexico, USA. Sediment. Geol. 412, 105821 (2021).CAS 
Article 

Google Scholar 
Bottjer, D. J. Paleoecology, Ichnology, and Depositional Environments of Upper Cretaceous Chalks (Annona Formation; chalk Member of Saratoga Formation), Southwestern Arkansas. PhD Dissertation, Indiana University, 424 (1978).Bottjer, D. J. Ichnology and depositional environments of Upper Cretaceous chalks, southwestern Arkansas (Annona Formation; chalk member, Saratoga Formation). Am. Assoc. Pet. Geol. Bull. 63, 422 (1979).
Google Scholar 
Bottjer, D. J. Trace fossils and paleoenvironments of two Arkansas Upper Cretaceous discontinuity surfaces. J. Paleontol. 59, 282–298 (1985).
Google Scholar 
Bottjer, D. J. Campanian-Maastrichtian chalks of southwestern Arkansas: Petrology, paleoenvironments and comparison with other North American and European chalks. Cretac. Res. 7, 161–196 (1986).Article 

Google Scholar 
Bayet-Goll, A., Neto de Carvalho, C., Monaco, P. & Sharafi, M. Sequence stratigraphic and sedimentologic significance of biogenic structures from chalky limestones of the Turonian-Campanian Abderaz Formation, Kopet-Dagh, Iran. In Cretaceous Period: Biotic Diversity and Biogeography (eds Khosla, A. & Lucas, S. G.) 19–43 (New Mex. Mus. Nat. His. Sci. Bull. 71, 2016).
Google Scholar 
Locklair, R. E. & Savrda, C. E. Ichnology of rhythmically bedded Demopolis Chalk (Upper Cretaceous, Alabama): Implications for paleoenvironment, depositional cycle origins, and tracemaker behavior. Palaios 13, 423–438 (1998).ADS 
Article 

Google Scholar 
Locklair, R. E. & Savrda, C. E. Ichnofossil tiering analysis of a rhythmically bedded chalk-marl sequence in the Upper Cretaceous of Alabama. Lethaia 31, 311–322 (1998).Article 

Google Scholar 
Kennedy, W. J. Trace fossils in the chalk environment. In Trace Fossils (eds Crimes, T. P. & Harper, J. C.) 263–282 (Geological Journal Special Issue 3, 1970).
Google Scholar 
Mortimore, R. N. & Pomerol, B. Stratigraphy and eustatic implications of trace fossil events in the Upper Cretaceous Chalk of northern Europe. Palaios 6, 216–231 (1991).ADS 
Article 

Google Scholar 
Foster, C. B. III. Geology of the Moscow Landing Section, Tombigbee River, Western Alabama, with Focus on Ichnologic Aspects of the Lower Paleocene Clayton Formation. M.Sc. Dissertation, Auburn University, 88 (2019).Gabdullin, R. R. Rhythmicity of the Upper Cretaceous Deposits of the East European Craton, Northwestern Caucasus and Southwestern Crimea: Structure, Classification, Formation Models (Mosk. Gos. Univ., 2002).
Google Scholar 
Baraboshkin, E. Y. & Zibrov, I. A. Characteristics of the Middle Cenomanian Rhythmic Sequence from Mount Selbukhra in Southwest Crimea. Moscow Univ. Geol. Bull. 67, 176–184 (2012).Article 

Google Scholar 
Blinkenberg, K. H., Lauridsen, B. W., Knaust, D. & Stemmerik, L. New ichnofabrics of the Cenomanian-Danian Chalk Group. J. Sediment. Res. 90, 701–712 (2020).ADS 
Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Trace fossils and ichnofabric in the Kjolby Gaard Marl, uppermost Cretaceous, Denmark. Bull. Geol. Soc. Denmark 31, 107–119 (1983).Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Cretaceous chalk ichnofacies in northern Europe. Geobios 8, 201–204 (1984).Article 

Google Scholar 
Ekdale, A. A. & Bromley, R. G. Analysis of composite ichnofabrics; An example in Uppermost Cretaceous chalk of Denmark. Palaios 6, 232–249 (1991).ADS 
Article 

Google Scholar 
Surlyk, F. et al. The cyclic Rørdal Member—A new lithostratigraphic unit of chronostratigraphic and palaeoclimatic importance in the upper Maastrichtian of Denmark. Bull. Geol. Soc. Denmark 58, 89–98 (2010).Article 

Google Scholar 
Lauridsen, B. W., Surlyk, F. & Bromley, R. G. Trace fossils of a cyclic chalk marl succession; the upper Maastrichtian Rørdal Member, Denamrk. Cretac. Res. 32, 194–211 (2011).Article 

Google Scholar 
Frey, R. W. Trace fossils of Fort Hays Limestone Member of Niobrara Chalk (Upper Cretaceous), west-central Kansas. Univ. Kansas Paleontol. Contrib. 53, 52 (1970).
Google Scholar 
Hattin, D. E. Stratigraphy and depositional environment of Smoky Hill Chalk Member, Niobrara Chalk (Upper Cretaceous) of the type area western Kansas. Kansas Geol. Surv. Bull. 225, 1–108 (1982).
Google Scholar 
Savrda, C. E. Ichnocoenoses in the Niobrara Formation: Implications for benthic oxygenation histories. In Stratigraphy and Paleoenvironments of the Cretaceous Western Interior Seaway, USA (eds Dean, W. E. & Arthur, M. A.) 137–151 (SEPM Society for Sedimentary Geology 6, 1998).Chapter 

Google Scholar 
Hattin, D. E. Widespread, synchronously deposited, burrow-mottled limestone beds in Greenhorn Limestone (Upper Cretaceous) of Kansas and southeastern Colorado. Am. Assoc. Pet. Geol. Bull. 55, 412–431 (1971).
Google Scholar 
Hattin, D. E. Stratigraphy and depositional environment of Greenhorn Limestone (Upper Cretaceous) of Kansas. Kansas Geol. Surv. Bull. 209, 128 (1975).
Google Scholar 
Savrda, C. E. Ichnology of the Bridge Creek Limestone: Evidence for temporal and spatial variations in paleo-oxygenation in the Western Interior Seaway. In Stratigraphy and Paleoenvironments of the Cretaceous Western Interior Seaway, USA (eds Dean, W. E. & Arthur, M. A.) 127–136 (SEPM Society for Sedimentary Geology 6, 1998).Chapter 

Google Scholar 
Rasmussen, S. L. & Surlyk, F. Facies and ichnology of an Upper Cretaceous chalk contourite drift complex, eastern Denmark, and the validity of contourite facies models. J. Geol. Soc. Lond. 169, 435–447 (2012).Article 

Google Scholar 
Surlyk, F. et al. Upper Campanian-Maastrichtian holostratigraphy of the eastern Danish Basin. Cretac. Res. 46, 232–256 (2013).Article 

Google Scholar 
Boussaha, M., Thibault, N., Anderskouv, K., Moreau, J. & Stemmerik, L. Controls on upper Campanian-Maastrichtian chalk deposition in the eastern Danish Basin. Sedimentology 64, 1998–2030 (2017).Article 

Google Scholar 
Reolid, J. & Betzler, C. The ichnology of carbonate drifts. Sedimentology 66, 1427–1448 (2019).Article 

Google Scholar 
Nygaard, E. Bathichnus and Its Significance in the Trace Fossil Association of Upper Cretaceous Chalk, Mors, Denmark 107–113 (Danm. Geol. Unders. Årbog, 1983).
Google Scholar 
Scholle, P. A., Albrechtsen, T. & Tirsgaard, H. Formation and diagenesis of bedding cycles in uppermost Cretaceous chalks of the Dan Field, Danish North Sea. Sedimentology 45, 223–243 (1998).ADS 
CAS 
Article 

Google Scholar 
Damholt, T. & Surlyk, F. Laminated–bioturbated cycles in Maastrichtian chalk of the North Sea: Oxygenation fluctuations within the Milankovitch frequency band. Sedimentology 51, 1323–1342 (2004).ADS 
Article 

Google Scholar 
Anderskouv, K. & Surlyk, F. Upper Cretaceous chalk facies and depositional history recorded in the Mona-1 core, Mona Ridge, Danish North Sea. Geol. Surv. Denmark Greenland Bull. 25, 1–60 (2011).Article 

Google Scholar 
Maliva, R. G. & Dickson, J. A. D. Microfacies and diagenetic controls of porosity in Cretaceous/Tertiary chalks, Eldfisk Field, Norwegian North Sea. Am. Assoc. Pet. Geol. Bull. 76, 1825–1838 (1992).
Google Scholar 
Knaust, D., Dorador, J. & Rodríguez-Tovar, F. J. Burrowed matrix powering dual porosity systems—A case study from the Maastrichtian chalk of the Gullfaks Field Norwegian North Sea. Mar. Petrol. Geol. 113, 104158 (2020).Article 

Google Scholar 
Phillips, C. & McIlroy, D. Ichnofabrics and biologically mediated changes in clay mineral assemblages from a deep-water, fine-grained, calcareous sedimentary succession: An example from the Upper Cretaceous Wyandot Formation, offshore Nova Scotia. Bull. Can. Petrol. Geol. 58, 203–218 (2010).Article 

Google Scholar 
Rodríguez-Tovar, F. J. & Hernández-Molina, F. J. Ichnological analysis of contourites: Past, present and future. Earth-Sci. Rev. 182, 28–41 (2018).ADS 
Article 

Google Scholar 
Miguez-Salas, O. & Rodríguez-Tovar, F. J. Ichnofacies distribution in the Eocene-Early Miocene Petra Tou Romiou outcrop, Cyprus: Sea level dynamics and palaeoenvironmental implications in a contourite environment. Int. J. Earth Sci. 108, 2531–2544 (2019).CAS 
Article 

Google Scholar 
Nelson, C. S. Bioturbation in middle bathyal, Cenozoic nannofossil oozes and chalks, southwest Pacific. In Initial Reports of the Deep Sea Drilling Project 90 (eds Kennett, J. P., von der Borch, C. C. et al.) 1189–1200 (Washington U.S. Government Printing Office, 1986).
Google Scholar 
Fütterer, D. K. Bioturbation and trace fossils in deep sea sediments of the Walvis Ridge, southeastern Atlantic, Leg 74. In Initial Reports of the Deep Sea Drilling Project 74 (eds Moore, T. C., Rabinowitz, P. D. et al.) 543–555 (Government Printing Office, 1984).
Google Scholar 
Wetzel, A. Ichnofabrics in Eocene to Maestrichtian sediments from Deep Sea Drilling Project Site 605, off the New Jersey coast. In Initial Reports of the Deep Sea Drilling Project 93 (eds. Hinte, J. E., Wise Jr., S. W. et al.) 825–835 (1987).Droser, M. L. & Bottjer, D. J. Trace fossils and ichnofabrics in Leg 119 cores. In Proceedings of the Ocean Drilling Program, Scientific Results 119 (eds. Barron, J., Larsen, B. et al.) 635–641 (1991).Desai, B. G. Ichnofabric analysis of bathyal chalks: The Miocene Inglis Formation of the Andaman and Nicobar Islands, India. J. Palaeogeogr. 10, 1–15 (2021).Article 

Google Scholar 
Warme, J. E., Kennedy, W. J. & Scheidermann, N. Biogenic sedimentary structures (trace fossils) in Leg 15 cores. In Initial Reports of the Deep Sea Drilling Project 15 (eds. Edgar, N. T., Saunders, J. B. et al.) 813–831 (1973).Maurrasse, F. Sedimentary structures of Caribbean Leg 15 sediments. In Initial Reports of the Deep-Sea Drilling Project 15 (eds. Edgar, T. et al.) (1974).Erba, E. & Premoli-Silva, I. Orbitally driven cycles in trace-fossil distribution from the Piobbico core (late Albian, central Italy). In Orbital Forcing and Cyclic Sequences, IAS Spec. Publ. 19 (eds De Boer, P. L. & Smith, D. G.) 211–225 (Blackwell Scientific, 1994).
Google Scholar 
Chamberlain, C. K. Trace fossils in DSDP cores of the Pacific. J. Paleontol. 49, 1074–1096 (1975).
Google Scholar 
Ekdale, A. A. Trace fossils in Deep Sea Drilling Project Leg 58 cores. In Initial Reports of the Deep Sea Drilling Project 58 (eds. de Vries Klein, G., Kobyashi, K. et al.) 601–605 (1980).Ekdale, A. A. Geologic history of the abyssal benthos: Evidence from trace fossils in Deep-Sea Drilling Project cores. PhD Dissertation, Rice University, 154 (1974).Ekdale, A. A. Abyssal trace fossils in worldwide Deep Sea Drilling Project cores. In Trace Fossils 2 (eds. Crimes, T. P. & Harper, J. C.) 163–182 (Geol. J., Spec. Iss. 9, 1977).Ekdale, A. A. & Berger, W. H. Deep-sea ichnofacies: Modern organism traces on and in pelagic carbonates of the western equatorial Pacific. Palaeogeogr. Palaeoclimatol. Palaeoecol. 23, 263–278 (1978).Article 

Google Scholar 
Ekdale, A. A., Muller, L. N. & Novak, M. T. Quantitative ichnology of modern pelagic deposits in the abyssal Atlantic. Palaeogeogr. Palaeoclimatol. Palaeoecol. 45, 189–223 (1984).CAS 
Article 

Google Scholar 
Savrda, C. E. Limited ichnologic fidelity and temporal resolution in pelagic sediments: Paleoenvironmental and paleoecologic implications. Palaios 29, 210–217 (2014).ADS 
Article 

Google Scholar 
Bromley, R. G. & Ekdale, A. A. Composite ichnofabrics and tiering of burrows. Geol. Mag. 123, 59–65 (1986).ADS 
Article 

Google Scholar 
Griffin, J. N. et al. Spatial heterogeneity increases the importance of species richness for an ecosystem process. Oikos 118, 1335–1342 (2009).Article 

Google Scholar 
Valentine, J. W. Overview of marine biodiversity. In Marine Macroecology (eds Witman, J. D. & Roy, K.) 3–28 (University of Chicago Press, 2009).Chapter 

Google Scholar 
Schlacher, T. A. et al. Soft-sediment benthic community structure in a coral reef lagoon—The prominence of spatial heterogeneity and “spot endemism”. Mar. Ecol. Prog. Ser. 174, 159–174 (1998).ADS 
Article 

Google Scholar 
Hummel, H. et al. Geographic patterns of biodiversity in European coastal marine benthos. J. Mar. Biol. Assoc. U.K. 97, 507–523 (2017).Article 

Google Scholar 
Harborne, A. R., Mumby, P. J., Żychaluk, K., Hedley, J. D. & Blackwell, P. G. Modeling the beta diversity of coral reefs. Ecology 87, 2871–2881 (2006).PubMed 
Article 

Google Scholar 
Christia, C., Giordani, G. & Papastergiadou, E. Environmental variability and macrophyte assemblages in coastal lagoon types of Western Greece (Mediterranean Sea). Water 10, 151 (2018).Article 
CAS 

Google Scholar 
Dorador, J., Rodríguez-Tovar, F. J., IODP Expedition 339 Scientists. Digital image treatment applied to ichnological analysis of marine core sediments. Facies 60, 39–44 (2014).Article 

Google Scholar 
Dorador, J. & Rodríguez-Tovar, F. J. High-resolution image treatment in ichnological core analysis: Initial steps, advances and prospects. Earth-Sci. Rev. 177, 226–237 (2018).ADS 
Article 

Google Scholar 
Taylor, A. M. & Goldring, R. Description and analysis of bioturbation and ichnofabric. J. Geol. Soc. 150, 141–148 (1993).ADS 
Article 

Google Scholar 
Cao, Y. M., Curran, A. H. & Glumac, B. Testing the use of photoshop and imageJ for evaluating ichnofabrics. 2015 GSA Annual Meeting in Baltimore, Maryland, USA, Paper No. 128-11 (The Geol. Soc. of Am., 2015). More

Zabalza, A., Orcaray, L., Fernandez-Escalada, M., Zulet-Gonzalez, A. & Royuela, M. The pattern of shikimate pathway and phenylpropanoids after inhibition by glyphosate or quinate feeding in pea roots. Pestic Biochem. Physiol. 141, 96–102. https://doi.org/10.1016/j.pestbp.2016.12.005 (2017).CAS 
Article 
PubMed 

Google Scholar 
Amrhein, N., Deus, B., Gehrke, P. & Steinrucken, H. C. The site of the inhibition of the shikimate pathway by glyphosate: II. Interference of glyphosate with chorismate formation in vivo and in vitro. Plant Physiol. 66, 830–834. https://doi.org/10.1104/pp.66.5.830 (1980).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Landrigan, P. J. & Belpoggi, F. The need for independent research on the health effects of glyphosate-based herbicides. Environ. Health 17, 51. https://doi.org/10.1186/s12940-018-0392-z (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Tu, M. & Randall, J. Adjuvants. Tu, M. et al. Weed Control Methods Handbook the Nature Conservancy. 1–24. (TNC, 2003).Brausch, J. M. & Smith, P. N. Toxicity of three polyethoxylated tallowamine surfactant formulations to laboratory and field collected fairy shrimp, Thamnocephalus platyurus. Arch. Environ. Contam. Toxicol. 52, 217–221. https://doi.org/10.1007/s00244-006-0151-y (2007).CAS 
Article 
PubMed 

Google Scholar 
Brausch, J. M., Beall, B. & Smith, P. N. Acute and sub-lethal toxicity of three POEA surfactant formulations to Daphnia magna. Bull. Environ. Contam. Toxicol. 78, 510–514. https://doi.org/10.1007/s00128-007-9091-0 (2007).CAS 
Article 
PubMed 

Google Scholar 
Tsui, M. T. & Chu, L. M. Aquatic toxicity of glyphosate-based formulations: Comparison between different organisms and the effects of environmental factors. Chemosphere 52, 1189–1197. https://doi.org/10.1016/S0045-6535(03)00306-0 (2003).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Adam, A., Marzuki, A., Abdul Rahman, H. & Abdul Aziz, M. The oral and intratracheal toxicities of ROUNDUP and its components to rats. Vet. Hum. Toxicol. 39, 147–151 (1997).CAS 
PubMed 

Google Scholar 
Howe, C. M. et al. Toxicity of glyphosate-based pesticides to four North American frog species. Environ. Toxicol. Chem. 23, 1928–1938. https://doi.org/10.1897/03-71 (2004).CAS 
Article 
PubMed 

Google Scholar 
Mesnage, R., Benbrook, C. & Antoniou, M. N. Insight into the confusion over surfactant co-formulants in glyphosate-based herbicides. Food Chem. Toxicol. 128, 137–145. https://doi.org/10.1016/j.fct.2019.03.053 (2019).CAS 
Article 
PubMed 

Google Scholar 
Mesnage, R., Bernay, B. & Seralini, G. E. Ethoxylated adjuvants of glyphosate-based herbicides are active principles of human cell toxicity. Toxicology 313, 122–128. https://doi.org/10.1016/j.tox.2012.09.006 (2013).CAS 
Article 
PubMed 

Google Scholar 
Chlopecka, M., Mendel, M., Dziekan, N. & Karlik, W. The effect of glyphosate-based herbicide Roundup and its co-formulant, POEA, on the motoric activity of rat intestine—In vitro study. Environ. Toxicol. Pharmacol. 49, 156–162. https://doi.org/10.1016/j.etap.2016.12.010 (2017).CAS 
Article 
PubMed 

Google Scholar 
Authority, E. F. S. Request for the evaluation of the toxicological assessment of the co-formulant POE-tallowamine. EFSA J. 13, 4303 (2015).
Google Scholar 
Bolognesi, C. et al. Genotoxic activity of glyphosate and its technical formulation Roundup. J. Agric. Food Chem. 45, 1957–1962 (1997).CAS 
Article 

Google Scholar 
Hao, Y. et al. Roundup((R)) confers cytotoxicity through DNA damage and mitochondria-associated apoptosis induction. Environ. Pollut. 252, 917–923. https://doi.org/10.1016/j.envpol.2019.05.128 (2019).CAS 
Article 
PubMed 

Google Scholar 
Luo, L. et al. In vitro cytotoxicity assessment of roundup (glyphosate) in L-02 hepatocytes. J. Environ. Sci. Health B 52, 410–417. https://doi.org/10.1080/03601234.2017.1293449 (2017).CAS 
Article 
PubMed 

Google Scholar 
Young, F., Ho, D., Glynn, D. & Edwards, V. Endocrine disruption and cytotoxicity of glyphosate and roundup in human JAr cells in vitro. Synthesis 14, 17 (2015).
Google Scholar 
Weinhold, B. Mystery in a bottle: Will the EPA require public disclosure of inert pesticide ingredients?. Environ. Health Perspect. 118, A168-171. https://doi.org/10.1289/ehp.118-a168 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Richmond, M. E. Glyphosate: A review of its global use, environmental impact, and potential health effects on humans and other species. J. Environ. Stud. Sci. 8, 416–434 (2018).Article 

Google Scholar 
Cole, R. D., Anderson, G. L. & Williams, P. L. The nematode Caenorhabditis elegans as a model of organophosphate-induced mammalian neurotoxicity. Toxicol. Appl. Pharmacol. 194, 248–256. https://doi.org/10.1016/j.taap.2003.09.013 (2004).CAS 
Article 
PubMed 

Google Scholar 
Lai, C. H., Chou, C. Y., Ch’ang, L. Y., Liu, C. S. & Lin, W. Identification of novel human genes evolutionarily conserved in Caenorhabditis elegans by comparative proteomics. Genome Res. 10, 703–713. https://doi.org/10.1101/gr.10.5.703 (2000).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Negga, R. et al. Exposure to glyphosate- and/or Mn/Zn-ethylene-bis-dithiocarbamate-containing pesticides leads to degeneration of gamma-aminobutyric acid and dopamine neurons in Caenorhabditis elegans. Neurotox. Res. 21, 281–290. https://doi.org/10.1007/s12640-011-9274-7 (2012).CAS 
Article 
PubMed 

Google Scholar 
Negga, R. et al. Exposure to Mn/Zn ethylene-bis-dithiocarbamate and glyphosate pesticides leads to neurodegeneration in Caenorhabditis elegans. Neurotoxicology 32, 331–341. https://doi.org/10.1016/j.neuro.2011.02.002 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Schuske, K., Beg, A. A. & Jorgensen, E. M. The GABA nervous system in C. elegans. Trends Neurosci. 27, 407–414. https://doi.org/10.1016/j.tins.2004.05.005 (2004).CAS 
Article 
PubMed 

Google Scholar 
McIntire, S. L., Jorgensen, E. & Horvitz, H. R. Genes required for GABA function in Caenorhabditis elegans. Nature 364, 334–337. https://doi.org/10.1038/364334a0 (1993).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Eastman, C., Horvitz, H. R. & Jin, Y. Coordinated transcriptional regulation of the unc-25 glutamic acid decarboxylase and the unc-47 GABA vesicular transporter by the Caenorhabditis elegans UNC-30 homeodomain protein. J. Neurosci. 19, 6225–6234 (1999).CAS 
Article 

Google Scholar 
Bamber, B. A., Beg, A. A., Twyman, R. E. & Jorgensen, E. M. The Caenorhabditis elegans unc-49 locus encodes multiple subunits of a heteromultimeric GABA receptor. J. Neurosci. 19, 5348–5359 (1999).CAS 
Article 

Google Scholar 
Risley, M. G., Kelly, S. P., Jia, K., Grill, B. & Dawson-Scully, K. Modulating behavior in C. elegans using electroshock and antiepileptic drugs. PLoS ONE 11, e0163786. https://doi.org/10.1371/journal.pone.0163786 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pandey, R. et al. Baccoside A suppresses epileptic-like seizure/convulsion in Caenorhabditis elegans. Seizure 19, 439–442. https://doi.org/10.1016/j.seizure.2010.06.005 (2010).Article 
PubMed 

Google Scholar 
Risley, M. G., Kelly, S. P. & Dawson-Scully, K. Electroshock induced seizures in adult C. elegans. Bio-Protoc. 7, 163786 (2017).Article 

Google Scholar 
Risley, M. G., Kelly, S. P., Minnerly, J., Jia, K. & Dawson-Scully, K. egl-4 modulates electroconvulsive seizure duration in C. elegans. Invert. Neurosci. 18, 8. https://doi.org/10.1007/s10158-018-0211-9 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
McVey, K. A. et al. Exposure of C. elegans eggs to a glyphosate-containing herbicide leads to abnormal neuronal morphology. Neurotoxicol. Teratol. 55, 23–31. https://doi.org/10.1016/j.ntt.2016.03.002 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Burchfield, S. L. et al. Acute exposure to a glyphosate-containing herbicide formulation inhibits Complex II and increases hydrogen peroxide in the model organism Caenorhabditis elegans. Environ. Toxicol. Pharmacol. 66, 36–42. https://doi.org/10.1016/j.etap.2018.12.019 (2019).CAS 
Article 
PubMed 

Google Scholar 
Weisskopf, M. G., Moisan, F., Tzourio, C., Rathouz, P. J. & Elbaz, A. Pesticide exposure and depression among agricultural workers in France. Am. J. Epidemiol. 178, 1051–1058. https://doi.org/10.1093/aje/kwt089 (2013).Article 
PubMed 

Google Scholar 
Kamel, F. et al. Pesticide exposure and self-reported Parkinson’s disease in the agricultural health study. Am. J. Epidemiol. 165, 364–374. https://doi.org/10.1093/aje/kwk024 (2007).CAS 
Article 
PubMed 

Google Scholar 
Tanner, C. M. Advances in environmental epidemiology. Mov. Disord. 25(Suppl 1), S58-62. https://doi.org/10.1002/mds.22721 (2010).Article 
PubMed 

Google Scholar 
Dick, F. D. Parkinson’s disease and pesticide exposures. Br. Med. Bull. 79–80, 219–231. https://doi.org/10.1093/bmb/ldl018 (2006).CAS 
Article 
PubMed 

Google Scholar 
Brown, T. P., Rumsby, P. C., Capleton, A. C., Rushton, L. & Levy, L. S. Pesticides and Parkinson’s disease–Is there a link?. Environ. Health Perspect. 114, 156–164. https://doi.org/10.1289/ehp.8095 (2006).Article 
PubMed 

Google Scholar 
Firestone, J. A. et al. Pesticides and risk of Parkinson disease: A population-based case-control study. Arch. Neurol. 62, 91–95. https://doi.org/10.1001/archneur.62.1.91 (2005).Article 
PubMed 

Google Scholar 
Martinez, M. A. et al. Neurotransmitter changes in rat brain regions following glyphosate exposure. Environ. Res. 161, 212–219. https://doi.org/10.1016/j.envres.2017.10.051 (2018).CAS 
Article 
PubMed 

Google Scholar 
Kalueff, A. V. & Nutt, D. J. Role of GABA in anxiety and depression. Depress. Anxiety 24, 495–517. https://doi.org/10.1002/da.20262 (2007).CAS 
Article 
PubMed 

Google Scholar 
Mohler, H. The GABA system in anxiety and depression and its therapeutic potential. Neuropharmacology 62, 42–53. https://doi.org/10.1016/j.neuropharm.2011.08.040 (2012).CAS 
Article 
PubMed 

Google Scholar 
Brambilla, P., Perez, J., Barale, F., Schettini, G. & Soares, J. C. GABAergic dysfunction in mood disorders. Mol. Psychiatry 8, 721–737. https://doi.org/10.1038/sj.mp.4001362 (2003) (715).CAS 
Article 
PubMed 

Google Scholar 
Xia, G. et al. Reciprocal control of obesity and anxiety-depressive disorder via a GABA and serotonin neural circuit. Mol. Psychiatry 26, 2837–2853. https://doi.org/10.1038/s41380-021-01053-w (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Martinez, A. & Al-Ahmad, A. J. Effects of glyphosate and aminomethylphosphonic acid on an isogeneic model of the human blood-brain barrier. Toxicol. Lett. 304, 39–49. https://doi.org/10.1016/j.toxlet.2018.12.013 (2019).CAS 
Article 
PubMed 

Google Scholar 
Goetz, T., Arslan, A., Wisden, W. & Wulff, P. GABA(A) receptors: Structure and function in the basal ganglia. Prog. Brain Res. 160, 21–41. https://doi.org/10.1016/S0079-6123(06)60003-4 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Shaw, W. Elevated urinary glyphosate and clostridia metabolites with altered dopamine metabolism in triplets with autistic spectrum disorder or suspected seizure disorder: A case study. Integr. Med. (Encinitas) 16, 50–57 (2017).
Google Scholar 
Gaupp-Berghausen, M., Hofer, M., Rewald, B. & Zaller, J. G. Glyphosate-based herbicides reduce the activity and reproduction of earthworms and lead to increased soil nutrient concentrations. Sci. Rep. 5, 12886. https://doi.org/10.1038/srep12886 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kanabar, M. et al. Roundup negatively impacts the behavior and nerve function of the Madagascar hissing cockroach (Gromphadorhina portentosa). Environ. Sci. Pollut. Res. Int. https://doi.org/10.1007/s11356-021-13021-6 (2021).Article 
PubMed 

Google Scholar 
Loscher, W., Fassbender, C. P. & Nolting, B. The role of technical, biological and pharmacological factors in the laboratory evaluation of anticonvulsant drugs. II. Maximal electroshock seizure models. Epilepsy Res. 8, 79–94. https://doi.org/10.1016/0920-1211(91)90075-q (1991).CAS 
Article 
PubMed 

Google Scholar 
Castel-Branco, M. M., Alves, G. L., Figueiredo, I. V., Falcao, A. C. & Caramona, M. M. The maximal electroshock seizure (MES) model in the preclinical assessment of potential new antiepileptic drugs. Methods Find. Exp. Clin. Pharmacol. 31, 101–106. https://doi.org/10.1358/mf.2009.31.2.1338414 (2009).CAS 
Article 
PubMed 

Google Scholar 
Luszczki, J. J. et al. Anticonvulsant and acute neurotoxic effects of imperatorin, osthole and valproate in the maximal electroshock seizure and chimney tests in mice: A comparative study. Epilepsy Res. 85, 293–299. https://doi.org/10.1016/j.eplepsyres.2009.03.027 (2009).CAS 
Article 
PubMed 

Google Scholar 
Suthakaran, N. et al. O-GlcNAc transferase OGT-1 and the ubiquitin ligase EEL-1 modulate seizure susceptibility in C. elegans. PLoS ONE 16, e0260072. https://doi.org/10.1371/journal.pone.0260072 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Hedberg, D. & Wallin, M. Effects of Roundup and glyphosate formulations on intracellular transport, microtubules and actin filaments in Xenopus laevis melanophores. Toxicol. In Vitro 24, 795–802. https://doi.org/10.1016/j.tiv.2009.12.020 (2010).CAS 
Article 
PubMed 

Google Scholar  More

Site descriptionThe study site (about 1 ha) is within a coastal dune ecosystem (35° 32′ 26.0″ N, 134° 12′ 27.5″ E) located at the Arid Land Research Center of Tottori University, Tottori, Japan. The mean annual temperature is 15.2 °C, and the mean total precipitation is 1931 mm, based on records collected from 1991 to 2020 at the Tottori observation station of the Japan Meteorological Agency. Dominant plant species around the measurement plot were Vitex rotundifolia and Artemisia capillaris. Carex kobomugi and Ischaemum anthephoroides were also scattered around the coastal side of the study site, and planted Pinus thunbergii trees cover the inland side.Experimental designIn May 2020, we established four measurement plots at the study site (Fig. 9). Plot 1 was a gap area surrounded by V. rotundifolia seedlings. Plot 2 consisted of clusters of V. rotundifolia seedlings and was adjacent to plot 1. Within plots 1 and 2, C. kobomugi and I. anthephoroides were also scattered. Plot 3 was in a mixed area of V. rotundifolia and A. capillaris; this plot was in the center of the study site. Plot 4 was located in front of P. thunbergii trees and was in the most inland area of the study site. On 10 June 2020, we set an environmental measurement system at the center of the study site adjacent to plot 3, and we then obtained continuous data for soil temperature and soil moisture. In each plot (main plot), we set 10 plastic (polypropylene) collars (n = 10) before the start of the Rs measurement. We measured Rs every 2 weeks from 15 June to 2 December 2020 in the main plots. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June and early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July (Fig. 9), and flux calculations for plot 1 were conducted after removing data from the invaded area measured on June 15.Figure 9Diagram and photos of measurement plots in the focal coastal dune ecosystem. Vitex rotundifolia and C. kobomugi invaded a part of plot 1 in late June to early July, after the first Rs measurement on 15 June. Therefore, we set new measurement points for plot 1 in early July.Full size imageEnvironmental measurement systemThe environmental measurement system was composed of a data logger (CR1000, Campbell Scientific Inc., Logan, UT, USA), battery (SC dry battery, Kind Techno Structure Co. Ltd, Saitama, Japan), solar panel (RNG-50D-SS, RENOGY International Inc., Ontario, CA, USA), charge controller (Solar Amp mini, CSA-MN05-8, DENRYO, Tokyo, Japan), thermocouples (E type), and soil moisture sensors (CS616, Campbell Scientific Inc.). The data logger, battery, and charge controller were kept in a plastic box to avoid exposure to rainfall and sand. Each end of the thermocouple was inserted into a copper tube (4-mm inner diameter, 5-cm length) and affixed with glue. To measure the reference soil temperature at different depths, copper tubes enclosing E-type thermocouples were buried horizontally in the sand at depths of 5, 10, 30, and 50 cm (n = 1 for each depth) at the center of plot 3 as reference soil temperature (the data was recorded every 30 min). In addition, we set stand-alone soil temperature sensors (Thermochron SL type, KN Laboratories, Inc. Osaka, Japan) at the center of plots 1 and 4 at depths of 5, 10, and 30 cm (n = 1 for each plot, each depth), and they recorded soil temperature data every 30 min. Reference soil temperature at the depth of 5, 10, and 30 cm was used for gap-filling for soil temperature measured by stand-alone sensors at each depth and plot. Soil moisture sensors were buried horizontally in the sand at a depth of 30 cm in the center of plots 1, 3, and 4 (n = 1 for each plot) and recorded data every 30 min. Raw values of soil moisture sensors were converted to volumetric soil moisture (%) using a calibration line from 0 to 15% measured in the laboratory using dune sand and three sensors (CS616) referring to the procedure of Bongiovanni et al.53. Data for precipitation at the local meteorological observatory in Tottori was downloaded from the home page of the Japan Meteorological Agency (https://www.data.jma.go.jp/gmd/risk/obsdl/index.php).
R
s measurement in the main plotsPolypropylene collars (30-cm inner diameter, 5-cm depth, n = 10) were set in each measurement plot in late May 2020. The first Rs measurement was conducted on 15 June 2020. However, V. rotundifolia and C. kobomugi then invaded about half of the gap area of plot 1, so on 1 July we set 5 new polypropylene collars for plot 1 to replace the 5 invaded measurement points (Fig. 9). The second Rs measurement was conducted on 2 July, and all polypropylene collars then remained in the same position until the end of the measurement period.Rs was measured using an automated closed dynamic chamber system54 composed of two cylindrical aluminum chambers (30 cm diameter, 30 cm height) equipped with thermistor temperature sensors (44006, Omega Engineering, Stanford, CA, USA) for measuring air temperature inside the chamber during Rs measurement. Those chambers were connected to a control box equipped with a pump, data logger (CR1000, Campbell Scientific Inc.), CO2 analyzer (Gascard NG infrared gas sensor, Edinburgh Sensors, Lancashire, UK), and thermometer (MHP, Omega Engineering). The composition of the control box is basically the same as used in previous studies54,55. The measurement period for each point was 3 min, and the CO2 concentration and air temperature inside the chamber were recorded every 5 s. During the measurement, another chamber was set on the next polypropylene collar with the lid opened, and the next measurement was started at that moment of finishing the previous measurement by automatically closing the chamber lid on the next polypropylene collar in the same plot. Soil temperature at a depth of 0–5 cm was recorded simultaneously by inserting the rod of the thermometer vertically into the soil surface near the polypropylene collar (about 1–2 m from the collar).Rs was calculated by using the following equation:$$R_{{text{s}}} = frac{{PV}}{{RS(T_{{{text{air}}}} + 273.15)}}frac{{partial C}}{{partial t}},$$
(1)
where P is the air pressure (Pa), V is the effective chamber volume (m3), R is the ideal gas constant (8.314 Pa m3 K−1 mol−1), S is the soil surface area (m2), Tair is the air temperature inside the chamber (°C). ∂C/∂t is the rate of change of the CO2 mole fraction (μmol mol−1 s−1), which was calculated using least-squares regression of the CO2 changes inside the chamber12. For the flux calculation, we removed data for the first 35 s (dead band) of each measurement as an outlier.Trench treatment and soil CO2 efflux (F
c) measurement in subplotsIn November 2020, we conducted root-cut treatment (trench treatment) in subplots using polyvinyl chloride (PVC) tubes to estimate the contribution of Ra to Rs in the soil layer above 50 cm in each plot (Ra_50/Rs). Small PVC collars (10.7 cm inner diameter, 5 cm depth, n = 10 for each plot), with the upper ends about 1–2 cm above the soil surface, were set in subplots adjacent to the main plots on 23 October 2020. Rs was measured in subplots using two cylindrical mini PVC chambers (11.8 cm inner diameter at the bottom, 30 cm height, equipped with the same thermistors as cylindrical aluminum chambers for air temperature measurement) connected to the same control box as used for Rs measurement in the main plots. The measurement period was 3 min, and the measurement procedure and the flux calculation were the same as the main plot. Rs was first measured in subplots on 3 November to examine the spatial variation of Rs before trench treatment. Using the data, we selected subplots to conduct trench treatment and control plots for comparison, while aiming to achieve a minimal difference in the average Rs between control and pre-trenched plots. On 4 November, we inserted PVC tubes (10.7 cm inner diameter, 50 cm length) into about half (n = 3–5) of the subplots (the same position as PVC collars were set on 23 October) by using a hammer and aluminum lid until the upper end of each PVC tube was 1–2 cm above the soil surface to exclude roots to a depth of about 50 cm. On 19 November, after 15 days of trench treatment, respiration was measured in the same subplots.The Ra_50/Rs was calculated as follows:$$R_{{{text{a}}_{5}0}} /R_{{text{s}}} = (F_{{{text{c}}_{text{control}}}} -F_{{{text{c}}_{text{trenched}}}}) /F_{{{text{c}}_{text{control}}}} ,$$
(2)
where Fc_trenched and Fc_control (= Rs) are the Fc values in trenched and control plots on 19 November, respectively.In late December 2020, all the belowground plant biomass (BPB) in subplots (control and trenched plots) to a depth of 50 cm was collected for biomass analysis, about 2 months after trench treatment. In the laboratory, all the collected plant materials were washed and oven-dried for 72 h at 70 °C, and then the dry weight of the BPB samples was measured.Biomass measurementWe conducted BPB analysis from 18 May to 8 June 2021 in each plot (n = 1). At that time, 100 cm × 100 cm sampling plots near the CO2 measurement plots (100 cm × 100 cm for plots 2–4 and 50 cm × 50 cm in plot 1 because of the narrow gap area) were dug to a depth of 100–220 cm, according to the root distribution in each plot, and all plant materials were collected by passing the soil through 5- to 7-mm sieves. Once we reached a depth where no roots were visible, no more digging was conducted. In plots 2 and 3, stolons of V. rotundifolia were difficult to distinguish from roots if underground. Therefore, we defined plant material as BPB if it was underground. In the laboratory, all of the collected plant materials were washed and air-dried at room temperature for 0–6 days depending on the biomass. After that, samples were oven-dried for 15–25 h at 70–80 °C, and the dry weight of those samples was then measured.Soil organic carbon and nitrogenOn 21 October 2020, soil pits were dug to a depth of 50 cm near each plot (n = 3), and soil core samples were collected. Cylindrical stainless core samplers (5 cm diameter, 5 cm height, 100 cc) were horizontally inserted into the soil pit at depths of 0–5, 5–10, 10–20, and 20–30 cm. In the laboratory, soil core samples were weighed and oven-dried at 105 °C for 48 h, and the dry weight was measured. Oven-dried soil samples were sieved with a 2-mm-pore stainless wire mesh screen, and visible fungal mycelia in soil samples from plot 4 were removed as well as possible. Sieved samples were ground with an agate mortar. Samples (fine powder) were oven-dried for 24 h at 105 °C and weighed before SOC and nitrogen analysis. About 1.5 g of powdered samples were used for the analysis. Organic carbon content (combustion at 400 °C) and total nitrogen in samples were analyzed using a Soli TOC cube (Elementar Analysensysteme GmbH, Langenselbold, Germany) by the combustion method.Microbial abundanceOn 21 October 2020, soil samples for microbial analysis were collected at the same time as soil core sampling for SOC and nitrogen analysis. Soil samples were collected at depths of 0–10, 10–20, and 20–30 cm using a stainless spatula and placed individually in a polyethylene bag. The bags were kept in a cooler box with ice in the field and then placed in a freezer (− 30 °C) in the laboratory soon after sampling.DNA was extracted from 0.5 g of the fresh soils using NucleoSpin Soil (Takara Bio, Inc., Shiga, Japan) according to the manufacturer’s instructions (SL1 buffer), and the extracts were stored at − 20 °C until further analysis. Bacterial and archaeal 16S rRNA and fungal internal transcribed spacer (ITS) gene were targeted to investigate the microbial abundance. Bacterial and archaeal 16S rRNA (V4 region) and fungal ITS were determined using the universal primer sets 515F/806R and ITS1F_KYO2/ITS2_KYO2, respectively56,57.For qPCR, samples were prepared with 10 μL of the KAPA SYBR Fast qPCR kit (Kapa Biosystems, Wilmington, MA, USA), 0.8 μL of forward primer, 0.8 μL of reverse primer, and 3 μL of 1–50 × diluted soil DNA. Nuclease-free water was added to make up to a final volume of 20 μL. Cycling conditions of 16S rRNA were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 1 min. Cycling conditions of ITS were 95 °C for 30 s, followed by 40 cycles at 95 °C for 30 s, 55 °C for 1 min, and 72 °C for 1 min. A melting curve analysis was performed in a final cycle of 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 15 s. High amplification efficiencies of 99% for bacterial and archaeal 16S rRNA genes and 101% for the fungal ITS were obtained based on the standard curves.Data analysisTo examine the environmental response (soil temperature and soil moisture) of Rs, nonlinear and quadratic regression models were applied. We conducted F-tests by comparing the regression model to a constant model whose value is the mean of the observations (significance set at p  More

Beillouin, D., Ben-Ari, T., Malezieux, E., Seufert, V. & Makowski, D. Positive but variable effects of crop diversification on biodiversity and ecosystem services. Glob. Change Biol. 27, 4697–4710 (2021).CAS 
Article 

Google Scholar 
Ditzler, L. et al. Current research on the ecosystem service potential of legume inclusive cropping systems in Europe. A review. Agron. Sustain. Dev. 41, 26 (2021).Article 

Google Scholar 
Snapp, S. S., Blackie, M. J., Gilbert, R. A., Bezner-Kerr, R. & Kanyama-Phiri, G. Y. Biodiversity can support a greener revolution in Africa. Proc. Natl Acad. Sci. USA 107, 20840–20845 (2010).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Renard, D. & Tilman, D. National food production stabilized by crop diversity. Nature 571, 257–260 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Rodriguez, C., Mårtensson, L.-M. D., Jensen, E. S. & Carlsson, G. Combining crop diversification practices can benefit cereal production in temperate climates. Agron. Sustain. Dev. 41, 48 (2021).Article 

Google Scholar 
Zeng, Z. H. et al. in Crop Rotations: Farming Practices, Monitoring and Environmental Benefits (ed. Ma, B. L.) Ch. 1, 51–70 (Nova Science Publishers, 2016).Cusworth, G., Garnett, T. & Lorimer, J. Legume dreams: the contested futures of sustainable plant-based food systems in Europe. Glob. Environ. Change 69, 102321 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Reckling, M. et al. Grain legume yields are as stable as other spring crops in long-term experiments across northern Europe. Agron. Sustain. Dev. 38, 63 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Snapp, S. S., Cox, C. M. & Peter, B. G. Multipurpose legumes for smallholders in sub-Saharan Africa: identification of promising ‘scale out’ options. Glob. Food Secur-Agr. 23, 22–32 (2019).Article 

Google Scholar 
Hegewald, H., Wensch-Dorendorf, M., Sieling, K. & Christen, O. Impacts of break crops and crop rotations on oilseed rape productivity: a review. Eur. J. Agron. 101, 63–77 (2018).Article 

Google Scholar 
Angus, J. F. et al. Break crops and rotations for wheat. Crop . Sci. 66, 523–552 (2015).
Google Scholar 
Franke, A. C., van den Brand, G. J., Vanlauwe, B. & Giller, K. E. Sustainable intensification through rotations with grain legumes in Sub-Saharan Africa: a review. Agric. Ecosyst. Environ. 261, 172–185 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Preissel, S., Reckling, M., Schlaefke, N. & Zander, P. Magnitude and farm-economic value of grain legume pre-crop benefits in Europe: a review. Field Crops Res. 175, 64–79 (2015).Article 

Google Scholar 
Zhao, J. et al. Does crop rotation yield more in China? A meta-analysis. Field Crops Res. 245, 107659 (2020).Article 

Google Scholar 
Tamburini, G. et al. Agricultural diversification promotes multiple ecosystem services without compromising yield. Sci. Adv. 6, eaba1715 (2020).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cernay, C., Makowski, D. & Pelzer, E. Preceding cultivation of grain legumes increases cereal yields under low nitrogen input conditions. Environ. Chem. Lett. 16, 631–636 (2018).CAS 
Article 

Google Scholar 
Peoples, M. B. et al. The contributions of nitrogen-fixing crop legumes to the productivity of agricultural systems. Symbiosis 48, 1–17 (2009).CAS 
Article 

Google Scholar 
Watson, C. A. et al. Grain legume production and use in European agricultural systems. Adv. Agron. 144, 235–303 (2017).Article 

Google Scholar 
Bennett, A. J., Bending, G. D., Chandler, D., Hilton, S. & Mills, P. Meeting the demand for crop production:The challenge of yield decline in crops grown in short rotations. Biol. Rev. 87, 52–71 (2012).PubMed 
Article 

Google Scholar 
Drinkwater, L. E., Wagoner, P. & Sarrantonio, M. Legume-based cropping systems have reduced carbon and nitrogen losses. Nature 396, 262–265 (1998).ADS 
CAS 
Article 

Google Scholar 
Smith, C. J. & Chalk, P. M. Grain legumes in crop rotations under low and variable rainfall: are observed short-term N benefits sustainable? Plant Soil 453, 271–279 (2020).CAS 
Article 

Google Scholar 
Pullens, J. W. M., Sorensen, P., Melander, B. & Olesen, J. E. Legacy effects of soil fertility management on cereal dry matter and nitrogen grain yield of organic arable cropping systems. Eur. J. Agron. 122, 126169 (2021).CAS 
Article 

Google Scholar 
Tognetti, P. M. et al. Negative effects of nitrogen override positive effects of phosphorus on grassland legumes worldwide. Proc. Natl Acad. Sci. USA 118, 28 (2021).Article 

Google Scholar 
Kirkegaard, J., Christen, O., Krupinsky, J. & Layzell, D. Break crop benefits in temperate wheat production. Field Crops Res. 107, 185–195 (2008).Article 

Google Scholar 
Brisson, N. et al. Why are wheat yields stagnating in Europe? A comprehensive data analysis for France. Field Crops Res. 119, 201–212 (2010).Article 

Google Scholar 
Anderson, R. L. Synergism: a rotation effect of improved growth efficiency. Adv. Agron. 112, 205–226 (2011).Article 

Google Scholar 
Bonilla-Cedrez, C., Chamberlin, J. & Hijmans, R. Fertilizer and grain prices constrain food production in sub-Saharan Africa. Nat. Food 2, 766–772 (2021).Article 

Google Scholar 
Seufert, V., Ramankutty, N. & Foley, J. A. Comparing the yields of organic and conventional agriculture. Nature 485, 229–232 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Barbieri, P., Pellerin, S., Seufert, V. & Nesme, T. Changes in crop rotations would impact food production in an organically farmed world. Nat. Sustain. 2, 378–385 (2019).Article 

Google Scholar 
Barbieri, P. et al. Global option space for organic agriculture is delimited by nitrogen availability. Nat. Food 2, 363–372 (2021).Article 

Google Scholar 
Muller, A. et al. Strategies for feeding the world more sustainably with organic agriculture. Nat. Commun. 8, 1290 (2017).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nowak, B., Nesme, T., David, C. & Pellerin, S. Disentangling the drivers of fertilising material inflows in organic farming. Nutr. Cycl. Agroecosyst. 96, 79–91 (2013).Article 

Google Scholar 
Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trends Ecol. Evol. 31, 440–452 (2016).PubMed 
Article 

Google Scholar 
Mariotte, P. et al. Plant-soil feedback: Bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).PubMed 
Article 

Google Scholar 
Everwand, G., Cass, S., Dauber, J., Williams, M. & Stout, J. Legume crops and biodiversity. Legumes in Cropping Systems, 4, 55–69 (2017).Peoples, M. B., Giller, K. E., Jensen, E. S. & Herridge, D. F. Quantifying country-to-global scale nitrogen fixation for grain legumes: I. Reliance on nitrogen fixation of soybean, groundnut and pulses. Plant Soil 469, 1–14 (2021).CAS 
Article 

Google Scholar 
Abalos, D., van Groenigen, J. W., Philippot, L., Lubbers, I. M. & De Deyn, G. B. Plant trait-based approaches to improve nitrogen cycling in agroecosystems. J. Appl. Ecol. 56, 2454–2466 (2019).Article 

Google Scholar 
Garland, G. et al. Crop cover is more important than rotational diversity for soil multifunctionality and cereal yields in European cropping systems. Nat. Food 2, 28–37 (2021).Article 

Google Scholar 
Pandey, A., Li, F., Askegaard, M., Rasmussen, I. A. & Olesen, J. E. Nitrogen balances in organic and conventional arable crop rotations and their relations to nitrogen yield and nitrate leaching losses. Agric. Ecosyst. Environ. 265, 350–362 (2018).CAS 
Article 

Google Scholar 
Cook, R. J. Toward cropping systems that enhance productivity and sustainability. Proc. Natl Acad. Sci. USA 103, 18389–18394 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gan, Y. T. et al. Improving farming practices reduces the carbon footprint of spring wheat production. Nat. Commun. 5, 13 (2014).
Google Scholar 
Hufnagel, J., Reckling, M. & Ewert, F. Diverse approaches to crop diversification in agricultural research. A review. Agron. Sustain. Dev. 40, 14 (2020).Article 

Google Scholar 
Ma, B. L. & Wu, W. in Crop Rotations: Farming Practices, Monitoring and Environmental Benefits (ed Ma B. L.) Ch. 1, 1–35 (Nova Science Publishers, 2016).Seymour, M., Kirkegaard, J. A., Peoples, M. B., White, P. F. & French, R. J. Break-crop benefits to wheat in Western Australia – insights from over three decades of research. Crop. Sci. 63, 1–16 (2012).
Google Scholar 
Sileshi, G., Akinnifesi, F. K., Ajayi, O. C. & Place, F. Meta-analysis of maize yield response to woody and herbaceous legumes in sub-Saharan Africa. Plant Soil 307, 1–19 (2008).CAS 
Article 

Google Scholar 
Bullock, D. G. Crop rotation. Crit. Rev. Plant Sci. 11, 309–326 (1992).Article 

Google Scholar 
Danga, B. O., Ouma, J. P., Wakindiki, I. I. C. & Bar-Tal, A. Legume-wheat ration effects on residual soil moisture, nitrogen and wheat yield in tropical regions. Adv. Agron. 101, 315–349 (2009).Article 

Google Scholar 
Ghosh, P. K. et al. Legume effect for enhancing productivity and nutrient use-efficiency in major cropping systems – An Indian perspective: a review. J. Sustain. Agric. 30, 59–86 (2007).Article 

Google Scholar 
Karlen, D. L., Varvel, G. E., Bullock, D. G. & Cruse, R. M. Crop rotation for the 21st century. Adv. Agron. 53, 1–45 (1994).Article 

Google Scholar 
Martin, G. et al. Role of ley pastures in tomorrow’s cropping systems. A review. Agron. Sustain. Dev. 40, 17 (2020).Article 

Google Scholar 
Ruisi, P. et al. Agro-ecological benefits of faba bean for rainfed Mediterranean cropping systems. Ital. J. Agron. 12, 233–245 (2017).
Google Scholar 
Ryan, J., Singh, M. & Pala, M. Long-term cereal-based rotation trials in the Mediterranean region: Implications for cropping sustainability. Adv. Agron. 97, 273–319 (2008).CAS 
Article 

Google Scholar 
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G. & Grp, P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. J. Clin. Epidemiol. 62, 1006–1012 (2009).PubMed 
Article 

Google Scholar 
Pittelkow, C. M. et al. Productivity limits and potentials of the principles of conservation agriculture. Nature 517, 365–368 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Wieder, W. R., Boehnert, J., Bonan, G. B. & Langseth, M. Regridded Harmonized World Soil Database v1.2. ORNL DAAC. https://doi.org/10.3334/ORNLDAAC/1247 (2014).Soil Survey Staff. Soil taxonomy: a basic system of soil classification for making and interpreting soil surveys. 2nd edition. Natural Resources Conservation Service. U.S. Department of Agriculture Handbook 436. (1999).FAO. World Programme of the Census of Agriculture 2020. Vol. 1 (2015).Tiemann, L. K., Grandy, A. S., Atkinson, E. E., Marin-Spiotta, E. & McDaniel, M. D. Crop rotational diversity enhances belowground communities and functions in an agroecosystem. Ecol. Lett. 18, 761–771 (2015).CAS 
PubMed 
Article 

Google Scholar 
Tilman, D. et al. The influence of functional diversity and composition on ecosystem processes. Science 277, 1300–1302 (1997).CAS 
Article 

Google Scholar 
Yates, F. The analysis of experiments containing different crop rotations. Biometrics 10, 324–346 (1954).Article 

Google Scholar 
Zhao, J. et al. Dataset for evaluating global yield advantage and its drivers of legume-based rotations. Figshare, https://doi.org/10.6084/m9.figshare.20290923 (2022).Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Adams, D. C., Gurevitch, J. & Rosenberg, M. S. Resampling tests for meta-analysis of ecological data. Ecology 78, 1277–1283 (1997).Article 

Google Scholar 
Van Lissa, C. MetaForest: Exploring Heterogeneity in Meta-analysis Using Random Forests. (2017).Terrer, C. et al. A trade-off between plant and soil carbon storage under elevated CO2. Nature 591, 599–CO603 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).MATH 
Article 

Google Scholar 
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Rosenberg, M. S. The file-drawer problem revisited: a general weighted method for calculating fail-safe numbers in meta-analysis. Evolution 59, 464–468 (2005).PubMed 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing v.4.0.3 (R Foundation for Statistical Computing, Vienna, Austria, 2021). More

Fodrie, F. J. et al. Measuring individuality in habitat use across complex landscapes: Approaches, constraints, and implications for assessing resource specialization. Oecologia 178, 75–87 (2015).ADS 
PubMed 
Article 

Google Scholar 
Bacheler, N. M., Michelot, T., Cheshire, R. T. & Shertzer, K. W. Fine-scale movement patterns and behavioral states of gray triggerfish Balistes capriscus determined from acoustic telemetry and hidden Markov models. Fish. Res. 215, 76–89 (2019).Article 

Google Scholar 
Furey, N. B., Dance, M. A. & Rooker, J. R. Fine-scale movements and habitat use of juvenile southern flounder Paralichthys lethostigma in an estuarine seascape. J. Fish Biol. 82, 1469–1483 (2013).CAS 
PubMed 
Article 

Google Scholar 
Froehlich, C. Y. M., Garcia, A. & Kline, R. J. Daily movement patterns of red snapper (Lutjanus campechanus) on a large artificial reef. Fish. Res. 209, 49–57 (2019).Article 

Google Scholar 
Williams-Grove, L. J. & Szedlmayer, S. T. Acoustic positioning and movement patterns of red snapper, Lutjanus campechanus, around artificial reefs in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 553, 233–251 (2016).ADS 
Article 

Google Scholar 
Secor, D. H., Zhang, F., O’Brien, M. H. P. & Li, M. Ocean destratification and fish evacuation caused by a Mid-Atlantic tropical storm. ICES J. Mar. Sci. 76, 573–584 (2019).Article 

Google Scholar 
Bacheler, N. M., Shertzer, K. W., Cheshire, R. T. & MacMahan, J. H. Tropical storms influence the movement behavior of a demersal oceanic fish species. Sci. Rep. 9, 1–13 (2019).CAS 
Article 

Google Scholar 
Lowerre-Barbieri, S. K., Walters, S., Bickford, J., Cooper, W. & Muller, R. Site fidelity and reproductive timing at a spotted seatrout spawning aggregation site: Individual versus population scale behavior. Mar. Ecol. Prog. Ser. 481, 181–197 (2013).ADS 
Article 

Google Scholar 
Espinoza, M., Farrugia, T. J., Webber, D. M., Smith, F. & Lowe, C. G. Testing a new acoustic telemetry technique to quantify long-term, fine-scale movements of aquatic animals. Fish. Res. 108, 364–371 (2011).Article 

Google Scholar 
Roy, R. et al. Testing the VEMCO positioning system: Spatial distribution of the probability of location and the positioning error in a reservoir. Anim. Biotelemetry 2, 1 (2014).CAS 
Article 

Google Scholar 
Guzzo, M. M. et al. Field testing a novel high residence positioning system for monitoring the fine-scale movements of aquatic organisms. Methods Ecol. Evol. 9, 1478–1488 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Smedbol, S., Smith, F., Webber, D., Vallée, R. & King, T. Using underwater coded acoustic telemetry for fine scale positioning of aquatic animals. In 20th Symposium of the International Society on Biotelemetry Proceedings, 9–11 (2014).Dean, M. J., Hoffman, W. S., Zemeckis, D. R. & Armstrong, M. P. Fine-scale diel and gender-based patterns in behaviour of Atlantic cod (Gadus morhua) on a spawning ground in the western Gulf of Maine. ICES J. Mar. Sci. 71, 1474–1489 (2014).Article 

Google Scholar 
Tarnecki, J. H. & Patterson, W. F. A mini ROV-based method for recovering marine instruments at depth. PLoS One 15, 1–9 (2020).
Google Scholar 
Ellis, R. D. et al. Acoustic telemetry array evolution: From species- and project-specific designs to large-scale, multispecies, cooperative networks. Fish. Res. 209, 186–195 (2019).Article 

Google Scholar 
Friess, C. et al. Regional-scale variability in the movement ecology of marine fishes revealed by an integrative acoustic tracking network. Mar. Ecol. Prog. Ser. 663, 157–177 (2021).ADS 
Article 

Google Scholar 
Walters, C. J. & Juanes, F. Recruitment limitation as a consequence of natural selection for use of restricted feeding habitats and predation risk taking by juvenile fishes. Can. J. Fish. Aquat. Sci. 50, 2058–2070 (1993).Article 

Google Scholar 
Ahrens, R. N. M., Walters, C. J. & Christensen, V. Foraging arena theory. Fish Fish. 13, 41–59 (2012).Article 

Google Scholar 
Schwartzkopf, B. D., Langland, T. A. & Cowan, J. H. Habitat selection important for red snapper feeding ecology in the northwestern Gulf of Mexico. Mar. Coast. Fish. 9, 373–387 (2017).Article 

Google Scholar 
Wells, R. J. D., Cowan, J. H. Jr. & Fry, B. Feeding ecology of red snapper Lutjanus campechanus in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 361, 213–225 (2008).ADS 
Article 

Google Scholar 
Goldman, S. F., Glasgow, D. M. & Falk, M. M. Feeding habits of 2 reef-associated fishes, red porgy (Pagrus pagrus) and gray triggerfish (Balistes capriscus), off the Southeastern United States. Fish. Bull. 114, 317–329 (2016).Article 

Google Scholar 
Villegas-Ríos, D., Réale, D., Freitas, C., Moland, E. & Olsen, E. M. Personalities influence spatial responses to environmental fluctuations in wild fish. J. Anim. Ecol. 87, 1309–1319 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Rooker, J. R. et al. Seascape connectivity and the influence of predation risk on the movement of fishes inhabiting a back-reef ecosystem. Ecosphere 9, e02200 (2018).Article 

Google Scholar 
Forman, R. T. T. & Godron, M. Patches and structural components for a landscape ecology. Bioscience 31, 733–740 (1981).Article 

Google Scholar 
Dahl, K. A. & Patterson, W. F. Movement, home range, and depredation of invasive lionfish revealed by fine-scale acoustic telemetry in the northern Gulf of Mexico. Mar. Biol. 167, 1–22 (2020).Article 
CAS 

Google Scholar 
Schoener, T. W. Resource partitioning in ecological communities. Science 185, 27–39 (1974).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Moulton, D. L. et al. Habitat partitioning and seasonal movement of red drum and spotted seatrout. Estuaries Coasts 40, 905–916 (2017).Article 

Google Scholar 
Hammerschlag, N., Luo, J., Irschick, D. J. & Ault, J. S. A Comparison of spatial and movement patterns between sympatric predators: bull sharks (Carcharhinus leucas) and Atlantic tarpon (Megalops atlanticus). PLoS ONE 7, e45958 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Novak, A. J. et al. Scale of biotelemetry data influences ecological interpretations of space and habitat use in yellowtail snapper. Mar. Coast. Fish. 12, 364–377 (2020).Article 

Google Scholar 
Lima, S. L. & Dill, L. M. Behavioral decisions made under the risk of predation: A review and prospectus. Can. J. Zool. 68, 619–640 (1990).Article 

Google Scholar 
Werner, E. E. & Gilliam, J. F. The ontogenetic niche and species interactions in size-structured populations. Annu. Rev. Ecol. Syst. 15, 393–425 (1984).Article 

Google Scholar 
Reale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B Biol. Sci. 365, 4051–4063 (2010).Article 

Google Scholar 
Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 
Article 

Google Scholar 
Huntingford, F. A. The relationship between anti-predator behavior and aggression among conspecifics in the three-spined stickleback, Gasterosteus aculeatus. Anim. Behav. 24, 245–260 (1976).Article 

Google Scholar 
Wilson, D. S., Clark, A. B., Coleman, K. & Dearstyne, T. Shyness and boldness in humans and other animals. Trends Ecol. Evol. 9, 442–446 (1994).Article 

Google Scholar 
Harrison, P. M. et al. Personality-dependent spatial ecology occurs independently from dispersal in wild burbot (Lota lota). Behav. Ecol. 26, 483–492 (2015).Article 

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

Google Scholar 
Hussey, N. E. et al. Aquatic animal telemetry: A panoramic window into the underwater world. Science 348, 1255642–1255642 (2015).PubMed 
Article 
CAS 

Google Scholar 
Lowerre-Barbieri, S. K., Kays, R., Thorson, J. T. & Wikelski, M. The ocean’s movescape: Fisheries management in the bio-logging decade (2018–2028). ICES J. Mar. Sci. 76, 477–488 (2019).Article 

Google Scholar 
National Marine Fisheries Service. Fisheries Economics of the United State 2016. NOAA Tech. Memo. NMFS-F/SPO-187a. https://www.fisheries.noaa.gov/resource/document/fisheries-economics-united-states-report-2016 (2018). Accessed 08 January 2018.Patterson, W. F. III, Tarnecki, J., Addis, D. T. & Barbieri, L. R. Reef fish community structure at natural versus artificial reefs in the northern Gulf of Mexico. In Proc. 66th Gulf Caribb. Fish. Inst. 4–8 (2014).Streich, M. K. et al. Effects of a new artificial reef complex on red snapper and the associated fish community: An evaluation using a before–after control–impact approach. Mar. Coast. Fish. 9, 404–418 (2017).Article 

Google Scholar 
Dance, M. A., Patterson, W. F. III. & Addis, D. T. Fish community and trophic structure at artificial reef sites in the northeastern Gulf of Mexico. Bull. Mar. Sci. 87, 301–324 (2011).Article 

Google Scholar 
Cowan, J. H. Red snapper in the Gulf of Mexico and the U.S. South Atlantic: data, doubt, and debate. Fisheries 36, 319–331 (2011).Article 

Google Scholar 
Addis, D. T., Patterson, W. F. III. & Dance, M. A. The potential for unreported artificial reefs to serve as refuges from fishing mortality for reef fishes. N. Am. J. Fish. Manag. 36, 131–139 (2016).Article 

Google Scholar 
McCawley, J. R., Cowan, J. H. Jr. & Shipp, R. L. Feeding periodicity and prey habitat preference of red snapper, Lutjanus campechanus (Poey, 1860), on Alabama artificial reefs. Gulf Mex. Sci. 24, 14–27 (2006).
Google Scholar 
Glenn, H. D., Cowan, J. H. Jr. & Powers, J. E. A comparison of red snapper reproductive potential in the northwestern Gulf of Mexico: Natural versus artificial habitats. Mar. Coast. Fish. 9, 139–148 (2017).Article 

Google Scholar 
Kulaw, D. H., Cowan, J. H. Jr. & Jackson, M. W. Temporal and spatial comparisons of the reproductive biology of northern Gulf of Mexico (USA) red snapper (Lutjanus campechanus) collected a decade apart. PLoS One 12, e0172360 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vose, F. E. & Nelson, W. G. Gray triggerfish (Balistes capriscus Gmelin) feeding from artificial and natural substrate in shallow Atlantic waters of Florida. Bull. Mar. Sci. 55, 1316–1323 (1994).
Google Scholar 
Herbig, J. L. & Szedlmayer, S. T. Movement patterns of gray triggerfish, Balistes capriscus, around artificial reefs in the northern Gulf of Mexico. Fish. Manag. Ecol. 23, 418–427 (2016).Article 

Google Scholar 
Szedlmayer, S. T. & Schroepfer, R. L. Long-term residence of red snapper on artificial reefs in the northeastern Gulf of Mexico. Trans. Am. Fish. Soc. 134, 315–325 (2005).Article 

Google Scholar 
Watterson, J. C. III., Patterson, W. F. I. I. I., Shipp, R. L. & Cowan, J. H. Jr. Movement of red snapper, Lutjanus campechanus, in the north central Gulf of Mexico: Potential effects of hurricanes. Gulf Mex. Sci. 16, 92–104 (1998).
Google Scholar 
Ingram, G. W. Jr. & Patterson, W. F. I. I. I. Movement patterns of red snapper (Lutjanus campechanus), greater amberjack (Seriola dumerili), and gray triggerfish (Balistes capriscus) in the Gulf of Mexico and the utility of marine reserves as management tools. Proc. Gulf Caribb. Fish. Inst. 52, 686–699 (2001).
Google Scholar 
Strelcheck, A. J., Cowan, J. H. Jr. & Patterson, W. F. III. Site fidelity, movement, and growth of red snapper Lutjanus campechanus: implications for artificial reef management. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 147–162 (2007).Addis, D. T., Patterson, W. F. I. I. I., Dance, M. A. & Ingram, G. W. Jr. Implications of reef fish movement from unreported artificial reef sites in the northern Gulf of Mexico. Fish. Res. 147, 349–358 (2013).Article 

Google Scholar 
Topping, D. T. & Szedlmayer, S. T. Site fidelity, residence time and movements of red snapper Lutjanus campechanus estimated with long-term acoustic monitoring. Mar. Ecol. Prog. Ser. 437, 183–200 (2011).ADS 
Article 

Google Scholar 
Everett, A. G., Szedlmayer, S. T. & Gallaway, B. J. Movement patterns of red snapper Lutjanus campechanus based on acoustic telemetry around oil and gas platforms in the northern Gulf of Mexico. Mar. Ecol. Prog. Ser. 649, 155–173 (2020).Article 

Google Scholar 
Tarnecki, J. H. & Patterson, W. F. I. I. I. Changes in red snapper diet and trophic ecology following the Deepwater Horizon Oil Spill. Mar. Coast. Fish. 7, 135–147 (2015).Article 

Google Scholar 
McCawley, J. R. & Cowan, J. H. Jr. Seasonal and size specific diet and prey demand of Red Snapper on Alabama artificial reefs. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III., Cowan, J. H. Jr., Fitzhugh, G. R. & Nieland, D. L.), 77–104 (2007).Piraino, M. N. & Szedlmayer, S. T. Fine-scale movements and home ranges of red snapper around artificial reefs in the northern Gulf of Mexico. Trans. Am. Fish. Soc. 143, 988–998 (2014).Article 

Google Scholar 
Williams-Grove, L. J. & Szedlmayer, S. T. Depth preferences and three-dimensional movements of red snapper, Lutjanus campechanus, on an artificial reef in the northern Gulf of Mexico. Fish. Res. 190, 61–70 (2017).Article 

Google Scholar 
Topping, D. T. & Szedlmayer, S. T. Home range and movement patterns of red snapper (Lutjanus campechanus) on artificial reefs. Fish. Res. 112, 77–84 (2011).Article 

Google Scholar 
Baker, M. S. J. & Wilson, C. A. Use of bomb radiocarbon to validate otolith section ages of red snapper Lutjanus campechanus from the northern Gulf of Mexico. Limnol. Oceanogr. 46, 1819–1824 (2001).ADS 
Article 

Google Scholar 
Allman, R. J., Fioramonti, C. L., Patterson, W. F. III. & Pacicco, A. E. Validation of annual growth-zone formation in gray triggerfish Balistes capriscus dorsal spines, fin rays, and vertebrae. Gulf Mex. Sci. 33, 68–76 (2016).
Google Scholar 
Frazer, T. K., Lindberg, W. J. & Stanton, G. R. Predation on sand dollars by gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Bull. Mar. Sci. 48, 159–164 (1991).
Google Scholar 
Delorenzo, D. M., Bethea, D. M. & Carlson, J. K. An assessment of the diet and trophic level of Atlantic sharpnose shark Rhizoprionodon terraenovae. J. Fish Biol. 86, 385–391 (2015).CAS 
PubMed 
Article 

Google Scholar 
Aines, A. C., Carlson, J. K., Boustany, A., Mathers, A. & Kohler, N. E. Feeding habits of the tiger shark, Galeocerdo cuvier, in the northwest Atlantic Ocean and Gulf of Mexico. Environ. Biol. Fish. 101, 403–415 (2018).Article 

Google Scholar 
Castro, J. I. The Sharks of North America (Oxford University Press, 2011).
Google Scholar 
Springer, S. A collection of fishes from the stomachs of sharks taken off Salerno, Florida. Copeia 3, 174–175 (1946).Article 

Google Scholar 
Bohaboy, E. C., Guttridge, T. L., Hammerschlag, N., Van Zinnicq Bergmann, M. P. M. & Patterson, W. F. III. Application of three-dimensional acoustic telemetry to assess the effects of rapid recompression on reef fish discard mortality. ICES J. Mar. Sci. 77, 83–96 (2020).Article 

Google Scholar 
Drymon, J. M., Powers, S. P., Dindo, J., Dzwonkowski, B. & Henwood, T. Distributions of sharks across a continental shelf in the northern Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 2, 440–450 (2010).Article 

Google Scholar 
Ajemian, M. J. et al. Movement patterns and habitat use of tiger sharks (Galeocerdo cuvier) across ontogeny in the Gulf of Mexico. PLoS One 15, 1–24 (2020).
Google Scholar 
Ouzts, A. C. & Szedlmayer, S. T. Diel feeding patterns of Red Snapper on artificial reefs in the north-central Gulf of Mexico. Trans. Am. Fish. Soc. 132, 1186–1193 (2003).Article 

Google Scholar 
White, D. B. & Palmer, S. M. Age, growth, and reproduction of the red snapper, Lutjanus campechanus, from the Atlantic waters of the Southeastern US. Bull. Mar. Sci. 75, 335–360 (2004).
Google Scholar 
Fitzhugh, G. R., Lyon, H. M. & Barnett, B. K. Reproductive parameters of gray triggerfish (Balistes capriscus) from the Gulf of Mexico: Sex ratio, maturity and spawning fraction. SEDAR43-WP-03. (2015). http://sedarweb.org/sedar-82-rd14-sedar43-wp-03reproductive-parameters-gray-triggerfish-balistes-capriscus-gulf-mexico. Accessed 12 April 2021.Kelly-Stormer, A. et al. Gray Triggerfish reproductive biology, age, and growth off the Atlantic coast of the Southeastern USA. Trans. Am. Fish. Soc. 146, 523–538 (2017).Article 

Google Scholar 
Porch, C. E., Fitzhugh, G. R., Lang, E. T., Lyon, H. M. & Linton, B. C. Estimating the dependence of spawning frequency on size and age in Gulf of Mexico red snapper. Mar. Coast. Fish. 7, 233–245 (2015).Article 

Google Scholar 
Lang, E. T. & Fitzhugh, G. R. Oogenesis and fecundity type of gray triggerfish in the Gulf of Mexico. Mar. Coast. Fish. Dyn. Manag. Ecosyst. Sci. 7, 338–348 (2015).Article 

Google Scholar 
Woods, M. K. et al. Size and age at maturity of female red snapper Lutjanus campechanus in the Northern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 54, 526–537 (2003).
Google Scholar 
Simmons, C. M. & Szedlmayer, S. T. Territoriality, reproductive behavior, and parental care in gray triggerfish, Balistes capriscus, from the Northern Gulf of Mexico. Bull. Mar. Sci. 88, 197–209 (2012).Article 

Google Scholar 
Mackichan, C. A. & Szedlmayer, S. T. Reproductive behavior of the gray triggerfish, Balistes capriscus, in the northeastern Gulf of Mexico. Proc. Gulf Caribb. Fish. Inst. 59, 213–218 (2007).
Google Scholar 
Diamond, S. L. et al. Movers and stayers: Individual variability in site fidelity and movements of red snapper off Texas. In Red Snapper Ecology and Fisheries in the U.S. Gulf of Mexico. American Fisheries Society Symposium 60 (eds. Patterson, W. F. III, Cowan, J. H. Jr., Nieland, D. A. & Fitzhugh, G. R.), 163–187 (2007).Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).ADS 
PubMed 
Article 

Google Scholar 
Smith, F. Understanding HPE in the VEMCO Positioning System (VPS). (2013).US Department of Defense. Global Positioning System Standard Positioning Service Performance Standard. http://www.gps.gov/technical/ps/2008-SPS-performance-standard.pdf (2008). Accessed 08 July 2020.Heupel, M. R., Reiss, K. L., Yeiser, B. G. & Simpfendorfer, C. A. Effects of biofouling on performance of moored data logging acoustic receivers. Limnol. Oceanogr. Methods 6, 327–335 (2008).Article 

Google Scholar 
National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42012—Orange Beach. http://www.ndbc.noaa.gov/station_page.php?station=42012 (2017). Accessed 07 November 2017.National Oceanic and Atmospheric Administration & National Weather Service. National Data Buoy Center: Station 42040- Luke Offshore Test Platform. https://www.ndbc.noaa.gov/station_page.php?station=42040 (2019). Accessed 07 January 2019.Lazaridis, E. R Package ‘lunar’: lunar phase & distance, seasons and other environmental factors. https://cran.r-project.org/web/packages/lunar/lunar.pdf (2015). Accessed 12 August 2019.Thieurmel, B. & Elmarhraoui, A. R Package ‘suncalc’: compute sun position, sunlight phases, moon position and lunar phase. https://cran.r-project.org/web/packages/suncalc/suncalc.pdf (2019). Accessed 22 June 2019.National Geophysical Data Center. U.S. Coastal Relief Model—Central Gulf of Mexico. https://doi.org/10.7289/V54Q7RW0 (2001).Cox, D. R. & Oakes, D. Analysis of Survival Data (Chapman and Hall, 1984).Benhamou, S. Dynamic approach to space and habitat use based on biased random bridges. PLoS One 6, e14592 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Horne, J. S., Garton, E. O., Krone, S. M. & Lewis, J. S. Analyzing animal movements using Brownian bridges. Ecology 88, 2354–2363 (2007).PubMed 
Article 

Google Scholar 
Tracey, J. A. et al. Movement-based estimation and visualization of space use in 3D for wildlife ecology and conservation. PLoS One 9, e101205 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tracey, J. A. et al. R Package ‘mkde’: 2D and 3D movement-based kernel density estimates (MKDEs). https://CRAN.R-project.org/package=mkde (2014). Accessed 17 June 2019.Worton, B. J. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70, 164–168 (1989).Article 

Google Scholar 
Wood, S. N. Package ‘mgcv’: Mixed GAM computation vehicle with automatic smoothness estimation. https://doi.org/10.1201/9781315370279 (2019). More

Wilson, E. O. Sociobiology: The New Synthesis (Harvard Univ. Press, 1975).Diamond, J. M. & Ordunio, D. Guns, Germs, and Steel (Books on Tape, 1999).Couzin, I. D. et al. Self-organization and collective behavior in vertebrates. Adv. Study Behav. 32, 1–75 (2003).
Google Scholar 
Keller, L. Adaptation and the genetics of social behaviour. Philos. Trans. R. Soc. Lond. B 364, 3209–3216 (2009).
Google Scholar 
Kay, T., Keller, L. & Lehmann, L. The evolution of altruism and the serial rediscovery of the role of relatedness. Proc. Natl Acad. Sci. USA 117, 28894–28898 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: the impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13, 701–712 (2012).CAS 
PubMed 

Google Scholar 
Johnson, K. V. A. & Foster, K. R. Why does the microbiome affect behaviour? Nat. Rev. Microbiol. 16, 647–655 (2018).CAS 
PubMed 

Google Scholar 
Sherwin, E., Bordenstein, S. R., Quinn, J. L., Dinan, T. G. & Cryan, J. F. Microbiota and the social brain. Science 366, eaar2016 (2019).CAS 
PubMed 

Google Scholar 
Desbonnet, L., Clarke, G., Shanahan, F., Dinan, T. G. & Cryan, J. F. Microbiota is essential for social development in the mouse. Mol. Psychiatry 19, 146–148 (2014).CAS 
PubMed 

Google Scholar 
Sharon, G. et al. Human gut microbiota from autism spectrum disorder promote behavioral symptoms in mice. Cell 177, 1600–1618 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhang, M. et al. A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children. Sci. Adv. 6, eaba3760 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wu, W.-L. et al. Microbiota regulate social behaviour via stress response neurons in the brain. Nature 595, 409–414 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Vuong, H. E., Yano, J. M., Fung, T. C. & Hsiao, E. Y. The microbiome and host behavior. Annu. Rev. Neurosci. 40, 21–49 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, A. E. Simple animal models for microbiome research. Nat. Rev. Microbiol. 17, 764–775 (2019).CAS 
PubMed 

Google Scholar 
Schretter, C. E. Links between the gut microbiota, metabolism, and host behavior. Gut Microbes 11, 245–248 (2020).PubMed 

Google Scholar 
Liberti, J. & Engel, P. The gut microbiota–brain axis of insects. Curr. Opin. Insect Sci. 39, 6–13 (2020).PubMed 

Google Scholar 
O’Donnell, M. P., Fox, B. W., Chao, P.-H., Schroeder, F. C. & Sengupta, P. A neurotransmitter produced by gut bacteria modulates host sensory behaviour. Nature 583, 415–420 (2020).PubMed 
PubMed Central 

Google Scholar 
Wilson, E. O. The Insect Societies (Harvard Univ. Press, 1971).Hölldobler, B. & Wilson, E. O. The Ants (Harvard Univ. Press, 1990).Teseo, S. et al. The scent of symbiosis: gut bacteria may affect social interactions in leaf-cutting ants. Anim. Behav. 150, 239–254 (2019).
Google Scholar 
Vernier, C. L. et al. The gut microbiome defines social group membership in honey bee colonies. Sci. Adv. 6, eabd3431 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, L. et al. Gut microbiome drives individual memory variation in bumblebees. Nat. Commun. 12, 6588 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Choi, S. H. et al. Individual variations lead to universal and cross-species patterns of social behavior. Proc. Natl Acad. Sci. USA 117, 31754–31759 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Geffre, A. C. et al. Honey bee virus causes context-dependent changes in host social behavior. Proc. Natl Acad. Sci. USA 117, 10406–10413 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kwong, W. K. & Moran, N. A. Gut microbial communities of social bees. Nat. Rev. Microbiol. 14, 374–384 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bonilla-Rosso, G. & Engel, P. Functional roles and metabolic niches in the honey bee gut microbiota. Curr. Opin. Microbiol. 43, 69–76 (2018).CAS 
PubMed 

Google Scholar 
Raymann, K. & Moran, N. A. The role of the gut microbiome in health and disease of adult honey bee workers. Curr. Opin. Insect Sci. 26, 97–104 (2018).PubMed 
PubMed Central 

Google Scholar 
Zheng, H., Powell, J. E., Steele, M. I., Dietrich, C. & Moran, N. A. Honeybee gut microbiota promotes host weight gain via bacterial metabolism and hormonal signaling. Proc. Natl Acad. Sci. USA 114, 4775–4780 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kešnerová, L. et al. Disentangling metabolic functions of bacteria in the honey bee gut. PLoS Biol. 15, e2003467 (2017).PubMed 
PubMed Central 

Google Scholar 
Kešnerová, L. et al. Gut microbiota structure differs between honeybees in winter and summer. ISME J. 14, 801–814 (2020).PubMed 

Google Scholar 
Mersch, D. P., Crespi, A. & Keller, L. Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340, 1090–1093 (2013).CAS 
PubMed 

Google Scholar 
Stroeymeyt, N. et al. Social network plasticity decreases disease transmission in a eusocial insect. Science 362, 941–945 (2018).CAS 
PubMed 

Google Scholar 
Kao, A. B. & Couzin, I. D. Modular structure within groups causes information loss but can improve decision accuracy. Philos. Trans. R. Soc. Lond. B 374, 20180378 (2019).
Google Scholar 
de Groot, A. P. Protein and amino acid requirements of the honeybee (Apis mellifica L.). Physiol. Comp. Oecol. 3, 197–285 (1953).
Google Scholar 
Billard, J.-M. d-Amino acids in brain neurotransmission and synaptic plasticity. Amino Acids 43, 1851–1860 (2012).CAS 
PubMed 

Google Scholar 
Marcaggi, P. & Attwell, D. Role of glial amino acid transporters in synaptic transmission and brain energetics. Glia 47, 217–225 (2004).PubMed 

Google Scholar 
Gage, S. L., Calle, S., Jacobson, N., Carroll, M. & DeGrandi-Hoffman, G. Pollen alters amino acid levels in the honey bee brain and this relationship changes with age and parasitic stress. Front. Neurosci. 14, 231 (2020).PubMed 
PubMed Central 

Google Scholar 
Kawase, T. et al. Gut microbiota of mice putatively modifies amino acid metabolism in the host brain. Br. J. Nutr. 117, 775–783 (2017).CAS 
PubMed 

Google Scholar 
Socha, E., Koba, M. & Koslinski, P. Amino acid profiling as a method of discovering biomarkers for diagnosis of neurodegenerative diseases. Amino Acids 51, 367–371 (2019).CAS 
PubMed 

Google Scholar 
Tarlungeanu, D. C. et al. Impaired amino acid transport at the blood brain barrier is a cause of autism spectrum disorder. Cell 167, 1481–1494 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Maynard, T. M. & Manzini, M. C. Balancing act: maintaining amino acid levels in the autistic brain. Neuron 93, 476–479 (2017).CAS 
PubMed 

Google Scholar 
Kurochkin, I. et al. Metabolome signature of autism in the human prefrontal cortex. Commun. Biol. 2, 234 (2019).PubMed 
PubMed Central 

Google Scholar 
van der Velpen, V. et al. Systemic and central nervous system metabolic alterations in Alzheimer’s disease. Alzheimer’s Res. Ther. 11, 93 (2019).
Google Scholar 
Aldana, B. I. et al. Glutamate–glutamine homeostasis is perturbed in neurons and astrocytes derived from patient iPSC models of frontotemporal dementia. Mol. Brain 13, 125 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Galizia, C. G., Eisenhardt, D. & Giurfa M. (eds) Honeybee Neurobiology and Behavior: A Tribute to Randolf Menzel (Springer Science & Business Media, 2011).Menzel, R. The honeybee as a model for understanding the basis of cognition. Nat. Rev. Neurosci. 13, 758–768 (2012).CAS 
PubMed 

Google Scholar 
Ellegaard, K. M. & Engel, P. Genomic diversity landscape of the honey bee gut microbiota. Nat. Commun. 10, 446 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Bruno, F., Angilica, A., Cosco, F., Luchi, M. L. & Muzzupappa, M. Mixed prototyping environment with different video tracking techniques. In IMProVe 2011 International Conference on Innovative Methods in Product Design (eds Concheri, G. et al.) 105–113 (Libreria Internazionale Cortina Padova, 2011).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Anderson, K. E., Rodrigues, P. A. P., Mott, B. M., Maes, P. & Corby-Harris, V. Ecological succession in the honey bee gut: shift in Lactobacillus strain dominance during early adult development. Microb. Ecol. 71, 1008–1019 (2016).CAS 
PubMed 

Google Scholar 
Almasri, H., Liberti, J., Brunet, J. L., Engel, P. & Belzunces, L. P. Mild chronic exposure to pesticides alters physiological markers of honey bee health without perturbing the core gut microbiota. Sci. Rep. 12, 4281 (2022).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfaffl, M. W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).Gallup, J. M. in PCR Troubleshooting and Optimization: The Essential Guide (eds Kennedy, S. & Oswald, N.) 23–65 (Caister Academic Press, 2011).Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet. J. 17, 10–12 (2011).
Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
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 

Google Scholar 
Davis, N. M., Proctor, D. M., Holmes, S. P., Relman, D. A. & Callahan, B. J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 6, 1–14 (2018).
Google Scholar 
Patassini, S. et al. Identification of elevated urea as a severe, ubiquitous metabolic defect in the brain of patients with Huntington’s disease. Biochem. Biophys. Res. Commun. 468, 161–166 (2015).CAS 
PubMed 

Google Scholar 
Gonzalez-Riano, C., Garcia, A. & Barbas, C. Metabolomics studies in brain tissue: a review. J. Pharm. Biomed. Anal. 130, 141–168 (2016).CAS 
PubMed 

Google Scholar 
Belle, J. E. L., Harris, N. G., Williams, S. R. & Bhakoo, K. K. A comparison of cell and tissue extraction techniques using high-resolution 1H-NMR spectroscopy. NMR Biomed. 15, 37–44 (2002).PubMed 

Google Scholar 
Wanichthanarak, K., Jeamsripong, S., Pornputtapong, N. & Khoomrung, S. Accounting for biological variation with linear mixed-effects modelling improves the quality of clinical metabolomics data. Comput. Struct. Biotechnol. J. 17, 611–618 (2019).PubMed 
PubMed Central 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).CAS 
PubMed 

Google Scholar 
Wallberg, A. et al. A hybrid de novo genome assembly of the honeybee, Apis mellifera, with chromosome-length scaffolds. BMC Genomics 20, 275 (2019).PubMed 
PubMed Central 

Google Scholar 
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).CAS 
PubMed 

Google Scholar 
Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).PubMed 
PubMed Central 

Google Scholar 
Durinck, S., Spellman, P. T., Birney, E. & Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4, 1184–1191 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Falcon, S. & Gentleman, R. Using GOstats to test gene lists for GO term association. Bioinformatics 23, 257–258 (2007).CAS 
PubMed 

Google Scholar 
Reijnders, M. J. & Waterhouse, R. M. Summary visualisations of gene ontology terms with GO-Figure! Front. Bioinform. 1, 638255 (2021).
Google Scholar  More

Flombaum, P. et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. PNAS 110, 9824–9829 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Honda, D. & Yokota, A. Detection of seven major evolutionary lineages in cyanobacteria based on the 165 rRNA gene sequence analysis with new sequences of five marine Synechococcus strains. J Mol Evol 48, 723–739 (1999).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Robertson, B. R., Tezuka, N. & Watanabe, M. M. Phylogenetic analyses of Synechococcus strains (cyanobacteria) using sequences of 16S rDNA and part of the phycocyanin operon reveal multiple evolutionary lines and reflect phycobilin content. Int. J. Syst. Evol. Microbiol. 51, 861–871 (2001).CAS 
PubMed 
Article 

Google Scholar 
Stomp, M. et al. Adaptive divergence in pigment composition promotes phytoplankton biodiversity. Nature 432, 104–107 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Albrecht, M., Pröschold, T. & Schumann, R. Identification of Cyanobacteria in a eutrophic coastal lagoon on the Southern Baltic Coast. Front. Microbiol. 8, 1–16 (2017).Article 

Google Scholar 
Bertos-Fortis, M. et al. Unscrambling cyanobacteria community dynamics related to environmental factors. Front. Microbiol. 7, 625 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature 532, 465–470 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hunter-Cevera, K. R. et al. Seasons of syn. Limnol. Oceanogr. 65, 1–18 (2019).
Google Scholar 
Kuosa, H. Picoplanktonic algae in the northern Baltic Sea: Seasonal dynamics and flagellate grazing. Mar. Ecol. Prog. Ser. 73, 269–276 (1991).ADS 
Article 

Google Scholar 
Sathicq, M. B., Unrein, F. & Gómez, N. Recurrent pattern of picophytoplankton dynamics in estuaries around the world: The case of Río de la Plata. Mar. Environ. Res. 161, 105136 (2020).CAS 
PubMed 
Article 

Google Scholar 
Rajaneesh, K. M. & Mitbavkar, S. Factors controlling the temporal and spatial variations in Synechococcus abundance in a monsoonal estuary. Mar. Environ. Res. 92, 133–143 (2013).Article 
CAS 

Google Scholar 
Crosbie, N. D., Pöckl, M. & Weisse, T. Dispersal and phylogenetic diversity of nonmarine picocyanobacteria, inferred from 16S rRNA gene and cpcBA-intergenic spacer sequence analyses. Appl. Environ. Microbiol. 69, 5716–5721 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ernst, A., Becker, S., Wollenzien, U. I. A. & Postius, C. Ecosystem-dependent adaptive radiations ofpicocyanobacteria inferred from 16S rRNA and ITS-1 sequence analysis. Microbiology 149, 217–228 (2003).CAS 
PubMed 
Article 

Google Scholar 
Sánchez-Baracaldo, P., Handley, B. A. & Hayest, P. K. Picocyanobacterial community structure of freshwater lakes and the Baltic Sea revealed by phylogenetic analyses and clade-specific quantitative PCR. Microbiology 154, 3347–3357 (2008).PubMed 
Article 
CAS 

Google Scholar 
Hu, Y. O. O., Karlson, B., Charvet, S. & Andersson, A. F. Diversity of pico- to mesoplankton along the 2000 km salinity gradient of the Baltic Sea. Front. Microbiol. 7, 679 (2016).PubMed 
PubMed Central 

Google Scholar 
Larsson, J. et al. Picocyanobacteria containing a novel pigment gene cluster dominate the brackish water Baltic Sea. ISME J. 8, 1892–1903 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Celepli, N. et al. Meta-omic analyses of Baltic Sea cyanobacteria: Diversity, community structure and salt acclimation. Environ. Microbiol. 19, 673–686 (2017).CAS 
PubMed 
Article 

Google Scholar 
Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. 13, 116–120 (2020).ADS 
CAS 
Article 

Google Scholar 
Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: Projections with CMIP5 models. Biogeosciences 10, 6225–6245 (2013).ADS 
Article 

Google Scholar 
Cabré, A., Marinov, I. & Leung, S. Consistent global responses of marine ecosystems to future climate change across the IPCC AR5 earth system models. Clim. Dyn. 45, 1253–1280 (2015).Article 

Google Scholar 
Wang, T., Chen, X., Qin, S. & Li, J. Phylogenetic and phenogenetic diversity of Synechococcus along a yellow sea section reveal its environmental dependent distribution and co-occurrence microbial pattern. J. Mar. Sci. Eng. 9, 1018 (2021).Article 

Google Scholar 
Tai, V. & Palenik, B. Temporal variation of Synechococcus clades at a coastal Pacific Ocean monitoring site. ISME J. 3, 903–915 (2009).CAS 
PubMed 
Article 

Google Scholar 
Ahlgren, N. A. & Rocap, G. Diversity and distribution of marine Synechococcus: Multiple gene phylogenies for consensus classification and development of qPCR assays for sensitive measurement of clades in the ocean. Front. Microbiol. 3, 1–24 (2012).Article 
CAS 

Google Scholar 
Rajaneesh, K. M., Mitbavkar, S., Anil, A. C. & Sawant, S. S. Synechococcus as an indicator of trophic status in the Cochin backwaters, west coast of India. Ecol. Indic. 55, 118–130 (2015).Article 

Google Scholar 
Campbell, L. & Carpenter, E. J. Characterization of phycoerythrin-containing Synechococcus spp. populations by immunofluorescence. J. Plankton Res. 9, 1167–1181 (1987).Article 

Google Scholar 
Stomp, M. et al. Colourful coexistence of red and green picocyanobacteria in lakes and seas. Ecol. Lett. 10, 290–298 (2007).PubMed 
Article 

Google Scholar 
Callieri, C. & Stockner, J. G. Freshwater autotrophic picoplankton: A review. J. Limnol. 61, 1–14 (2002).Article 

Google Scholar 
Liu, H., Jing, H., Wong, T. H. C. & Chen, B. Co-occurrence of phycocyanin- and phycoerythrin-rich Synechococcus in subtropical estuarine and coastal waters of Hong Kong. Environ. Microbiol. Rep. 6, 90–99 (2014).PubMed 
Article 
CAS 

Google Scholar 
Haverkamp, T. et al. Diversity and phylogeny of Baltic Sea picocyanobacteria inferred from their ITS and phycobiliprotein operons. Environ. Microbiol. 10, 174–188 (2008).CAS 
PubMed 

Google Scholar 
Otero-Ferrer, J. L. et al. Factors controlling the community structure of picoplankton in contrasting marine environments. Biogeosciences 15, 6199–6220 (2018).ADS 
CAS 
Article 

Google Scholar 
Ploug, H. et al. Carbon, nitrogen and O2 fluxes associated with the cyanobacterium Nodularia spumigena in the Baltic Sea. ISME J. 5, 1549–1558 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ohlendieck, U., Stuhr, A. & Siegmund, H. Nitrogen fixation by diazotrophic cyanobacteria in the Baltic Sea and transfer of the newly fixed nitrogen to picoplankton organisms. J. Mar. Syst. 25, 213–219 (2000).Article 

Google Scholar 
Klawonn, I. et al. Untangling hidden nutrient dynamics: Rapid ammonium cycling and single-cell ammonium assimilation in marine plankton communities. ISME J. 13, 1960–1974 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lin, Y., Wang, L., Xu, K., Huang, H. & Ren, H. Algae biofilm reduces microbe-derived dissolved organic nitrogen discharges: Performance and mechanisms. Environ. Sci. Technol. 55, 6227–6238 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Berthelot, H., Bonnet, S., Camps, M., Grosso, O. & Moutin, T. Assessment of the dinitrogen released as ammonium and dissolved organic nitrogen by unicellular and filamentous marine diazotrophic cyanobacteria grown in culture. Front. Mar. Sci. https://doi.org/10.3389/fmars.2015.00080 (2015).Article 

Google Scholar 
Loick-Wilde, N. et al. De novo amino acid synthesis and turnover during N2 fixation. Limnol. Ocean. 63, 1076–1092 (2018).CAS 
Article 

Google Scholar 
Glibert, P. M. & Bronk, D. A. Release of dissolved organic nitrogen by marine diazotrophic cyanobacteria Trichodesmium spp.. Appl. Environ. Microbiol. 60, 3996–4000 (1994).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kuo, J. et al. Picoplankton dynamics and picoeukaryote diversity in a hyper-eutrophic subtropical lagoon. J. Environ. Sci. Heal. 4, 521–523 (2014).
Google Scholar 
Grébert, T. et al. Light color acclimation is a key process in the global ocean distribution of Synechococcus cyanobacteria. PNAS 115, E2010–E2019 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Urbach, E., Scanlan, D., Distel, D., Waterbury, J. & Chisholm, S. Rapid diversification of marine picophytoplankton with dissimilar light-harvesting structures inferred from sequences of Prochlorococcus and Synechococcus (cyanobacteria). J. Mol. Biol. 46, 188–201 (1998).ADS 
CAS 

Google Scholar 
Farrant, G. K. et al. Delineating ecologically significant taxonomic units from global patterns of marine picocyanobacteria. PNAS 113, E3365–E3374 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rocap, G., Distel, D. L., Waterbury, J. B. & Chisholm, S. W. Resolution of Prochlorococcus and Synechococcus ecotypes by using 16S–23S ribosomal DNA internal transcribed spacer sequences. Appl. Environ. Microbiol. 68, 1180–1191 (2002).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mazard, S., Ostrowski, M., Partensky, F. & Scanlan, D. J. Multi-locus sequence analysis, taxonomic resolution and biogeography of marine Synechococcus. Environ. Microbiol. 14, 372–386 (2012).CAS 
PubMed 
Article 

Google Scholar 
Huang, S. et al. Novel lineages of Prochlorococcus and Synechococcus in the global oceans. ISME J. 6, 285–297 (2011).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Choi, D. H. & Noh, J. H. Phylogenetic diversity of Synechococcus strains isolated from the East China Sea and the East Sea. FEMS Microbiol. Ecol. 69, 439–448 (2009).CAS 
PubMed 
Article 

Google Scholar 
Lee, M. D. et al. Marine Synechococcus isolates representing globally abundant genomic lineages demonstrate a unique evolutionary path of genome reduction without a decrease in GC content. Environ. Microbiol. 21, 1677–1686 (2019).CAS 
PubMed 
Article 

Google Scholar 
Paerl, R., Foster, R., Jenkins, B., Montoya, J. & Zehr, J. Phylogenetic diversity of cyanobacterial narB genes from various marine habitats. Environ. Microbiol. 10, 3377–3387 (2008).CAS 
PubMed 
Article 

Google Scholar 
Fuller, N. et al. Clade-specific 16S ribosomal DNA oligonucleotides reveal the predominance of a single marine Synechococcus clade throughout a stratified water column in the Red sea. Appl. Environ. Microbiol. 69, 2430–2443 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Scanlan, D. J. et al. Ecological genomics of marine Picocyanobacteria. Microbiol. Mol. Biol. Rev. 73, 249–299 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mazard, S., Wilson, W. H. & Scanlan, D. J. Dissecting the physiological response to phosphorus stress in marine Synechococcus isolates (cyanophyceae). J. Phycol. 48, 94–105 (2012).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. Synechococcus bloom in the Pearl River Estuary and adjacent coastal area –With special focus on flooding during wet seasons. Sci. Total Environ. 692, 769–783 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zwirglmaier, K. et al. Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes. Environ. Microbiol. 10, 147–161 (2008).PubMed 

Google Scholar 
Sohm, J. A. et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 10, 333–345 (2016).CAS 
PubMed 
Article 

Google Scholar 
Bunse, C. et al. High frequency multi-year variability in Baltic Sea microbial plankton stocks and activities. Front. Microbiol. 10, 1–18 (2019).Article 

Google Scholar 
Alegria Zufia, J., Farnelid, H. & Legrand, C. Seasonality of coastal picophytoplankton growth, nutrient limitation and biomass contribution. Front. Microbiol. 12, 1–13 (2021).Article 

Google Scholar 
Granéli, E., Wallström, K., Larsson, U., Granéli, W. & Elmgren, R. Nutrient limitation of primary production in the Baltic Sea Area. Ambio 19, 142–151 (1990).
Google Scholar 
Mazur-Marzec, H. et al. Occurrence of cyanobacteria and cyanotoxin in the Southern Baltic Proper. Filamentous cyanobacteria versus single-celled picocyanobacteria. Hydrobiologia 701, 235–252 (2013).CAS 
Article 

Google Scholar 
Stal, L. et al. BASIC: Baltic Sea cyanobacteria. An investigation of the structure and dynamics of water blooms of cyanobacteria in the Baltic Sea—Responses to a changing environment. Cont. Shelf Res. 23, 1695–1714 (2003).ADS 
Article 

Google Scholar 
Herlemann, D. P. et al. Transitions in bacterial communities along the 2000 km salinity gradient of the Baltic Sea. ISME J. 5, 1571–1579 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hugerth, L. W. et al. Metagenome-assembled genomes uncover a global brackish microbiome. Genome Biol. 16, 1–18 (2015).Article 
CAS 

Google Scholar 
Walve, J. & Larsson, U. Seasonal changes in Baltic Sea seston stoichiometry: The influence of diazotrophic cyanobacteria. Mar. Ecol. Prog. Ser. 407, 13–25 (2010).ADS 
CAS 
Article 

Google Scholar 
Huber, P. et al. Primer design for an accurate view of picocyanobacterial community structure by using high-throughput sequencing. Appl. Environ. Microbiol. 85, 1–17 (2019).Article 

Google Scholar 
Jiang, T. et al. Temporal and spatial variations of abundance of phycocyanin- and phycoerythrin-rich Synechococcus in Pearl River Estuary and adjacent coastal area. J. Ocean Univ. China 15, 897–904 (2016).ADS 
CAS 
Article 

Google Scholar 
Li, S. et al. Unexpected predominance of photosynthetic picoeukaryotes in shallow eutrophic lakes. J. Plankton Res. 38, 830–842 (2016).CAS 
Article 

Google Scholar 
Collos, Y. et al. Oligotrophication and emergence of picocyanobacteria and a toxic dinoflagellate in Thau lagoon, southern France. J. Sea Res. 61, 68–75 (2009).ADS 
Article 

Google Scholar 
Bec, B., Husseini-Ratrema, J., Collos, Y., Souchu, P. & Vaquer, A. Phytoplankton seasonal dynamics in a Mediterranean coastal lagoon: Emphasis on the picoeukaryote community. J. Plankton Res. 27, 881–894 (2005).CAS 
Article 

Google Scholar 
Hunter-Cevera, K. R. et al. Physiological and ecological drivers of early spring blooms of coastal phytoplankter. Science 354, 326–329 (2016).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Albertano, P., Di Somma, D. & Capucci, E. Cyanobacterial picoplankton from the central Baltic Sea: Cell size classification by image analyzed fluorescence microscopy. J. Plankton Res. 19, 1405–1416 (1997).Article 

Google Scholar 
Paulsen, M. L. et al. Synechococcus in the Atlantic gateway to the Arctic Ocean. Front. Mar. Sci. https://doi.org/10.3389/fmars.2016.00191 (2016).Article 

Google Scholar 
Felföldi, T. et al. Diversity and seasonal dynamics of the photoautotrophic picoplankton in Lake Balaton (Hungary). Aquat. Microb. Ecol. 63, 273–287 (2011).Article 

Google Scholar 
Grinienė, E., Šulčius, S. & Kuosa, H. Size-selective microzooplankton grazing on the phytoplankton in the Curonian Lagoon (SE Baltic Sea). Oceanologia 58, 292–301 (2016).Article 

Google Scholar 
Tsai, A. Y., Gong, G. C., Huang, Y. W. & Chao, C. F. Estimates of bacterioplankton and Synechococcus spp. mortality from nanoflagellate grazing and viral lysis in the subtropical Danshui River estuary. Estuar. Coast. Shelf Sci. 153, 54–61 (2015).ADS 
Article 

Google Scholar 
Camacho, A., Miracle, M. R. & Vicente, E. Which factors determine the abundance and distribution of picocyanobacteria in inland waters? A comparison among different types of lakes and ponds. Arch. Hydrobiol. 157(321), 338 (2003).
Google Scholar 
Berry, D. L. et al. Shifts in cyanobacterial strain dominance during the onset of harmful algal blooms in Florida Bay, USA. Microb. Ecol. 70, 361–371 (2015).PubMed 
Article 

Google Scholar 
Zborowsky, S. & Lindell, D. Resistance in marine cyanobacteria differs against specialist and generalist cyanophages. PNAS 116, 16899–16908 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wall, C. C., Rodgers, B. S., Gobler, C. J. & Peterson, B. J. Responses of loggerhead sponges Spechiospongia vesparium during harmful cyanobacterial blooms in a sub-tropical lagoon. Mar. Ecol. Prog. Ser. 451, 31–43 (2012).ADS 
Article 

Google Scholar 
Glibert, P. M. et al. Pluses and minuses of ammonium and nitrate uptake and assimilation by phytoplankton and implications for productivity and community composition, with emphasis on nitrogen-enriched conditions. Limnol. Oceanogr. 61, 165–197 (2016).ADS 
Article 

Google Scholar 
Herbert, R. A. Nitrogen cycling in coastal marine ecosystems. FEMS Microbiol. Rev. 23, 563–590 (1999).CAS 
PubMed 
Article 

Google Scholar 
Cai, J., Hodoki, Y. & Nakano, S. I. Phylogenetic diversity of the picocyanobacterial community from a novel winter bloom in Lake Biwa. Limnology 22, 161–167 (2021).Article 

Google Scholar 
Guyet, U. et al. Synergic effects of temperature and irradiance on the physiology of the marine Synechococcus strain WH7803. Front. Microbiol. 11, 1707 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Meier, H. E. M. et al. Ensemble modeling of the Baltic Sea ecosystem to provide scenarios for management. Ambio 43, 37–48 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Neumann, T. et al. Extremes of temperature, oxygen and blooms in the baltic sea in a changing climate. Ambio 41, 574–585 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Andersson, A. et al. Projected future climate change and Baltic Sea ecosystem management. Ambio 44, 345–356 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schmidt, K. et al. Increasing picocyanobacteria success in shelf waters contributes to long-term food web degradation. Glob. Change Biol. 26, 5574–5587 (2020).ADS 
Article 

Google Scholar 
Legrand, C. et al. Interannual variability of phyto-bacterioplankton biomass and production in coastal and offshore waters of the Baltic Sea. Ambio 44, 427–438 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Capuzzo, E. et al. A decline in primary production in the North Sea over 25 years, associated with reductions in zooplankton abundance and fish stock recruitment. Glob. Change Biol. 24, e352–e364 (2017).Article 

Google Scholar 
Valderrama, J. C. Methods of nutrient analysis. In Manual on Harmful Marine Microalgae (eds Hallagraeff, G. M. et al.) 251–268 (IOC Manuals and Guides, 1995).
Google Scholar 
Jespersen, A. M. & Christoffersen, K. Measurements of chlorophyll-a from phytoplankton using ethanol as extraction solvent. Arch. Hydrobiol. 109, 445–454 (1987).CAS 

Google Scholar 
Edler, L. Recommendations on methods for marine biological studies in the Baltic Sea. Phytoplankton and chlorophyll (Baltic Marine Biologists BMB (Sweden), 1979).HELCOM Phytoplankton Expert Group. Phytoplankton biovolume and carbon content. https://www.ices.dk/data/Documents/ENV/PEG_BVOL.zip (2013).Mostböck, S. FCSalyzer (2021).Gregory Caporaso, J. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods https://doi.org/10.1038/nmeth.f.303 (2010).Article 
PubMed 

Google Scholar 
Callahan, B. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Katoh, K., Misawa, K., Kuma, K. I. & Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crosbie, N. D., Pöckl, M. & Weisse, T. Rapid establishment of clonal isolates of freshwater autotrophic picoplankton by single-cell and single-colony sorting. J. Microbiol. Methods 55, 361–370 (2003).CAS 
PubMed 
Article 

Google Scholar 
Silva, C. S. P., Genuário, D. B., Vaz, M. G. M. V. & Fiore, M. F. Phylogeny of culturable cyanobacteria from Brazilian mangroves. Syst. Appl. Microbiol. 37, 100–112 (2014).CAS 
PubMed 
Article 

Google Scholar 
Marsan, D., Wommack, K. E. & Ravel, J. Draft genome sequence of Synechococcus sp. strain CB0101, isolated from the Chesapeake Bay estuary. Genome Announc. 2, e01111 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. R version 3.5.1. https://www.r-project.org/ (2019).Oksanen, J. et al. Package ‘vegan’ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, New York, 2016) (ISBN 978-3-319-24277-4).MATH 
Book 

Google Scholar 
Gu, Z., Eils, R. & Schlesner, M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics 32, 2847–2849 (2016).CAS 
PubMed 
Article 

Google Scholar 
Gill, A. E. Atmosphere-Ocean Dynamics (Academic Press, USA, 1982).
Google Scholar 
Li, X., Wang, Y., Li, J. & Lei, B. Physical and socioeconomic driving forces of land-use and land-cover changes: A Case Study of Wuhan City, China. Discret Dyn. Nat. Soc. 2016 (2016).Paliy, O. & Shankar, V. Application of multivariate statistical techniques in microbial ecology. Mol. Ecol. 25, 1032–1057 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

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