Ecology

Institute of Environment, Department of Biological Sciences, Florida International University, Miami, FL, USAFrederick C. Draper & Christopher BaralotoSchool of Geography, University of Leeds, Leeds, UKFrederick C. Draper, Oliver L. Phillips, Timothy R. Baker, Roel J. W. Brienen & David R. GalbraithCenter for Global Discovery and Conservation Science, Arizona State University, Tempe, AZ, USAFrederick C. Draper, Gregory P. Asner, Jason Vleminckx & Oscar J. Valverde BarrantesInstituto Nacional de Pesquisas da Amazônia (INPA), Manaus, BrazilFlavia R. C. Costa, Juliana Schietti, Fernanda Coelho de Souza, William E. Magnusson, Karina Melgaço, André B. Junqueira, Ana C. Andrade, José Luís Camargo, Flávia D. Santana, Ricardo O. Perdiz, Jessica Soares Cravo, Alberto Vicentini, Henrique Nascimento, Niro Higuchi & Thaiane Rodrigues de SousaEcology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USAGabriel Arellano & Paul E. BerryDepartamento de Ciencias Forestales, Universidad Nacional de Colombia, Medellín, ColombiaAlvaro Duque & Mauricio Sánchez SáenzDepartamento de Biología, Universidad Autónoma de Madrid, Madrid, SpainManuel J. MacíaCentro de Investigación en Biodiversidad y Cambio Global (CIBC-UAM), Universidad Autónoma de Madrid, Madrid, SpainManuel J. MacíaNaturalis Biodiversity Center, Leiden, The NetherlandsHans ter Steege & Tinde Van AndelSystems Ecology, Vrije Universiteit, Amsterdam, The NetherlandsHans ter SteegeLancaster Environment Centre, Lancaster University, Lancaster, UKErika BerenguerEnvironmental Change Institute, University of Oxford, Oxford, UKErika Berenguer & Yadvinder MalhiFaculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, Ås, NorwayJacob B. SocolarSchool of Geosciences, University of Edinburgh, Edinburgh, UKKyle G. DexterMissouri Botanical Garden, St Louis, MO, USAPeter M. Jørgensen & J. Sebastian TelloBrazilian Agricultural Research Corporation (Embrapa), Roraima, BrazilCarolina V. CastilhoUniversidad Nacional de San Antonio Abad del Cusco, Cusco, PeruAbel Monteagudo-Mendoza, Victor Chama Moscoso, Darcy Galiano Cabrera & Percy Núñez VargasDepartment of Intergrative Biology, University of California Berkeley, Berkeley, CA, USAPaul V. A. Fine & Italo MesonesDepartment of Biology, University of Turku, Turku, FinlandKalle RuokolainenInstituto de Investigaciones de la Amazonía Peruana, Iquitos, PeruEuridice N. Honorio Coronado, Nállarett Dávila, Marcos A. Rios Paredes, Jhon del Aguila Pasquel, Gerardo Flores Llampazo, Ricardo Zarate Gomez, José Reyna Huaymacari, Julio M. Grandez Rios & Cesar J. Cordova OrocheUNELLEZ-Guanare, Programa de Ciencias del Agro y el Mar, Herbario Universitario (PORT), Mesa de Cavacas, VenezuelaGerardo AymardCompensation International Progress S. A.—Ciprogress Greenlife, Bogotá, ColombiaGerardo AymardAMAP, Université de Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, FranceJulien Engel, Claire Fortunel, Jean-François Molino, Daniel Sabatier & Maxime Réjou-MéchainEnvironmental and Rural Science, University of New England, Armidale, New South Wales, AustraliaC. E. Timothy PaineINRA, UMR EcoFoG, AgroParisTech, CNRS, CIRAD, Université des Antilles, Université de Guyane, Kourou, French GuianaJean-Yves Goret & Elodie AllieCIRAD, UMR EcoFoG, Kourou, French GuianaAurelie Dourdain & Pascal PetronelliBIOMAS, Universidad de Las Américas, Quito, EcuadorJuan E. Guevara AndinoInstituto de Ecología, Herbario Nacional de Bolivia, La Paz, BoliviaLeslie Cayola Pérez, Narel Y. Paniagua Zambrana & Alfredo F. FuentesDepartamento de Biologia, Universidade Federal de Rondônia, Porto Velho, BrazilÂngelo G. ManzattoLaboratoire Evolution et Diversité Biologique (EDB) CNRS/UPS, Toulouse, FranceJerôme ChaveSchool of Geography, Earth and Environmental Sciences, University of Plymouth, Plymouth, UKSophie FausetDepartment of Ecology and Evolutionary Biology, Cornell University, Ithaca, NY, USARoosevelt Garcia VillacortaDepartment of Geography, University of Exeter, Exeter, UKTed R. FeldpauschFacultad de Ciencias Biológicas, Universidad Nacional de la Amazonía Peruana, Iquito, PeruElvis Valderamma Sandoval, Gilberto E. Navarro Aguilar, Jim Vega Arenas & Manuel FloresEstación Biológica del Jardín Botánico de Missouri, Oxapampa, PeruRodolfo Vasquez Martinez, Victor Chama Moscoso & Luis Valenzuela GamarraInstitut de Ciència i Tecnologia Ambientals, Universitat Autònoma de Barcelona, Barcelona, SpainAndré B. JunqueiraSchool of Geography & Sustainable Development, University of St Andrews, St Andrews, UKKatherine H. RoucouxDepartment of Environment and Development, Federal University of Amapá, Macapa, BrazilJosé J. de Toledo & Renato R. HilárioCentre for Tropical Environmental and Sustainability Science (TESS) and College of Marine and Environmental Sciences, James Cook University, Cairns, Queensland, AustraliaWilliam F. Laurance & Susan G. LauranceDepartment of Environmental Science and Policy, George Mason University, Fairfax, VA, USAThomas E. LovejoyInventory and Monitoring Program, National Park Service, Fredericksburg, VA, USAJames A. ComiskeySmithsonian Institution, Washington DC, USAJames A. ComiskeyDepartment of Plant Sciences, University of Cambridge, Cambridge, UKMichelle KalamandeenLiving with Lakes Centre, Laurentian University, Greater Sudbury, Ontario, CanadaMichelle KalamandeenDRGB, Instituto Nacional de Innovación Agraria (INIA), Lima, PeruCarlos A. Amasifuen GuerraHerbarium Amazonense (AMAZ), Universidad Nacional de la Amazonia Peruana, Loreto, PerúLuis A. Torres MontenegroDepartment of Ecology, Universidade de São Paulo, São Paulo, BrazilMarcelo P. PansonatoInstitute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The NetherlandsJoost F. DuivenvoordenCentro de Estudos da Biodiversidade, Universidade Federal de Roraima, Boa Vista, BrazilSidney Araújo de Sousa & Marcos Salgado VitalMuseo de Historia Natural Noel Kempff Mercado, Universidad Autónoma Gabriel Rene Moreno, Santa Cruz, BoliviaLuzmila Arroyo, Alejandro Araujo-Murakami & Germaine A. Parada GutierrezFaculdade de Ciências Agrárias, Biológicas e Sociais Aplicadas, Universidad do Estado de Mato Grosso, Nova Xavantina, BrazilBeatriz S. Marimon, Ben Hur Marimon Junior, Ricardo Keichi Umetsu & Nayane C. C. S. PrestesCentro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, BrazilFernanda Antunes CarvalhoDepartment of Ecology, Evolution and Behaviour, University of Minnesota, Minneapolis, MN, USAGabriel DamascoDepartment of Geography, University College London, London, UKMathias DisneyDepartamento de Ciencias Biológicas, Universidad de Los Andes (Colombia), Bogotá, ColombiaPablo R. Stevenson Diaz & Ana M. AldanaCentro de Ciências Biológicas e da Natureza, Universidade Federal do Acre, Rio Branco, BrazilSabina Cerruto Ribeiro, Richarlly da Costa Silva & Wenderson CastroNicholas School of the Environment, Duke University, Durham, NC, USAJohn W. TerborghIwokrama International Centre for Rainforest Conservation and Development, Georgetown, GuyanaRaquel S. ThomasSmithsonian’s National Zoo & Conservation Biology Institute, Washington DC, USAFrancisco DallmeierInstituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, ColombiaAdriana PrietoUniversidade Federal Rural da Amazônia—UFRA/CAPES, Belém, BrazilRafael P. SalomãoMuseu Paraense Emílio Goeldi, Belém, BrasilRafael P. Salomão, Ima C. Guimarães Vieira & Antonio S. LimaLaboratorio de Ecología de Bosques Tropicales y Primatología, Fundación Natura Colombia, Universidad de Los Andes, Bogotá, ColombiaLuisa F. CasasFacultad de Forestales, Universidad Nacional de la Amazonía Peruana, Iquito, PeruFredy Ramirez ArevaloInstitute of Research for Forestry Development, Universidad de los Andes, Merida, VenezuelaHirma Ramírez-Angulo, Emilio Vilanova Torre & Armando Torres-LezamaSchool of Environmental and Forest Sciences (SEFS), University of Washington, Seattle, WA, USAEmilio Vilanova TorreUniversidad Regional Amazónica Ikiam, Tena, EcuadorMaria C. PeñuelaAgteca-Amazonica, Santa Cruz, BoliviaTimothy J. KilleenUniversidad Autónoma del Beni, Riberalta, BoliviaGuido Pardo & Vincent VosInstituto Amazónico de Investigaciones (IMANI), Universidad Nacional de Colombia, Sede Amazonia, BrazilEliana Jimenez-RojasBroward County Parks and Recreation, Miami, FL, USAJohn PipolyBiological Sciences, Florida Atlantic University-Davie, Miami, FL, USAJohn PipolyMuseu Universitário, Universidade Federal do Acre, Rio Branco, BrazilMarcos SilveraFacultad de Ingeniería Ambiental, Universidad Estatal Amazónica, Puyo, EcuadorDavid NeillDepartment of Biology, Washington University in St Louis, St Louis, MO, USADilys M. VelaNational Institute for Space Research (INPE), São José dos Campos, BrazilLuiz E. O. C. AragãoGeoinformática & Sistemas (GeoIS), Quito, EcuadorRodrigo SierraSchool of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ, USAOphelia WangDepartment of Geography and the Environment, University of Texas at Austin, Austin, TX, USAKenneth R. YoungInstituto de Ciência e Tecnologia, São Paulo State University (UNESP), São José dos Campos, BrazilKlécia G. MassiSchool of Anthropology and Conservation, University of Kent, Canterbury, UKMiguel N. AlexiadesUniversidade Federal do Amazonas, Manaus, BrazilFabrício BaccaroHerbario Alfredo Paredes (QAP), Universidad Central del Ecuador, Quito, EcuadorCarlos CéronSchool of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, UKAdriane Esquivel MuelbertDepartment of Life Sciences, Imperial College London, London, UKJonathan L. LloydScience and Education, The Field Museum, Chicago, IL, USANigel C. A. PitmanUniversidad Tecnica del Norte, Herbario Nacional del Ecuador, Quito, EcuadorWalter PalaciosResearch Institute Alexander von Humboldt, Bogotá, ColombiaSandra PatiñoF.C.D. and C.B. conceived the study. F.C.D., G.P.A. and C.B. designed the study with input from F.R.C.C., G. Arellano, O.L.P. and H.t.S. F.C.D. and J.B.S. performed the analysis with input from C.B., G.P.A., G. Arellano, O.L.P., A. Duque, F.C.d.S. and K.D. F.C.D. wrote the manuscript with input from C.B., F.R.C.C., G. Arellano, O.L.P., A. Duque, M.J.M., G.P.A. and H.t.S. All other coauthors contributed data and had the opportunity to comment on the manuscript. More

Experimental design and crop establishmentA microcosm experiment was conducted at Yamagata University, Tsuruoka City, Japan, from May to October 2019, with six growth containers (36 cm in height, 30 cm in width, 60 cm in length) to simulate paddy fields of 0.18 m2 in area (see Supplementary Fig. S1). The experiment was laid out in a completely randomized design with three replications of two treatments: (1) rice cropping under CSI and (2) conventional rice cultivation fertilized with mineral fertilisers and irrigated with tap water (Control).Each container was filled with 32 kg of a paddy soil collected from an experimental field in the university farm and transplanted with four hills of 30-day-old seedlings (Oryza sativa L., cv. Bekoaoba) on 27th May 2019. The experiment was performed in accordance with relevant guidelines and regulations for research involving plants. The experimental soil was classified as loamy soil (air-dried, 20% moisture) with the following basic properties: pH (H2O) of 5.78, electrical conductivity (EC) of 0.09 dS m−1, SOM of 4.9%, and a total N, P, and K of 1.46, 0.88, and 3.17 g kg−1, respectively. The TWW used in the CSI system was collected from a local WWTP and monitored weekly for its basic properties (Table 2) following our previous studies6,7. In brief, pH, EC, and DO of water samples were measured on-site using pH/conductivity and DO portable meters (D-54 and OM-51, HORIBA, Ltd., Kyoto, Japan), whereas TOC and total N were analyzed using a TOC analyzer (TOC-VCSV, Shimadzu Corp., Kyoto, Japan) attached to a total N measuring unit (TNM-1, Shimadzu Corp., Kyoto, Japan). After a standard acid-digestion of water samples6, the concentration of P was measured using a portable colorimeter (DR/890, HATCH, USA), and the concentration of K was measured using an inductively coupled plasma mass spectrometry (ICP-MS ELAN DRCII, PerkinElmer Japan Co., Ltd.). The tap water used in this study was also tested on a regular basis and found to be stable throughout the crop season, with the following properties: pH of 7.8, EC of 0.095 dS m−1, DO, TOC, N, and P of 6.85, 0.49, 0.06, and 0.07 mg L−1, respectively, with K being below the ICP-MS detection limit ( More

1.Collatz, G. J., Berry, J. A. & Clark, J. S. Effects of climate and atmospheric CO2 partial pressure on the global distribution of C4 grasses: present, past, and future. Oecologia 114, 441–454 (1998).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.von Fischer, J. C., Tieszen, L. L. & Schimel, D. S. Climate controls on C3 vs. C4 productivity in North American grasslands from carbon isotope composition of soil organic matter. Glob. Chang. Biol. 14, 1141–1155 (2008).ADS 
Article 

Google Scholar 
3.Sage, R. F., Wedin, D. A. & Li, M. The biogeography of C4 photosynthesis: patterns and controlling factors. in C4 plant biology (eds Rowan F. Sage & Russel K. Monson) 313–373 (Academic Press, 1999).4.Kellogg, E. A. Evolutionary history of the grasses. Plant Physiol. 125, 1198–1205 (2001).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Sage, R. F. A portrait of the C4 photosynthetic family on the 50th anniversary of its discovery: species number, evolutionary lineages, and hall of fame. J Exp. Bot. 68, 11–28 (2016).
Google Scholar 
6.Sage, R. F., Sage, T. L. & Kocacinar, F. Photorespiration and the evolution of C4 photosynthesis. Ann. Rev. Plant. Biol. 63, 19–47 (2012).CAS 
Article 

Google Scholar 
7.Sayed, O. H. Crassulacean Acid Metabolism 1975–2000, a Check List. Photosynthetica 39, 339–352 (2001).CAS 
Article 

Google Scholar 
8.Andrews, J. T. & Lorimer, G. H. Rubisco: structure, mechanisms, and prospects for improvement. in The Biochemistry of Plants: A Comprehensive Treatise Vol. 10 (eds MD Haleh & NK Boardman) 132–207 (Academic Press, 1987).9.Ogren, W. L. Photorespiration: pathways, regulation, and modification. Annu. Rev. Plant. Physiol. 35, 415–442 (1984).CAS 
Article 

Google Scholar 
10.Walker, B. J., VanLoocke, A., Bernacchi, C. J. & Ort, D. R. The costs of photorespiration to food production now and in the future. Annu. Rev. Plant. Biol. 67, 107–129 (2016).CAS 
PubMed 
Article 

Google Scholar 
11.Dusenge, M. E., Duarte, A. G. & Way, D. A. Plant carbon metabolism and climate change: elevated CO2 and temperature impacts on photosynthesis, photorespiration and respiration. New Phytol. 221, 32–49 (2019).CAS 
PubMed 
Article 

Google Scholar 
12.Winter, K. Ecophysiology of constitutive and facultative CAM photosynthesis. J Exp. Bot. 70, 6495–6508 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
13.Edwards, E. J. & Still, C. J. Climate, phylogeny and the ecological distribution of C4 grasses. Ecol. Lett. 11, 266–276 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Hasegawa, S. et al. Elevated CO2 concentrations reduce C4 cover and decrease diversity of understorey plant community in a Eucalyptus woodland. J Ecol. 106, 1483–1494 (2018).CAS 
Article 

Google Scholar 
15.Wittmer, M. H. O. M., Auerswald, K., Bai, Y., Schaufele, R. & Schnyder, H. Changes in the abundance of C3/C4 species of Inner Mongolia grassland: evidence from isotopic composition of soil and vegetation. Glob. Chang. Biol. 16, 605–616 (2010).ADS 
Article 

Google Scholar 
16.Winslow, J. C., Hunt, E. R. Jr & Piper, S. C. The influence of seasonal water availability on global C3 versus C4 grassland biomass and its implications for climate change research. Ecol. Model. 163, 153–173 (2003).CAS 
Article 

Google Scholar 
17.Haveles, A. W., Fox, D. L. & Fox-Dobbs, K. Carbon isoscapes of rodent diets in the Great Plains USA deviate from regional gradients in C4 grass abundance due to a preference for C3 plant resources. Palaeogeogr. Palaeoclimatol. Palaeoecol. 527, 53–66 (2019).Article 

Google Scholar 
18.Haddad, N. M. et al. Plant species loss decreases arthropod diversity and shifts trophic structure. Ecol. Lett. 12, 1029–1039 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
19.Warne, R. W., Pershall, A. D. & Wolf, B. O. Linking precipitation and C3–C4 plant production to resource dynamics in higher‐trophic‐level consumers. Ecology 91, 1628–1638 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Griffith, D. M. et al. Biogeographically distinct controls on C3 and C4 grass distributions: merging community and physiological ecology. Glob. Ecol. Biogeogr. 24, 304–313 (2015).Article 

Google Scholar 
21.Still, C. J., Cotton, J. M. & Griffith, D. M. Assessing earth system model predictions of C4 grass cover in North America: From the glacial era to the end of this century. Glob. Ecol. Biogeogr. 28, 145–157 (2019).Article 

Google Scholar 
22.Griffith, D. M., Cotton, J. M., Powell, R. L., Sheldon, N. D. & Still, C. J. Multi-century stasis in C3 and C4 grass distributions across the contiguous United States since the industrial revolution. J Biogeogr. 44, 2564–2574 (2017).Article 

Google Scholar 
23.Hattersley, P. The distribution of C3 and C4 grasses in Australia in relation to climate. Oecologia 57, 113–128 (1983).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Chang. Biol. 26, 119–188 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Sage, R. F., Sage, T. L., Pearcy, R. W. & Borsch, T. The taxonomic distribution of C4 photosynthesis in Amaranthaceae sensu stricto. Am J Bot 94, 1992–2003 (2007).PubMed 
Article 

Google Scholar 
26.Murphy, B. P. & Bowman, D. M. Seasonal water availability predicts the relative abundance of C3 and C4 grasses in Australia. Glob. Ecol. Biogeogr. 16, 160–169 (2007).Article 

Google Scholar 
27.White, A. et al. AUSPLOTS rangelands survey protocols manual. (The University of Adelaide Press, 2012).28.Sparrow, B. D. et al. A vegetation and soil survey method for surveillance monitoring of rangeland environments. Front. Ecol. Evol. 8 (2020).29.Orians, G. H. & Milewski, A. V. Ecology of Australia: the effects of nutrient‐poor soils and intense fires. Biol. Rev. 82, 393–423 (2007).PubMed 
Article 

Google Scholar 
30.Sparrow, B. et al. Our capacity to tell an Australian ecological story. in Biodiversity and Environmental Change: Monitoring, Challenges and Direction 51–84 (CSIRO Publishing Collingwood, Victoria, 2014).31.Thackway, R. & Cresswell, I. An Interim Biogeographic Regionalisation for Australia: a framework for establishing the national system of reserves, Version 4.0. (Australian Nature Conservation Agency, Canberra, 1995).32.Tokmakoff, A., Sparrow, B., Turner, D. & Lowe, A. AusPlots Rangelands field data collection and publication: Infrastructure for ecological monitoring. Future Gener. Comp. Sy. 56, 537–549 (2016).Article 

Google Scholar 
33.R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2019).34.Guerin, G. et al. ausplotsR: TERN AusPlots analysis package. https://cran.r-project.org/web/packages/ausplotsR/index.html (2020).35.Munroe, S. et al. ausplotsR: An R package for rapid extraction and analysis of vegetation and soil data collected by Australia’s Terrestrial Ecosystem Research Network. Preprint at https://ecoevorxiv.org/25phx/ (2020).36.Osborne, C. P. et al. A global database of C4 photosynthesis in grasses. New Phytol. 204, 441–446 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Watson, L., & Dallwitz, M. J. The Families of Flowering Plants: Descriptions, Illustrations, Identification, and Information Retrieval. http://www1.biologie.uni-hamburg.de/b-online/delta/angio/index.htm (1992).38.Kohn, M. J. Carbon isotope compositions of terrestrial C3 plants as indicators of (paleo)ecology and (paleo)climate. PNAS 107, 19691–19695 (2010).ADS 
CAS 
PubMed 
Article 

Google Scholar 
39.O’Leary, M. H. Carbon isotopes in photosynthesis. Bioscience 38, 328–336 (1988).Article 

Google Scholar 
40.Winter, K., Holtum, J. A. M. & Smith, J. A. C. Crassulacean acid metabolism: a continuous or discrete trait? New Phytol. 208, 73–78 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
41.Winter, K. & Holtum, J. A. How closely do the δ13C values of crassulacean acid metabolism plants reflect the proportion of CO2 fixed during day and night? Plant Physiol. 129, 1843–1851 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Cernusak, L. A. et al. Environmental and physiological determinants of carbon isotope discrimination in terrestrial plants. New Phytol. 200, 950–965 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Winter, K. & Holtum, J. A. M. Facultative crassulacean acid metabolism (CAM) plants: powerful tools for unravelling the functional elements of CAM photosynthesis. J Exp. Bot. 65, 3425–3441 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Bloom, A. J. & Troughton, J. H. High productivity and photosynthetic flexibility in a CAM plant. Oecologia 38, 35–43 (1979).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Hancock, L. P., Holtum, J. A. M. & Edwards, E. J. The evolution of CAM photosynthesis in Australian Calandrinia reveals lability in C3+ CAM phenotypes and a possible constraint to the evolution of strong CAM. Integr. Comp. Biol. 59, 517–534 (2019).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
46.Guralnick, L. J., Cline, A., Smith, M. & Sage, R. F. Evolutionary physiology: the extent of C4 and CAM photosynthesis in the genera Anacampseros and Grahamia of the Portulacaceae. J Exp. Bot. 59, 1735–1742 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Munroe, S. et al. The Photosynthetic Pathways of Plant Species surveyed in TERN Ecosystem Surveillance Plots. Terrestrial Ecosystem Research Network (TERN) https://doi.org/10.25901/k61f-yz90 (2020).48.Sage, R. F. The evolution of C4 photosynthesis. New Phytol. 161, 341–370 (2004).CAS 
Article 

Google Scholar 
49.Keeley, J. E. & Rundel, P. W. Evolution of CAM and C4 carbon-concentrating mechanisms. Int. J Plant Sci. 164, S55–S77 (2003).CAS 
Article 

Google Scholar 
50.Wang, R. & Ma, L. Climate-driven C4 plant distributions in China: divergence in C4 taxa. Sci. Rep. 6, 27977 (2016).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
51.Stowe, L. G. & Teeri, J. A. The geographic distribution of C4 species of the Dicotyledonae in relation to climate. Am. Nat. 112, 609–623 (1978).Article 

Google Scholar 
52.Pyankov, V. I., Gunin, P. D., Tsoog, S. & Black, C. C. C4 plants in the vegetation of Mongolia: their natural occurrence and geographical distribution in relation to climate. 123, 15-31 (2000).53.Guralnick, L. J., Edwards, G., Ku, M. S., Hockema, B. & Franceschi, V. Photosynthetic and anatomical characteristics in the C4–crassulacean acid metabolism-cycling plant Portulaca grandiflora. Funct. Plant Biol. 29, 763–773 (2002).CAS 
PubMed 
Article 

Google Scholar 
54.Winter, K., Sage, R. F., Edwards, E. J., Virgo, A. & Holtum, J. A. M. Facultative crassulacean acid metabolism in a C3–C4 intermediate. J Exp. Bot. 70, 6571–6579 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Coplen, T. B. et al. New guidelines for δ13C measurements. Anal. Chem. 78, 2439–2441 (2006).CAS 
PubMed 
Article 

Google Scholar 
56.Skrzypek, G. Normalization procedures and reference material selection in stable HCNOS isotope analyses: an overview. Anal. Bioanal. Chem. 405, 2815–2823 (2013).CAS 
PubMed 
Article 

Google Scholar 
57.Ke, L., Lin, Z. & Guoxing, Z. Study of normalization method of isotopic compositions to isotope reference scales. J Chem. Pharmaceut. Res 6, 1 (2014).
Google Scholar 
58.Harwood, T. et al. 9s climatology for continental Australia 1976–2005: Summary variables with elevation and radiative adjustment, version 3. Commonwealth Scientific and Industrial Research Organisation (CSIRO) https://doi.org/10.4225/08/5afa9f7d1a552 (2016).59.Viscarra Rossel, R. et al. Soil and Landscape Grid National Soil Attribute Maps – pH – CaCl2 (3” resolution), version 3. Commonwealth Scientific and Industrial Research Organisation (CSIRO) https://doi.org/10.4225/08/546F17EC6AB6E (2014).60.Besnard, G. et al. Phylogenomics of C4 photosynthesis in sedges (Cyperaceae): multiple appearances and genetic convergence. Mol. Biol. Evol. 26, 1909–1919 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Bohley, K. et al. Phylogeny of Sesuvioideae (Aizoaceae)–Biogeography, leaf anatomy and the evolution of C4 photosynthesis. Perspect. Plant Ecol. Evol. Syst. 17, 116–130 (2015).Article 

Google Scholar 
62.Bruhl, J. J. & Wilson, K. L. Towards a comprehensive survey of C3 and C4 photosynthetic pathways in Cyperaceae. Aliso 23, 99–148 (2007).Article 

Google Scholar 
63.Caddy-Retalic, S. Quantifying responses of ecological communities to bioclimatic gradients PhD thesis, University of Adelaide, School of Biological Sciences (2017).64.Carolin, R., Jacobs, S. & Vesk, M. The chlorenchyma of some members of the Salicornieae (Chenopodiaceae). Aust. J. Bot. 30, 387–392 (1982).Article 

Google Scholar 
65.Clayton, W. D., Vorontsova, M. S., Harman, K. T. & Williamson, H. World Grass Species: Synonymy. http://www.kew.org/data/grasses-syn.html (2002).66.D’andrea, R. M., Andreo, C. S. & Lara, M. V. Deciphering the mechanisms involved in Portulaca oleracea (C4) response to drought: metabolic changes including crassulacean acid‐like metabolism induction and reversal upon re‐watering. Physiol. Plant. 152, 414–430 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
67.Ehleringer, J. R. & Monson, R. K. Evolutionary and ecological aspects of photosynthetic pathway variation. Annu. Rev. Ecol. Evol. Syst. 24, 411–439 (1993).Article 

Google Scholar 
68.Feodorova, T. A., Voznesenskaya, E. V., Edwards, G. E. & Roalson, E. H. Biogeographic patterns of diversification and the origins of C4 in Cleome (Cleomaceae). Syst. Bot. 35, 811–826 (2010).Article 

Google Scholar 
69.Guillaume, K., Huard, M., Gignoux, J., Mariotti, A. & Abbadie, L. Does the timing of litter inputs determine natural abundance of 13C in soil organic matter? Insights from an African tiger bush ecosystem. Oecologia 127, 295–304 (2001).ADS 
CAS 
PubMed 
Article 

Google Scholar 
70.Herppich, W. B. & Herppich, M. Ecophysiological investigations on plants of the genus Plectranthus (Fam. Lamiaceae) native to Yemen and southern Africa. Flora 191, 401–408 (1996).Article 

Google Scholar 
71.Holtum, J. A. et al. Australia lacks stem succulents but is it depauperate in plants with crassulacean acid metabolism (CAM)? Curr. Opin. Plant Biol. 31, 109–117 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Holtum, J. A., Hancock, L. P., Edwards, E. J. & Winter, K. Facultative CAM photosynthesis (crassulacean acid metabolism) in four species of Calandrinia, ephemeral succulents of arid Australia. Photosynth. Res. 134, 17–25 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
73.Horn, J. W. et al. Evolutionary bursts in Euphorbia (Euphorbiaceae) are linked with photosynthetic pathway. Evolution 68, 3485–3504 (2014).CAS 
Article 

Google Scholar 
74.Kadereit, G., Borsch, T., Weising, K. & Freitag, H. Phylogeny of Amaranthaceae and Chenopodiaceae and the evolution of C4 photosynthesis. Int. J. Plant Sci. 164, 959–986 (2003).CAS 
Article 

Google Scholar 
75.Koch, K. E. & Kennedy, R. A. Crassulacean acid metabolism in the succulent C4 dicot, Portulaca oleracea L under natural environmental conditions. Plant. Physiol. 69, 757–761 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
76.Madhusudana Rao, I., Swamy, P. M. & Das, V. S. R. Some characteristics of crassulacean acid metabolism in five nonsucculent scrub species under natural semiarid conditions. Zeitschrift für Pflanzenphysiologie 94, 201–210 (1979).Article 

Google Scholar 
77.Metcalfe, C. R. Anatomy of the monocotyledons. 1. Gramineae. (Clarendon Press, 1960).78.Pate, J. S., Unkovich, M. J., Erskine, P. D. & Stewart, G. R. Australian mulga ecosystems –13C and 15N natural abundances of biota components and their ecophysiological significance. Plant Cell Environ. 21, 1231–1242 (1998).CAS 
Article 

Google Scholar 
79.Schmidt, S. & Stewart, G. δ15N values of tropical savanna and monsoon forest species reflect root specialisations and soil nitrogen status. Oecologia 134, 569–577 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
80.Taylor, S. H. et al. Ecophysiological traits in C3 and C4 grasses: a phylogenetically controlled screening experiment. New Phytol. 185, 780–791 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Thiede, J. & Eggli, U. Crassulaceae. in Flowering Plants· Eudicots 83–118 (Springer, 2007).82.Ting, I. P. Photosynthesis of arid and subtropical succulent plants. Aliso 12, 387–406 (1989).Article 

Google Scholar 
83.Watson, L., & Dallwitz, M. J. The grass genera of the world: descriptions, illustrations, identification, and information retrieval; including synonyms, morphology, anatomy, physiology, phytochemistry, cytology, classification, pathogens, world and local distribution, and references. https://www.delta-intkey.com/grass/intro.htm (1992).84.Winter, K., Garcia, M., Virgo, A. & Holtum, J. A. Operating at the very low end of the crassulacean acid metabolism spectrum: Sesuvium portulacastrum (Aizoaceae). J. Exp. Bot. 70, 6561–6570 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

1.Bullock, J. M., Kenward, R. E. & Hails, R. S. Dispersal Ecology (Blackwell, 2002).
Google Scholar 
2.Dieckmann, U., O’Hara, B. & Weisser, W. The evolutionary Ecology Of Dispersal. Trends Ecol. Evol. 14, 88–90 (1999).Article 

Google Scholar 
3.Allendorf, F. W. & Luikart, G. Conservation and the Genetics of Populations (Blackwell Publishing, 2009).
Google Scholar 
4.Vøllestad, L. A. et al. Small-scale dispersal and population structure in stream-living brown trout (Salmo trutta) inferred by mark–recapture, pedigree reconstruction, and population genetics. Can. J. Fish. Aquat. Sci. 69, 1513–1524 (2012).Article 

Google Scholar 
5.Edelsparre, A. H., Shahid, A. & Fitzpatrick, M. J. Habitat connectivity is determined by the scale of habitat loss and dispersal strategy. Ecol. Evol. 8, 5508–5514 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
6.Filipe, A. F. et al. Forecasting fish distribution along stream networks: Brown trout (Salmo trutta) in Europe. Divers. Distrib. 19, 1059–1071 (2013).Article 

Google Scholar 
7.Cote, J., Clobert, J., Brodin, T., Fogarty, S. & Sih, A. Personality-dependent dispersal: Characterization, ontogeny and consequences for spatially structured populations. Philos. Trans. R. Soc. B. 365, 4065–4076 (2010).CAS 
Article 

Google Scholar 
8.Cote, J. et al. Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. Ecography 40, 56–73 (2017).Article 

Google Scholar 
9.Comte, L. & Olden, J. D. Fish dispersal in flowing waters: A synthesis of movement- and genetic-based studies. Fish Fish. 19, 1063–1077 (2018).Article 

Google Scholar 
10.Ducatez, S. et al. Inter-individual variation in movement: is there a mobility syndrome in the large white butterfly Pieris brassicae?. Ecol. Entomol. 37, 377–385 (2012).Article 

Google Scholar 
11.Clobert, E., Le Galliard, J. F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).PubMed 
Article 

Google Scholar 
12.Stevens, V. M. et al. Dispersal syndromes and the use of life-histories to predict dispersal. Evol. Appl. 6, 630–642 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
13.Daufresne, M., Capra, H. & Gaudin, P. Downstream displacement of post emergent brown trout: Effects of development stage and water velocity. J. Fish Biol. 67, 599–614 (2005).Article 

Google Scholar 
14.Chapman, D. W. Aggressive behaviour in juvenile coho salmon as a cause of emigration. J. Fish. Res. Bd. Can. 19, 1047–1079 (1962).Article 

Google Scholar 
15.McCarthy, I. D. Competitive ability is related to metabolic asymmetry in juvenile rainbow trout. J. Fish Biol. 59, 1002–1014 (2001).Article 

Google Scholar 
16.Metcalfe, N. B., Taylor, A. C. & Thorpe, J. E. Metabolic rate, social status and life-history strategies in Atlantic salmon. Anim. Behav. 49, 431–436 (1995).Article 

Google Scholar 
17.Lahti, K., Huuskonen, H., Laurila, A. & Piironen, J. Metabolic rate and aggressiveness between Brown Trout populations. Funct. Ecol. 16, 167–174 (2002).Article 

Google Scholar 
18.Fraser, D. J., Weir, L. K., Darwish, T. L., Eddington, J. D. & Hutchings, J. A. Divergent compensatory growth responses within species: Linked to contrasting migrations in salmon?. Oecologia 153, 543–553 (2007).PubMed 
Article 
ADS 

Google Scholar 
19.Swain, D. P. & Holtby, L. B. Differences in morphology and agonistic behaviour in coho salmon (Oncorhynchus kisutch) rearing in a lake or its tributary stream. Can. J. Fish. Aquat. Sci. 46, 1406–1414 (1989).Article 

Google Scholar 
20.Kaiser, A., Merckx, T. & Van Dyck, H. Personality traits influence contest outcome, and vice versa, in a territorial butterfly. Sci. Rep. 9, 2778 (2019).PubMed 
PubMed Central 
Article 
ADS 
CAS 

Google Scholar 
21.Studds, C. E., Kyser, T. K. & Marra, P. P. Natal dispersal driven by environmental conditions interacting across the annual cycle of a migratory songbird. Proc. Natl. Acad. Sci. 105, 2929–2933 (2008).CAS 
PubMed 
Article 
ADS 

Google Scholar 
22.Behr, D. M., McNutt, J. W., Ozgul, A. & Cozzi, G. When to stay and when to leave? Proximate causes of dispersal in an endangered social carnivore. J. Anim. Ecol. 89, 2356–2366. https://doi.org/10.1111/1365-2656.13300 (2020).Article 
PubMed 

Google Scholar 
23.Keenleyside, M. H. & Yamamoto, F. T. Territorial behaviour of juvenile Atlantic salmon (Salmo salar L.). Behaviour 19, 139–169 (1962).Article 

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

Google Scholar 
25.Peery, C. A. & Bjornn, T. C. Dispersal of hatchery-reared chinook salmon parr following release into four Idaho streams. N. Am. J. Fish. Manag. 20, 19–27 (2000).Article 

Google Scholar 
26.Nagata, M. & Irvine, J. R. Differential dispersal patterns of male and female masu salmon fry. J. Fish Biol. 51, 601–606 (1997).Article 

Google Scholar 
27.Li, X.-Y. & Kokko, H. Sex-biased dispersal: A review of the theory. Biol. Rev. 94, 721–736. https://doi.org/10.1111/brv.12475 (2019).Article 
PubMed 

Google Scholar 
28.Hutchings, J. A. & Gerber, L. Sex-biased dispersal in a salmonid fish. Proc. R. Soc. B. 269, 2487–2493. https://doi.org/10.1098/rspb.2002.2176 (2002).Article 
PubMed 

Google Scholar 
29.Bekkevold, D., Hansen, M. M. & Mensberg, K.-L.D. Genetic detection of sex-specific dispersal in historical and contemporary populations of anadromous brown trout Salmo trutta. Mol. Ecol. 13, 1707–1712. https://doi.org/10.1111/j.1365-294X.2004.02156.x (2004).CAS 
Article 
PubMed 

Google Scholar 
30.Biro, P. A. & Stamps, J. A. Do consistent individual differences in metabolic rate promote consistent individual differences in behavior?. Trends Ecol. Evol. 25, 653–659 (2010).PubMed 
Article 

Google Scholar 
31.Le Galliard, J. F., Paquet, M., Cisel, M. & Montes-Poloni, L. Personality and the pace-of-life syndrome: Variation and selection on exploration, metabolism and locomotor performances. Funct. Ecol. 27, 136–144 (2013).Article 

Google Scholar 
32.Cano, J. M. & Nicieza, A. G. Temperature, metabolic rate, and constraints on locomotor performance in ectotherm vertebrates. Funct. Ecol. 20, 464–470 (2006).Article 

Google Scholar 
33.Van Leeuwen, T. E., Rosenfeld, J. S. & Richards, J. G. Adaptive tradeoffs in juvenile salmonid metabolism associated with habitat partitioning between coho salmon and steelhead trout in coastal streams. J. Anim. Ecol. 80, 1012–1023 (2011).PubMed 
Article 

Google Scholar 
34.Sundström, L. F., Petersson, E., Höjesjö, J., Johnsson, J. I. & Järvi, T. Hatchery selection promotes boldness in newly hatched brown trout (Salmo trutta): Implications for dominance. Behav. Ecol. 15, 192–198 (2004).Article 

Google Scholar 
35.Clobert, J., Ims, R. A. & Rousset, F. Causes, mechanisms and consequences of dispersal. In Ecology, Genetics and Evolution of Metapopulations (eds Hanski, I. & Gaggiotti, O. E.) 307–336 (Elsevier Academic Press, 2004).
Google Scholar 
36.Bohlin, T., Sundström, L. F., Johnsson, J. I., Höjesjö, J. & Pettersson, J. Density-dependent growth in brown trout: Effects of introducing wild and hatchery fish. J. Anim. Ecol. 71, 683–692 (2002).Article 

Google Scholar 
37.Gerking, S. D. The restricted movement of fish populations. Biol. Rev. 34, 221–242 (1959).Article 

Google Scholar 
38.Rodríguez, M. A. Restricted movement in stream fish: The paradigm is incomplete, not lost. Ecology 83, 1–13 (2002).Article 

Google Scholar 
39.Sánchez-González, J.-R. & Nicieza, A. G. Phenotypic convergence of artificially reared and wild trout is mediated by shape plasticity. Ecol. Evol. 7, 5922–5929 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
40.Webb, P. W. Body form, locomotion and foraging in aquatic vertebrates. Am. Zool. 24, 107–120 (1984).Article 

Google Scholar 
41.Blake, R. W. Fish functional design and swimming performance. J. Fish Biol. 65, 1193–1222 (2010).Article 

Google Scholar 
42.Lowe, W. H. What drives long-distance dispersal? A test of theoretical predictions. Ecology 90, 1456–1462 (2009).PubMed 
Article 

Google Scholar 
43.Nicieza, A. G. Morphological variation between geographically disjunct populations of Atlantic salmon: The effects of ontogeny and habitat shift. Funct. Ecol. 9, 448–456 (1995).Article 

Google Scholar 
44.Billman, E. J. et al. Body morphology differs in wild juvenile Chinook salmon Oncorhynchus tshawytscha that express different migratory phenotypes in the Willamette River, Oregon, USA. J. Fish Biol. 85, 1097–1110. https://doi.org/10.1111/jfb.12482 (2014).CAS 
Article 
PubMed 

Google Scholar 
45.Langerhans, R. B. & Reznick, D. N. Ecology and Evolution of Swimming Performance in Fishes: Predicting Evolution with Biomechanics. Fish Locomotion: An Ethoecological Perspective (Science Publishers, 2010).
Google Scholar 
46.Cano-Barbacil, C. et al. Key factors explaining critical swimming speed in freshwater fish: A review and statistical analysis for Iberian species. Sci. Rep. 10, 18947. https://doi.org/10.1038/s41598-020-75974-x (2020).CAS 
Article 
PubMed 
PubMed Central 
ADS 

Google Scholar 
47.Johnsson, J. I., Nöbbelin, F. & Bohlin, T. Territorial competition among wild brown trout fry: effects of ownership and body size. J. Fish Biol. 54, 469–472 (1999).Article 

Google Scholar 
48.Deverill, J. I., Adams, C. E. & Bean, C. W. Prior residence, aggression and territory acquisition in hatchery-reared and wild brown trout. J. Fish Biol. 55, 868–875 (1999).Article 

Google Scholar 
49.Weiss, S. & Schmutz, S. Performance of hatchery-reared brown trout and their effects on wild fish in two small Austrian streams. Trans. Am. Fish. Soc. 128, 302–316 (1999).Article 

Google Scholar 
50.Fagan, W. F. Connectivity, fragmentation, and extinction risk in dendritic metapopulations. Ecology 83, 3243–3249 (2002).Article 

Google Scholar 
51.Álvarez, D. & Nicieza, A. G. Is metabolic rate a reliable predictor of growth and survival of brown trout (Salmo trutta) in the wild?. Can. J. Fish. Aquat. Sci. 62, 643–649 (2005).Article 

Google Scholar 
52.Álvarez, D., Cano, J. M. & Nicieza, A. G. Microgeographic variation in metabolic rate and energy storage of brown trout: Countergradient selection or thermal sensitivity?. Evol. Ecol. 20, 345–363 (2006).Article 

Google Scholar 
53.Valentin, A. E. et al. Arching effect on fish body shape in geometric morphometric studies. J. Fish Biol. 73, 623–638 (2008).Article 

Google Scholar 
54.Leblanc, C. A. & Noakes, D. L. Visible Iiplant elastomer (VIE) tags for marking small rainbow trout. N. Am. J. Fish. Manag. 32, 716–719 (2012).Article 

Google Scholar 
55.Rohlf, F. J. tpsDig2: A Program Digitize Landmarks and Outlines (Springer, 2013).
Google Scholar 
56.Zelditch, M. L., Swiderski, D. L., Sheets, H. D. & Fink, W. L. Geometric Morphometrics for Biologists: A Primer (Elsevier Academic Press, 2004).
Google Scholar 
57.Rohlf, F. J. tpsRelw: Relative Warps Analysis (Spinger, 2013).
Google Scholar 
58.Horton, R. E. Erosional development of streams and their drainage basins; hydrophysical approach to quantitative morphology. Bull. Geol. Soc. Am. 56, 275–370 (1945).Article 

Google Scholar 
59.Fraser, N. H. C., Metcalfe, N. B. & Thorpe, J. E. Temperature-dependent switch between diurnal and nocturnal foraging in salmon. Proc. R. Soc. B Biol. Sci. 252, 135–139 (1993).Article 
ADS 

Google Scholar 
60.Contor, C. R. & Griffith, J. S. Nocturnal emergence of juvenile rainbow trout from winter concealment relative to light intensity. Hydrobiologia 299, 179–183 (1995).Article 

Google Scholar 
61.Závorka, L., Aldvén, D., Näslund, J., Höjesjö, J. & Johnsson, J. I. Inactive trout come out at night: Behavioral variation, circadian activity, and fitness in the wild. Ecology 97, 2223–2231 (2016).PubMed 
Article 

Google Scholar 
62.Lyon, J. P. et al. Efficiency of electrofishing in turbid lowland rivers: Implications for measuring temporal change in fish populations. Can. J. Fish. Aquat. Sci. 71, 878–886 (2014).Article 

Google Scholar 
63.Clark, P. J. & Evans, F. C. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35, 445–453 (1954).Article 

Google Scholar 
64.Baddeley, A. & Turner, R. spatstat: An R package for analyzing spatial point patterns. J. Stat. Softw. 12, 1–42 (2005).Article 

Google Scholar 
65.Baddeley, A., Rubak, E. & Turner, R. Spatial Point Patterns: Methodology and Applications with R (Chapman & Hall/CRC Press Book, 2015).
Google Scholar 
66.R Development Core Team. R: A Language and Environment for Statistical Computing. R version 4.0.2: “Taking Off Again”. (2020).67.Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002). https://doi.org/10.1007/978-0-387-21706-268.Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Statist. Soc. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
69.Benjamini, Y. Discovering the false discovery rate. J. R. Stat. Soc. B 57, 405–416 (2010).MathSciNet 
MATH 
Article 

Google Scholar 
70.Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: A practical information-theoretic approach. (Springer, 2002).
Google Scholar 
71.Symonds, M. R. E. & Moussalli, A. A brief guide to model selection, multimodel inference and model averaging in behavioural ecology using Akaike’s information criterion. Behav. Ecol. Sociobiol. 65, 13–21 (2011).Article 

Google Scholar 
72.Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: Some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35 (2011).Article 

Google Scholar 
73.Richards, S. A., Whittingham, M. J. & Stephens, P. A. Model selection and model averaging in behavioural ecology: The utility of the IT-AIC framework. Behav. Ecol. Sociobiol. 65, 77–89 (2011).Article 

Google Scholar  More

1.Murray, N. J. et al. The global distribution and trajectory of tidal flats. Nature 565(7738), 222 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
2.Bu, N. S. et al. Reclamation of coastal salt marshes promoted carbon loss from previously-sequestered soil carbon pool. Ecol. Eng. 81, 335–339 (2015).Article 

Google Scholar 
3.Cui, B. S., He, Q., Gu, B. H., Bai, J. H. & Liu, X. H. China’s coastal wetlands: understanding environmental changes and human impacts for management and conservation. Wetlands 36(Suppl 1), S1–S9 (2016).Article 

Google Scholar 
4.Cao, Z. Q. et al. Heavy metal pollution and the risk from tidal flat reclamation in coastal areas of Jiangsu, China. Mar. Pollut. Bull. 158, 111427 (2020).CAS 
PubMed 
Article 

Google Scholar 
5.Yin, A. J. et al. Salinity evolution of coastal soils following reclamation and intensive usage, Eastern China. Environ. Earth Sci. 75, 1281 (2016).Article 
CAS 

Google Scholar 
6.Wang, W., Liu, H., Li, Y. Q. & Su, J. L. Development and management of land reclamation in China. Ocean Coast. Manage. 102, 415–425 (2014).Article 

Google Scholar 
7.Laffoley, D. & Grimsditch, G. The Management of Natural Coastal Carbon Sinks (IUCN, 2009).
Google Scholar 
8.Cheong, S. et al. Coastal adaptation with ecological engineering. Nat. Clim. Change 3, 787–791 (2013).ADS 
Article 

Google Scholar 
9.Yang, W. et al. Seawall construction alters soil carbon and nitrogen dynamics and soil microbial biomass in an invasive Spartina alterniflora salt marsh in eastern China. Appl. Soil Ecol. 110, 1–11 (2017).ADS 
CAS 
Article 

Google Scholar 
10.Ding, L. J., Su, J. Q., Li, H., Zhu, Y. G. & Cao, Z. H. Bacterial succession along a long-term chronosequence of paddy soil in the Yangtze River Delta, China. Soil Biol. Biochem. 104, 59–67 (2017).CAS 
Article 

Google Scholar 
11.Zhang, H. et al. Changes in surface soil organic/inorganic carbon concentrations and their driving forces in reclaimed coastal tidal flats. Geoderma 352, 150–159 (2019).ADS 
CAS 
Article 

Google Scholar 
12.Han, G. X. et al. Agricultural reclamation effects on ecosystem CO2 exchange of a coastal wetland in the Yellow River Delta. Agr. Ecosyst. Environ. 196, 187–198 (2014).Article 

Google Scholar 
13.Hargreaves, S. K. & Hofmockel, K. S. Physiological shifts in the microbial community drive changes in enzyme activity in a perennial agroecosystem. Biogeochemistry 117, 67–79 (2014).CAS 
Article 

Google Scholar 
14.Ramirez, K. S., Lauber, C. L., Knight, R., Bradford, M. A. & Fierer, N. Consistent effects of nitrogen fertilization on soil bacterial communities in contrasting systems. Ecology 91, 3463–3470 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Rousk, J., Brookes, P. C. & Bååth, E. The microbial PLFA composition as affected by pH in an arable soil. Soil Biol. Biochem. 42, 516–520 (2010).CAS 
Article 

Google Scholar 
16.Kamble, P. N., Gaikwad, V. B., Kuchekar, S. R. & Bååth, E. Microbial growth, biomass, community structure and nutrient limitation in high pH and salinity soils from Pravaranagar (India). Eur. J. Soil Biol. 65, 87–95 (2014).CAS 
Article 

Google Scholar 
17.Gao, Y. C. et al. Effects of salinization and crude oil contamination on soil bacterial community structure in the Yellow River Delta region, China. Appl. Soil Ecol. 86, 165–173 (2015).Article 

Google Scholar 
18.Placella, S. A., Brodie, E. L. & Firestone, M. K. Rainfall – induced carbon oxide pulses results from sequential resuscitation of phylogenetically cluster microbial groups. Proc. Natl. Acad. Sci. 109, 10931–10936 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Yuan, Y. et al. Responses of microbial community structure to land-use conversion and fertilization in southern China. Eur. J. Soil Biol. 70, 1–6 (2015).ADS 
Article 

Google Scholar 
20.Iost, S., Landgraf, D. & Makeschin, F. Chemical soil properties of reclaimed marsh soil from Zhejiang Province P.R. China. Geoderma 142, 245–250 (2007).ADS 
CAS 
Article 

Google Scholar 
21.Yang, W. et al. Shift in soil organic carbon and nitrogen pools in different reclaimed lands following intensive coastal reclamation on the coasts of eastern China. Sci. Rep. 9, 5921 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
22.Assefa, D. et al. Deforestation and land use strongly effect soil organic carbon and nitrogen stock in Northwest Ethiopia. Catena 153, 89–99 (2017).CAS 
Article 

Google Scholar 
23.Chen, G. X., Gao, D. Z., Wang, Z. P. & Zeng, C. S. Contents of carbon, nitrogen and phosphorus in sediments in aquaculture ponds for different reclamation years in Shanyutan wetlands and its pollution risk assessment. Wetland Sci. 15, 309–314 (2017).
Google Scholar 
24.Whitting, G. J. & Chanton, J. P. Greenhouse carbon balance of wetlands: methane emission versus carbon sequestration. Tellus B. 53, 521–528 (2001).ADS 

Google Scholar 
25.Wissing, L. et al. Management-induced organic carbon accumulation in paddy soils: the role of organo-mineral associations. Soil Tillage. Res. 126, 60–71 (2013).Article 

Google Scholar 
26.Xing, W. L., Cheng, X. R., Xiong, J., Yuan, H. J. & Yu, M. K. Variations in soil biological properties in poplar plantations along coastal reclamation stages. Appl. Soil Ecol. 154, 103649 (2020).Article 

Google Scholar 
27.Grybos, M., Davranche, M., Gruau, G., Petitjean, P. & Pedrot, M. Increasing pH drives organic matter solubilization from wetland soils under reducing conditions. Geoderma 154, 13–19 (2009).ADS 
CAS 
Article 

Google Scholar 
28.Krishnamoorthy, R., Kim, K., Kim, C. & Sa, T. Changes of arbuscular mycorrhizal traits and community structure with respect to soil salinity in a coastal reclamation land. Soil Biol. Biochem. 72, 1–10 (2014).CAS 
Article 

Google Scholar 
29.Chodak, M., Gołębiewski, M., Morawska-Płoskonka, J., Kuduk, K. & Niklińska, M. Diversity of microorganisms from forest soils differently polluted with heavy metals. Appl. Soil Ecol. 64, 7–14 (2013).Article 

Google Scholar 
30.Peay, K. G., Baraloto, C. & Fine, P. V. Strong coupling of plant and fungal community structure across western Amazonian rainforests. ISME J. 7, 1852–1861 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Santonja, M. et al. Plant litter mixture partly mitigates the negative effects of extended drought on soil biota and litter decomposition in a Mediterranean oak forest. J. Ecol. 105, 801–815 (2017).Article 

Google Scholar 
32.Yang, W. et al. Response of the soil microbial community composition and biomass to a short-term Spartina alterniflora invasion in a coastal wetland of eastern China. Plant Soil 408, 443–456 (2016).CAS 
Article 

Google Scholar 
33.Anderson, C. R. et al. Biochar induced soil microbial community change: Implications for biogeochemical cycling of carbon, nitrogen and phosphorus. Pedobiologia 54, 309–320 (2011).CAS 
Article 

Google Scholar 
34.Mavi, M. S. & Marschner, P. Salinity affects the response of soil microbial activity and biomass to addition of carbon and nitrogen. Soil Res. 51, 68–75 (2013).CAS 
Article 

Google Scholar 
35.Xie, X. F. et al. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol. Indic. 120, 106925 (2021).CAS 
Article 

Google Scholar 
36.Mohammad, M. J., Malkawi, H. I. & Shibli, R. Effects of arbuscular mycorrhizal fungi and phosphorus fertilization on growth and nutrient uptake of barley grown on soils with different levels of salts. J. Plant Nutr. 26, 125–137 (2003).CAS 
Article 

Google Scholar 
37.Cui, X. C., Hu, J. L., Wang, J. J., Yang, J. S. & Lin, X. G. Reclamation negatively influences arbuscular mycorrhizal fungal community structure and diversity in coastal saline-alkaline land in Eastern China as revealed by Illumina sequencing. Appl. Soil Ecol. 98, 140–149 (2016).Article 

Google Scholar 
38.Guo, X. & Gong, J. Differential effects of abiotic factors and host plant traits on diversity and community composition of root-colonizing arbuscular mycorrhizal fungi in a salt-stressed ecosystem. Mycorrhiza 24, 79–94 (2014).PubMed 
Article 

Google Scholar 
39.Yamato, M., Yagame, T., Yoshimura, Y. & Iwase, K. Effect of environmental gradient in coastal vegetation on communities of arbuscular mycorrhizal fungi associated with Ixeris repens (Asteraceae). Mycorrhiza 22, 622–630 (2012).Article 

Google Scholar 
40.Strickland, M. S. & Rousk, J. Considering fungal :bacterial dominance in soils: Methods, controls, and ecosystem implications. Soil Biol. Biochem. 42, 1385–1395 (2010).CAS 
Article 

Google Scholar 
41.Collins, C. G., Stajich, J. E., Weber, S. E., Pombubpa, N. & Diez, J. M. Shrub range expansion alters diversity and distribution of soil fungal communities across an alpine elevation gradient. Mol. Ecol. 27, 2461–2476 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
42.Yang, W. et al. Soil fungal communities vary with invasion by the exotic Spartina alternifolia Loisel. in coastal salt marshes of eastern China. Plant Soil 442, 215–232 (2019).CAS 
Article 

Google Scholar 
43.Yang, W. et al. Exotic Spartina alterniflora Loisel. invasion significantly shifts soil bacterial communities with the successional gradient of saltmarsh in eastern China. Plant Soil 449, 97–115 (2020).CAS 
Article 

Google Scholar 
44.Wang, C. et al. Responses of soil microbial community to continuous experimental nitrogen additions for 13 years in a nitrogen-rich tropical forest. Soil Biol. Biochem. 121, 103–112 (2018).CAS 
Article 

Google Scholar 
45.Högberg, M. N., Baath, E., Nordgren, A., Arnebrant, K. & Högberg, P. Contrasting effects of nitrogen availability on plant carbon supply to mycorrhizal fungi and saprotrophs: A hypothesis based on field observations in boreal forests. New Phytol. 160, 225–238 (2003).Article 
CAS 

Google Scholar 
46.Joergensen, R. G. & Wichern, F. Quantitative assessment of the fungal contribution to microbial tissue in soil. Soil Biol. Biochem. 40, 2977–2991 (2008).CAS 
Article 

Google Scholar 
47.Xu, S. Q. et al. Comparison of microbial community composition and diversity in native coastal wetlands and wetlands that have undergone long-term agricultural reclamation. Wetlands 37, 99–108 (2017).CAS 
Article 

Google Scholar 
48.Vangestel, M., Merckx, R. & Vlassak, K. Microbial biomass responses to soil drying and rewetting-the fate of fast-growing and slow-growing microorganisms in soils from different climates. Soil Biol. Biochem. 25, 109–123 (1993).Article 

Google Scholar 
49.Farrell, M. Microbial utilisation of biochar-derived carbon. Sci. Total Environ. 465, 288–297 (2013).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Luo, S. S. Aggregate-related changes in soil microbial communities under different ameliorant applications in saline-sodic soils. Geoderma 329, 108–117 (2018).ADS 
CAS 
Article 

Google Scholar 
51.Tripathi, B. M. et al. Tropical soil bacterial communities in Malaysia: pH dominates in the equatorial tropics too. Microb. Ecol. 64, 474–484 (2012).PubMed 
Article 
PubMed Central 

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

Google Scholar 
53.Huang, Y. M., Liu, D. & An, S. S. Effects of slope aspect on soil nitrogen and microbial properties in the Chinese Loess region. Catena 125, 135–145 (2015).CAS 
Article 

Google Scholar 
54.Bossio, D. A., Fleck, J. A., Scow, K. M. & Fujii, R. Alteration of soil microbial communities and water quality in restored wetlands. Soil Biol. Biochem. 38, 1223–1233 (2006).CAS 
Article 

Google Scholar 
55.Chang, E. H., Chen, C. P., Tian, G. L. & Chiu, C. Y. Replacement of natural hardwood forest with planted bamboo and cedar in a humid subtropical mountain affects soil microbial community. Appl. Soil Ecol. 124, 146–154 (2018).ADS 
Article 

Google Scholar 
56.Cao, Y. S. et al. Soil microbial community composition under Eucalyptus plantations of different age in subtropical China. Eur. J. Soil Biol. 46, 128–135 (2010).CAS 
Article 

Google Scholar 
57.Vance, E. D., Brookes, P. C. & Jenkinson, D. S. An extraction method for measuring microbial biomass C. Soil Biol. Biochem. 19, 703–707 (1987).CAS 
Article 

Google Scholar 
58.Bossio, D. A. & Scow, K. M. Impacts of carbon and flooding on soil microbial communities: Phospholipid fatty acid profiles and substrate utilization patterns. Microbial. Ecol. 35, 265–278 (1998).CAS 
Article 

Google Scholar 
59.Bååth, E. & Anderson, T. H. Comparison of soil fungal/bacterial ratios in a pH gradient using physiological and PLFA-based techniques. Soil Biol. Biochem. 35, 955–963 (2003).Article 
CAS 

Google Scholar 
60.Kourtev, P. S., Ehrenfeld, J. G. & Häggblom, M. Exotic plant species alter the microbial community structure and function in the soil. Ecology 83, 3152–3166 (2002).Article 

Google Scholar  More

1.Ryan RP, Germaine K, Franks A, Ryan DJ, Dowling DN. Bacterial endophytes: recent developments and applications. FEMS Microbiol Lett. 2008;278:1–9. https://doi.org/10.1111/j.1574-6968.2007.00918.x.Article 
PubMed 

Google Scholar 
2.Rodriguez R, White J Jr, Arnold A, Redman R. Fungal endophytes: diversity and functional roles. New Phytol. 2009;182:314–30.Article 

Google Scholar 
3.Wilson D. Endophyte: the evolution of a term, and clarification of its use and definition Oikos. 1995;73:274–6.4.Harrison JG, Griffin EA. The diversity and distribution of endophytes across biomes, plant phylogeny and host tissues: How far have we come and where do we go from here?. Environ Microbiol. 2020;22:2107–23. https://doi.org/10.1111/1462-2920.14968.Article 
PubMed 

Google Scholar 
5.Clay K, Schardl C. Evolutionary origins and ecological consequences of endophyte symbiosis with grasses. Am Nat. 2002;160:S99–S127. https://doi.org/10.1086/342161.Article 
PubMed 

Google Scholar 
6.Rudgers JA, Afkhami ME, Rúa MA, Davitt AJ, Hammer S, Huguet VM. A fungus among us: broad patterns of endophyte distribution in the grasses. Ecology. 2009;90:1531–9. https://doi.org/10.1890/08-0116.1.Article 
PubMed 

Google Scholar 
7.Clay K, Holah J. Fungal endophyte symbiosis and plant diversity in successional fields. Science. 1999;285:1742–4. https://doi.org/10.1126/science.285.5434.1742.Article 
PubMed 

Google Scholar 
8.Afkhami ME, Strauss SY. Native fungal endophytes suppress an exotic dominant and increase plant diversity over small and large spatial scales. Ecology. 2016;97:1159–69. https://doi.org/10.1890/15-1166.1.Article 
PubMed 

Google Scholar 
9.Rudgers JA, Clay K. An invasive plant–fungal mutualism reduces arthropod diversity. Ecol Lett. 2008;11:831–40. https://doi.org/10.1111/j.1461-0248.2008.01201.x.Article 
PubMed 

Google Scholar 
10.Gorischek AM, Afkhami ME, Seifert EK, Rudgers JA. Fungal symbionts as manipulators of plant reproductive biology. Am Nat. 2013;181:562–70. https://doi.org/10.1086/669606.Article 
PubMed 

Google Scholar 
11.Malloch D, Blackwell M. Dispersal of fungal diaspores. The fungal community: Its organization and role in the ecosystem. 2nd ed. New York, NY: Marcel Dekker, Inc; 1992. p. 147–71.
Google Scholar 
12.Devarajan P, Suryanarayanan T. Evidence for the role of phytophagous insects in dispersal of non-grass fungal endophytes. Fungal Divers. 2006;23:111–9.
Google Scholar 
13.Lodge DJ, Fisher P, Sutton B. Endophytic fungi of Manilkara bidentata leaves in Puerto Rico. Mycologia. 1996;88:733–8.14.Paine RT. A note on trophic complexity and community stability. Am Nat. 1969;103:91–93. https://doi.org/10.1086/282586.Article 

Google Scholar 
15.Jenkins SH, Busher PE. Castor canadensis, Mammalian Species. 1979. https://doi.org/10.2307/3503787.16.Hajishengallis G, Darveau RP, Curtis MA. The keystone-pathogen hypothesis. Nat Rev Microbiol. 2012;10:717–25. https://doi.org/10.1038/nrmicro2873.Article 
PubMed 
PubMed Central 

Google Scholar 
17.Jousset A, Bienhold C, Chatzinotas A, Gallien L, Gobet A, Kurm V, et al. Where less may be more: How the rare biosphere pulls ecosystems strings. ISME J. 2017;11:853–62. https://doi.org/10.1038/ismej.2016.174.Article 
PubMed 
PubMed Central 

Google Scholar 
18.Hassani MA, Durán P, Hacquard S. Microbial interactions within the plant holobiont. Microbiome. 2018;6:58. https://doi.org/10.1186/s40168-018-0445-0.Article 
PubMed 
PubMed Central 

Google Scholar 
19.Rockman MV. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution. 2012;66:1–17. https://doi.org/10.1111/j.1558-5646.2011.01486.x.Article 
PubMed 

Google Scholar 
20.Beckers GJ, Conrath U. Priming for stress resistance: from the lab to the field. Curr Opin Plant Biol. 2007;10:425–31. https://doi.org/10.1016/j.pbi.2007.06.002.Article 
PubMed 

Google Scholar 
21.Hartmann A, Rothballer M, Hense BA, Schröder P. Bacterial quorum sensing compounds are important modulators of microbe-plant interactions. Front Plant Sci. 2014;5. https://doi.org/10.3389/fpls.2014.00131.22.Friesen ML, Porter SS, Stark SC, von Wettberg EJ, Sachs JL, Martinez-Romero E. Microbially mediated plant functional traits. Annu Rev Ecol Evol Syst. 2011;42:23–46. https://doi.org/10.1146/annurev-ecolsys-102710-145039.Article 

Google Scholar 
23.Hardoim PR, Van Overbeek LS, Berg G, Pirttilä AM, Compant S, Campisano A, et al. The hidden world within plants: ecological and evolutionary considerations for defining functioning of microbial endophytes. Microbiol Mol Biol Rev. 2015;79:293–320. https://doi.org/10.1128/MMBR.00050-14.Article 
PubMed 
PubMed Central 

Google Scholar 
24.Doty SL. Growth-promoting endophytic fungi of forest trees. In: Pirttilä AM, Frank AC, editors. Endophytes of forest trees: biology and applications. Dordrecht: Springer Netherlands; 2011;151–6.
Google Scholar 
25.Arnold AE, Herre EA. Canopy cover and leaf age affect colonization by tropical fungal endophytes: Ecological pattern and process in Theobroma cacao (Malvaceae). Mycologia. 2003;95:388–98. http://www.mycologia.org.unr.idm.oclc.org/content/95/3/388. Accessed 12 Dec 2016.Article 

Google Scholar 
26.Busby PE, Peay KG, Newcombe G. Common foliar fungi of Populus trichocarpa modify melampsora rust disease severity. New Phytol. 2016;209:1681–92. https://doi.org/10.1111/nph.13742.Article 
PubMed 

Google Scholar 
27.Christian N, Herre EA, Mejia LC, Clay K. Exposure to the leaf litter microbiome of healthy adults protects seedlings from pathogen damage. Proc R Soc B. 2017;284:20170641. https://doi.org/10.1098/rspb.2017.0641.Article 
PubMed 

Google Scholar 
28.Cheplick GP, Cho R. Interactive effects of fungal endophyte infection and host genotype on growth and storage in Lolium perenne. New Phytol. 2003;158:183–91. https://doi.org/10.1046/j.1469-8137.2003.00723.x.Article 

Google Scholar 
29.Zahn G, Amend AS. Foliar fungi alter reproductive timing and allocation in arabidopsis under normal and water-stressed conditions. 2019. https://www.biorxiv.org/content/10.1101/519678v1.30.Christian N, Herre EA, Clay K. Foliar endophytic fungi alter patterns of nitrogen uptake and distribution in Theobroma cacao. New Phytol. 2019;222:1573–83. https://doi.org/10.1111/nph.15693.Article 
PubMed 

Google Scholar 
31.Rosado BHP, Almeida LC, Alves LF, Lambais MR, Oliveira RS. The importance of phyllosphere on plant functional ecology: a phyllo trait manifesto. New Phytol. 2018;219:1145–9. https://doi.org/10.1111/nph.15235.Article 
PubMed 

Google Scholar 
32.Mejía LC, Herre EA, Sparks JP, Winter K, García MN, Van Bael SA, et al. Pervasive effects of a dominant foliar endophytic fungus on host genetic and phenotypic expression in a tropical tree. Front Microbiol. 2014;5:479.33.Dupont PY, Eaton CJ, Wargent JJ, Fechtner S, Solomon P, Schmid J, et al. Fungal endophyte infection of ryegrass reprograms host metabolism and alters development. New Phytol. 2015;208:1227–40. https://doi.org/10.1111/nph.13614.Article 
PubMed 
PubMed Central 

Google Scholar 
34.Dinkins RD, Nagabhyru P, Graham MA, Boykin D, Schardl CL. Transcriptome response of Lolium arundinaceum to its fungal endophyte Epichloë coenophiala. New Phytol. 2017;213:324–37. https://doi.org/10.1111/nph.14103.Article 
PubMed 

Google Scholar 
35.Welsh S, North American species of astragalus Linnaeus (Leguminosae): a taxonomic revision. Provo, Utah: Brigham Young University; 2007.36.Knaus BJ. Morphometric architecture of the most taxon-rich species in the U.S. Flora: Astragalus lentiginosus (Fabaceae). Am J Bot. 2010;97;1816–26. https://doi.org/10.3732/ajb.0900145.37.Baucom DL, Romero M, Belfon R, Creamer R. Two new species of undifilum, fungal endophytes of astragalus (locoweeds) in the United States. Botany. 2012;90:866–75. https://doi.org/10.1139/b2012-056.Article 

Google Scholar 
38.Woudenberg JHC, Groenewald JZ, Binder M, Crous PW. Alternaria redefined. Stud Mycol. 2013;75:171–212. https://doi.org/10.3114/sim0015.Article 
PubMed 
PubMed Central 

Google Scholar 
39.Cook D, Gardner DR, Martinez A, Robles CA, Pfister JA. Screening for swainsonine among South American astragalus species. Toxicon. 2017;139:54–7. https://doi.org/10.1016/j.toxicon.2017.09.014.Article 
PubMed 

Google Scholar 
40.Molyneux RJ, James LF. Loco intoxication: indolizidine alkaloids of spotted locoweed (Astragalus lentiginosus). Science. 1982;216:190–1. https://doi.org/10.1126/science.6801763.Article 
PubMed 

Google Scholar 
41.Cook D, Gardner DR, Ralphs MH, Pfister JA, Welch KD, Green BT. Swainsoninine concentrations and endophyte amounts of Undifilum oxytropis in different plant parts of Oxytropis sericea. J Chem Ecol. 2009;35:1272–8. https://doi.org/10.1007/s10886-009-9710-9.Article 
PubMed 

Google Scholar 
42.Harrison JG, Parchman TL, Cook D, Gardner DR, Forister ML. A heritable symbiont and host-associated factors shape fungal endophyte communities across spatial scales. J Ecol. 2018;106:2274–86. https://doi.org/10.1111/1365-2745.12967.Article 

Google Scholar 
43.Grum DS, Cook D, Baucom D, Mott IW, Gardner DR, Creamer R, et al. Production of the alkaloid swainsonine by a fungal endophyte in the host Swainsona canescens. J Nat Prod. 2013;76:1984–8. https://doi.org/10.1021/np400274n.44.Cook D, Gardner DR, Pfister JA. Swainsonine-containing plants and their relationship to endophytic fungi. J Agric Food Chem. 2014;62:7326–34. https://doi.org/10.1021/jf501674r.Article 
PubMed 

Google Scholar 
45.Panaccione DG, Beaulieu WT, Cook D. Bioactive alkaloids in vertically transmitted fungal endophytes. Funct Ecol. 2014;28:299–314. https://doi.org/10.1111/1365-2435.12076.Article 

Google Scholar 
46.Thompson DC, Knight JL, Sterling TM, Murray LW. Preference for specific varieties of woolly locoweed by a specialist weevil, Cleonidius trivittatus (Say). Southwest Entomol. 1995;20:325–325.
Google Scholar 
47.Parker JE. Effects of insect herbivory by the four-lined locoweed weevil, Cleonidius trivittatus (say) (Coleoptera: Curculionidae), on the alkaloid swainsonine in locoweeds Astragalus mollissimus and Oxytropis sericea. Ph.D. thesis. Las Cruces, New Mexico: New Mexico State University; 2008.48.Creamer R, Baucom D. Fungal endophytes of locoweeds: a commensal relationship? J Plant Physiol Pathol. 2013;1. https://doi.org/10.4172/2329-955X.1000104.49.Lu H, Quan H, Zhou Q, Ren Z, Xue R, Zhao B, et al. Endogenous fungi isolated from three locoweed species from rangeland in western China. Afr J Microbiol Res. 2017;11:155–70. https://doi.org/10.5897/AJMR2016.8392.Article 

Google Scholar 
50.Schulthess FM, Faeth SH. Distribution, abundances, and associations of the endophytic fungal community of Arizona fescue (Festuca arizonica). Mycologia. 1998;90:569–78. https://doi.org/10.1080/00275514.1998.12026945.Article 

Google Scholar 
51.Cook D, Gardner DR, Pfister JA, Stonecipher CA, Robins JG, Morgan JA. Effects of elevated CO2 on the swainsonine chemotypes of Astragalus lentiginosus and Astragalus mollissimus. J Chem Ecol. 2017;43:307–16. https://doi.org/10.1007/s10886-017-0820-5.Article 
PubMed 

Google Scholar 
52.Oldrup E, McLain-Romero J, Padilla A, Moya A, Gardner D, Creamer R. Localization of endophytic undifilum fungi in locoweed seed and influence of environmental parameters on a locoweed in vitro culture system. Botany. 2010;88:512–21. https://doi.org/10.1139/B10-026.Article 

Google Scholar 
53.Gardner DR, Molyneux RJ, Ralphs MH. Analysis of swainsonine: extraction methods, detection, and measurement in populations of locoweeds (oxytropis spp.). J Agric Food Chem. 2001;49:4573–80.Article 

Google Scholar 
54.Högberg P. 15N natural abundance in soil–plant systems. New Phytol. 1997;137:179–203. https://www.cambridge.org/core/journals/new-phytologist/article/tansley-review-no-95-15n-natural-abundance-in-soilplant-systems/304069FD5C8283EDB78D0AA594465E71. Accessed 2 Jul 2017.Article 

Google Scholar 
55.Wang Y, Qian P-Y. Conservative fragments in bacterial 16S rRNA genes and primer design for 16S ribosomal DNA amplicons in metagenomic studies. PLoS ONE. 2009;4:e7401. https://doi.org/10.1371/journal.pone.0007401.Article 
PubMed 
PubMed Central 

Google Scholar 
56.White TJ, Bruns T, Lee S, Taylor J. Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. In Innis MA, Glefand DH, Sninsky JJ, White TJ, editors, PCR protocols: a guide to methods and applications. London: Academic Press; 1990.57.Harrison JG, Calder WJ, Shuman B, Buerkle CA, The quest for absolute abundance: the use of internal standards for DNA-based community ecology. Mol Ecol Resour. 2020, https://doi.org/10.1111/1755-0998.13247.58.Tourlousse DM, Yoshiike S, Ohashi A, Matsukura S, Noda N, Sekiguchi Y. Synthetic spike-in standards for high-throughput 16S rRNA gene amplicon sequencing. Nucleic Acids Res. 2017;45:e23–e23. https://doi.org/10.1093/nar/gkw984.Article 
PubMed 

Google Scholar 
59.Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.Article 

Google Scholar 
60.Rognes T, Flouri T, Nichols B, Quince C, Mahé F, “VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016. https://doi.org/10.7717/peerj.2584.61.Edgar RC, UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. 2016. https://www.biorxiv.org/content/10.1101/081257v1.full.62.Callahan BJ, McMurdie PJ, Holmes SP. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017:2639. https://doi.org/10.1038/ismej.2017.119.63.Edgar R, “SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. 2016. https://www.biorxiv.org/content/10.1101/074161v1.64.Nilsson RH, Larsson KH, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, et al. The UNITE database for molecular identification of fungi: Handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 2018, https://doi.org/10.1093/nar/gky1022.65.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal database project: data and tools for high throughput rRNA analysis Nucleic Acids Res. 2014. 42, https://doi.org/10.1093/nar/gkt1244.66.Machida RJ, Leray M, Ho S-L, Knowlton N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci Data. 2017;4:17007 https://doi.org/10.1038/sdata.2017.27.Article 

Google Scholar 
67.Fordyce JA, Gompert Z, Forister ML, Nice CC. A hierarchical Bayesian approach to ecological count data: A flexible tool for ecologists. PLoS ONE. 2011;6;e26785. https://doi.org/10.1371/journal.pone.0026785.68.Harrison JG, Calder WJ, Shastry V, Buerkle CA. Dirichlet-multinomial modelling outperforms alternatives for analysis of microbiome and other ecological count data. Mol Ecol Resour. 2020;20:481–97. https://doi.org/10.1111/1755-0998.13128.Article 
PubMed 

Google Scholar 
69.R Core Team, R: a language and environment for statistical computing. Vienna: R Core Team; 2019.70.Harrison J, Shastry V, Calder WJ, Buerkle CA, “CNVRG: Dirichlet-multinomial modelling of relative abundance data.” Sep. 2020. https://CRAN.R-project.org/package=CNVRG. Accessed 28 Oct 2020.71.S. D. Team, Stan modeling language users guide and reference manual. 2020. https://mc-stan.org/users/documentation/72.S. D. Team, “RStan: The R interface to Stan. R package.” 2020. http://mc-stan.org/.73.Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7:457–72. http://www.jstor.org/stable/2246093. Accessed 16 Jun 2018.
Google Scholar 
74.Gloor GB, Macklaim JM, Vu M, Fernandes AD. Compositional uncertainty should not be ignored in high-throughput sequencing data analysis. Austrian J Stat. 2016;45:73–87. http://ajs.data-analysis.at/index.php/ajs/article/view/vol45-4-5. Accessed 4 Dec 2017.Article 

Google Scholar 
75.Jost L. Entropy and diversity. Oikos. 2006;113:363–75.Article 

Google Scholar 
76.Marion ZH, Fordyce JA, Fitzpatrick BM. A hierarchical Bayesian model to incorporate uncertainty into methods for diversity partitioning. Ecology. 2018;99:947–56. https://doi.org/10.1002/ecy.2174.Article 
PubMed 

Google Scholar 
77.Harrison JG, Gompert Z, Fordyce JA, Buerkle CA, Grinstead R, Jahner JP. et al. The many dimensions of diet breadth: phytochemical, genetic, behavioral, and physiological perspectives on the interaction between a native herbivore and an exotic host. PLoS ONE. 2016;11:e0147971. https://doi.org/10.1371/journal.pone.0147971.Article 
PubMed 
PubMed Central 

Google Scholar 
78.Plummer M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling,”. Proc 3rd Int workshop Distrib Stat Comput. 2003;124:1–8. 10.2003.
Google Scholar 
79.Plummer M. Rjags: Bayesian graphical models using MCMC. R package version 3-15. 2015. Https://CRAN.R-project.org/package=rjags.80.Breiman L. Random forests. Mach Learn. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324.Article 

Google Scholar 
81.Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;2:18–22.
Google Scholar 
82.Grum DS, Cook D, Gardner DR, Roper JM, Pfister JA, Ralphs MH. Influence of seed endophyte amounts on swainsonine concentrations in astragalus and oxytropis locoweeds. J Agric Food Chem. 2012;60:8083–9. https://doi.org/10.1021/jf3024062.Article 
PubMed 

Google Scholar 
83.Marion ZH, Fordyce JA, Fitzpatrick BM. Extending the concept of diversity partitioning to characterize phenotypic complexity. Am Nat. 2015;186:348–61.Article 

Google Scholar 
84.Arnold AE, Lutzoni F. Diversity and host range of foliar fungal endophytes: are tropical leaves biodiversity hotspots?”. Ecology. 2007;88:541–49. https://doi.org/10.1890/05-1459.Article 
PubMed 

Google Scholar 
85.Strong DR, Lawton JH, Southwood SR. Insects on plants. Community patterns and mechanisms. Oxford, UK: Blackwell Scientific Publicatons; 1984.
Google Scholar 
86.Carmona D, Lajeunesse MJ, Johnson MTJ. Plant traits that predict resistance to herbivores. Funct Ecol. 2011;25:358–67. https://doi.org/10.1111/j.1365-2435.2010.01794.x.Article 

Google Scholar 
87.Berry D, Widder S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front Microbiol. 2014;5. https://doi.org/10.3389/fmicb.2014.00219.88.Trosvik P, de Muinck EJ. Ecology of bacteria in the human gastrointestinal tract—identification of keystone and foundation taxa. Microbiome. 2015;3:44 https://doi.org/10.1186/s40168-015-0107-4.Article 
PubMed 
PubMed Central 

Google Scholar 
89.Banerjee S, Schlaeppi K, van der Heijden MGA, Keystone taxa as drivers of microbiome structure and functioning. Nat Rev Microbiol. 2018;16:567. https://doi.org/10.1038/s41579-018-0024-1.90.Braun K, Romero J, Liddell C, Creamer R. Production of swainsonine by fungal endophytes of locoweed. Mycological Res. 2003;107:980–8. https://doi.org/10.1017/S095375620300813X.Article 

Google Scholar 
91.Noor AI, Nava A, Cooke P, Cook D, Creamer R. Evidence for nonpathogenic relationships of alternaria section undifilum endophytes within three host locoweed plant species. Botany. 2018;96:187–200. https://doi.org/10.1139/cjb-2017-0117.Article 

Google Scholar 
92.Kulpa SM, Leger EA. Strong natural selection during plant restoration favors an unexpected suite of plant traits. Evolut Appl. 2013;6:510–23. https://doi.org/10.1111/eva.12038.Article 

Google Scholar 
93.Leger EA, Baughman OW. What seeds to plant in the Great Basin? Comparing traits prioritized in native plant cultivars and releases with those that promote survival in the field. Nat Areas J. 2015;35:54–68. https://doi.org/10.3375/043.035.0108.Article 

Google Scholar 
94.Klypina N, Pinch M, Schutte BJ, Maruthavanan J, Sterling TM, Water-deficit stress tolerance differs between two locoweed genera (astragalus and oxytropis) with fungal endophytes. Weed Sci. 2017:1–13. https://doi.org/10.1017/wsc.2017.21.95.Stamp N. Out of the quagmire of plant defense hypotheses. Q Rev Biol. 2003;78:23–55. https://doi.org/10.1086/367580.Article 
PubMed 

Google Scholar 
96.Eades CJ, Hintz WE. Characterization of the α-mannosidase gene family in filamentous fungi: N-glycan remodelling for the development of eukaryotic expression systems. Biotechnol Bioprocess Eng. 2000;5:227. https://doi.org/10.1007/BF02942178.Article 

Google Scholar 
97.Schmid J, Day R, Zhang N, Dupont PY, Cox MP, Schardl CL, et al. Host tissue environment directs activities of an epichloë endophyte, while it induces systemic hormone and defense responses in its native perennial ryegrass host. Mol Plant Microbe Interact. 2016;30:138–49. https://doi.org/10.1094/MPMI-10-16-0215-R.98.Zamioudis C, Pieterse CMJ. Modulation of host immunity by beneficial microbes. Mol Plant-Microbe Interact. 2011;25:139–50. https://doi.org/10.1094/MPMI-06-11-0179.Article 

Google Scholar 
99.Kannadan S, Rudgers JA. Endophyte symbiosis benefits a rare grass under low water availability. Funct Ecol. 2008;22:706–13. https://doi.org/10.1111/j.1365-2435.2008.01395.x.Article 

Google Scholar 
100.Barillas JRV, Paschke MW, Ralphs MH, Child RD. White locoweed toxicity is facilitated by a fungal endophyte and nitrogen-fixing bacteria. Ecology. 2007;88:1850–6. https://doi.org/10.1890/06-0728.1.Article 

Google Scholar 
101.Isbell F, Calcagno V, Hector A, Connolly J, Harpole WS, Reich PB, et al. High plant diversity is needed to maintain ecosystem services. Nature. 2011;477:199–202. https://doi.org/10.1038/nature10282. More

This is the first comparative study of commercial scallop species in the Pacific coast of the MP combining morphological and molecular characters. Our phylogenetic analyses highlight the association between A. natans and Ad. colbecki; two members of monospecific tribes and last extant representatives of their Southern Ocean-restricted genera.These results confirm the presence of both Magallanes scallops in the MP, as well as the so-far unsuspected presence of mixed “banks” where both species occur in sympatry. The BND/VH ratio helps discriminate between two distinct entities that belong to the genetic lineage of Z. patagonica and to a different lineage, highly divergent from the former, which corresponds to A. natans. A. natans is the only species of a whole lineage with a particular phylogenetic value, therefore having developed and tested an accurate identification criterion for both scallops will allow efficient fishery management in the future.Here we discuss the phylogenetic position and the taxonomic status of both Magallanes scallops, as well as the implications of these results for the future management and conservation of Z. patagonica and A. natans in the Magallanes Region. Despite the numerous classifications built on morphological, ecological or molecular data, the relationships among pectinids are still under constant modification depending on the number of taxa, loci, length of the sequence and the selected outgroups1,4. The work of Alejandrino et al.7 is the most inclusive so far in terms of taxon sampling, with 81 species. Although Scherrat et al.25 included 143 species, the node supports of the phylogenetic trees are not provided, making it difficult to assess the robustness of this large phylogeny. In order to define the phylogenetic position of Zygochlamys patagonica and Austrochlamys natans, we included 93 pectinid taxa (43 genera) representative of tribes Chlamydini, Crassadomini, Fortipectini, Palliolini, Aequipectinini, Pectinini and Amussini. Comparing to Waller’s5 and Dijkstra’s15 classifications, only the subfamily Camptonectinae and the tribe Mesoplepini are missing. We used three ribosomal regions (one nuclear and two mitochondrial). Compared to Alejandrino et al.7, histone H3 is missing here, however this locus is among the least informative4. The family Pectinidae appears to be monophyletic with high support values (Fig. 5, S2), as previously demonstrated4,7,26,27,28. According to Dijkstra15 there are currently five subfamilies of Pectinidae, two of which are absent from our analysis: Camptonectinae and Pedinae. This topology supports the classifications of Waller5 and Dijkstra15, except for the position of the tribe Austrochlamydini.Our Magallanes scallops separated into two very divergent clades: Z. patagonica is associated with its conspecifics and congenerics in a single lineage (Fig. 5), which also contains species of Veprichlamys and Talochlamys. This lineage already appeared well supported as the sister clade to Palliolinae and Pectininae in Alejandrino7. For the first time, Talochlamys dichroa and T. gemmulata are nested with high support values into the Zygochlamys clade, making this latter genus paraphyletic (Fig. 5). These taxa are all restricted to high latitudes of the Southern Ocean. Due to phylogenetic and geographic affinities, we suggest that these three genera may constitute a tribe separate from Chlamydini. Since Dijkstra15 moved the two Atlantic ‘Crassadoma’ into the genus Talochlamys, the affinities among Talochlamys spp. had not been explored until now. Talochlamys species rather associate according to geographic affinities, splitting the genus into two highly divergent entities corresponding to European and New Zealand Talochlamys. A systematic revision of these four species would be useful.Austrochlamys natans associated with the Palliolinae, which was elevated to a subfamily rank by Waller5. Of the three extant tribes that compose this group, Mesopleplini are missing from our phylogenetic analyses. We included 4 genera (8 species) of the remaining two tribes: Adamussium (Adamussini) and Palliolum, Pseudamussium, Placopecten (Palliolini). The present sampling of Palliolini is the most inclusive to date and led to the monophyly and full support of the tribe Palliolini. Our phylogenetic results do not support any of the previous classifications of the tribe Austrochlamydini1,5,9,13,15, and introduce this monospecific tribe as a new member of the subfamily Palliolinae. Indeed, Austrochlamys natans clusters together with Adamussium colbecki, both in a sister clade to Palliolini. The first molecular characterization of Ad. colbecki did not lead to a clear classification due to the low polymorphism of the 18S26. Later, Ad. colbecki appears either as sister species to Chlamydinae or to Palliolini, depending on tribe sampling and the choice of outgroup and loci4,10,11. However, in the most recent and inclusive studies of taxon sampling7 (present study) or genomic cover29, Ad. colbecki is the sister group of the tribe Palliolini, as in the present phylogeny.The subfamily Palliolinae originated from a Chlamydinine ancestor in the Cretaceous and subsequently underwent diversification in the Northern Hemisphere1 and in the Southern Hemisphere, where the extinct genus Lentipecten spread in the Paleocene–Eocene Thermal Maximum30. The genus Adamussium derived from Lentipecten and appeared in the early Oligocene; it comprises 5 endemic Antarctic species; Ad. colbecki is the only one extant13,31,32. The genus Austrochlamys also appeared in the Oligocene and was first restricted to King George Island (South Shetlands), then spread around the north of the Antarctic Peninsula and achieved a circum-Antarctic distribution until the Pliocene13,33,34. Austrochlamys persisted during the progressive cooling of the Antarctic Continent from the Paleocene to the Pliocene, dominating the coastal areas, while Adamussium occupied the deep seas and continental platform33. The opening and deepening of the Drake Passage and the intensification of the Antarctic Circumpolar Current during the Pliocene provoked a drastic cooling and the extension of sea ice over the coastal habitat, which caused the northward movement of Austrochlamys and its subsequent disappearance from Antarctica, along with the circumpolar expansion of Ad. colbecki in Antarctic shallow waters33. The colonization of the coastal habitat has been related to the sea ice extent that provided a more stable environment and low-energy fine-grained sediment with which Adamussium was associated in the deep waters. Austrochlamys fossils appear in the Subantarctic Heard Island in late Pliocene layers (3.62–2.5 Ma35). Today Ad. colbecki is a circum-Antarctic and eurybathic species that reaches high local density in protected locations13,36, while all Austrochlamys became extinct except for A. natans, which is restricted to southern South America33. The phylogenetic affinity highlighted here between A. natans and Ad. colbecki has its origins in the Southern Ocean; the deep divergence between the lineages of these monospecific tribes attests to the long time since their common origin in the Paleogene. These results point out both species as relevant biogeographic models to address longstanding questions regarding the origin of marine biota from Southern Ocean.The nomenclature, taxonomy and ecology of both A. natans and Z. patagonica have been problematic for almost 200 years. Since its original description37, Z. patagonica, a.k.a. the “Ostión Patagónico” has been named with more than 10 synonyms, probably due to the great intra-specific morphological variability throughout its distribution19,38 (see the nomenclatural history in Supplementary Table S1). In contrast, there are very few records in the scientific literature and no genetic data on A. natans, a.k.a. the “Ostión del Sur”13,14,17,19, and some problems of nomenclature and establishing diagnostic characters persist since its description13,39. Many of the current junior synonyms of both species were described from small and juvenile specimens (under 52 mm VH39,40,41). Indeed, all deposited type material of A. natans ranges from 23.5 to 52 mm VH; the latter is half of the maximum size39. The criteria most commonly used for the identification of both scallops were number of radial primary ribs, maximum size, shell colour and presence of laminated concentric lines (Supplementary Table S1). Specimens with marked primary and secondary radial ribs alternated regularly and more whitish colouring of the right shell were attributed to Z. patagonica, while those with weaker and less markedly coloured radial ribs and the maximum size were considered as A. natans42. However, the number of radial ribs overlaps between Z. patagonica (26–4212,43) and A. natans (22–5017,19). These characters also have high variability across different environments and during ontogeny13,17. Thus the use of a taxonomy based on environment-sensitive and allometric characters has led to confusion in the morphological identification of these species13,38. The criterion used in the present study, the BND/VH ratio established by Jonkers13, discriminates the species efficiently. As attested by the narrow dispersal cluster in Fig. 3, this character has low intra-population variability13. In some cases a level of intraspecific variation can be detected, and this is mainly due to the environments where the scallop populations inhabit19 (e.g. exposed, protected, substrate type, fjord, oceanic). However, although there may be some intraspecific variability between populations, this variability does not generate problems for the identification of the two species. Individuals of A. natans generally presented a significantly greater BND/VH ratio than those of Z. patagonica. However, it is important to consider that, given that this character varies during ontogeny, it is more accurate in individuals over 25 mm VH13. Only the molecular identification was able to discriminate juvenile scallops of both species accurately.According to the literature, A. natans is restricted to interior waters of channels and is associated with kelp forests of M. pyrifera (Supplementary Table S1). Z. patagonica inhabits a wider range of environments such as bottoms of shells, sand, mud and gravel in protected and exposed areas, between 2 and 300 m depth (Supplementary Table S1), but is also associated with kelp forests in fjords with different degrees of glacial retreat12,16,44. The juveniles of both scallops recruit in kelp forests44,45. According to the local artisanal fishermen, adults of “Ostión del Sur” (A. natans) occur in fjords with glaciers (orange circles in Fig. 123). We included two sampling locations near glaciers (in Pia and Montañas fjords), where large individuals (between 46 and 86 mm) of A. natans and Z. patagonica occur in sympatry. This sympatry was previously reported in Silva Palma Fjord between 5 and 25 m depth16. In conclusion, scallop banks are not monospecific but rather mixed and Z. patagonica occurs in the interior waters of the channels and fjords. Consequently, these two species have overlapping ecology (recruiting zone and glacial affinity) in the channels and fjords, overturning a long-held view that these scallops have marked habitat segregation.The fishery for both species was established in the 1990s in the political-administrative Region of Magallanes16, despite the complexity of the morphological recognition of scallops. The distinction between species was based on shell colour and radial ribs42, two characters that, given the results of this study, do not have this diagnostic capacity. Consequently, the scallop fisheries in the Magallanes Region are currently based on inaccurately discriminative characters. Scallop banks in MP have always been considered as monospecific16,47. A great part of scallop landing has always been attributed to A. natans47, about which the scientific literature is scarce (Supplementary Table S1). Conversely, Z. patagonica, which was erroneously considered as the commercial species of southern Chile, has more scientific research (Supplementary Table S1).The difficulty to discriminate A. natans and Z. patagonica morphologically may lead to incorrect fishery statistics and uncertain conservation status of A. natans. Incorrect fishery statistics could overestimate the abundance of banks of A. natans compared to Z. patagonica. If the minimum catch size is reduced23 in the context of the fishing overuse of the last decade, A. natans may suffer a reduction of its maximum size48. Therefore, an identification criterion between species is a need to improve fishery management. We showcased a quantified criterion that is useful to identify both species. In the short-term, this method can be used, but it is difficult to enforce in practical ways. We suggest to train fishing inspectors, following three guidelines. First, the identification should consider only the right valve (RV) for species identification, since the left valve is not taxonomically informative. Second, for visual classification, check the outline of the BN, mainly because the individuals of Z. patagonica have a more arcute BN. Third, a reliable identification has to measure the depth of the byssal notch (BND) and shell height (VH) ratio. Lastly, future research and fishery monitoring should follow these criteria to carry out a correct identification and subsequently better landings statistics.Molecular tools allowed evaluating the phylogenetic relationships of scallops globally or regionally and incorporating parameters that can be used for the management and conservation of species of commercial interest49. For example, in the last few decades metrics have been developed to address conservation problems that give us a measure of the current state of particular taxa. These conservation priorities are often seen as measures for threatened species categorized by the IUCN Red List (World Conservation Union, 1980), one of the most widely and recognized systems. Although this prioritization metric incorporates phylogenetic distinctiveness (PD), this factor has been updated due to the importance of quantifying the loss of evolutionary diversity that would be implied by the extinction of a species50. The magnitude of the PD loss from any species will depend (but not exclusively) on the fate of its close relatives51. The “Ostión del Sur”, Austrochlamys natans is the last representative of its tribe (Austrochlamydini) in the Southern Ocean. Its phylogenetic position and the long branch length (i.e. the length of the branch from the tip to where it joins the tree), which represents an important amount of evolutionary change, highlights the degree of isolation of A. natans and calls attention to the possible loss of a unique genetic lineage. There is currently no conservation value for this relict species; we sought to alert the current fishery management that the “Ostión del Sur” is a distinct taxon and provide integrative evidence for further conservation studies.Finally, regarding the overlapping niche of these scallops and the conservation importance of the clade of A. natans, we propose three key recommendations for the future scallop fishery policies in the sub-Antarctic channels. First, it is necessary to assess the proportion of both species per bank and landing to generate a distribution map through the sub-Antarctic channels. For this assessment, the byssal notch depth is the most appropriate morphological character. Second, we recommend reassessments of biological and ecological parameters (e.g. size at first maturity) for A. natans across the glacial fjords, which are the most relevant fishing sites. As a final point, today there is a complete lack of knowledge of the genetic connectivity along the Subantarctic Channels. Thus we should generate more research about spatial population genetics at different temporal scales. The integration of genomic approaches (e.g. SNPs) with macro- and micro-environmental modelling approaches provide enormous opportunity to establish a new regional zoning for fishery management and conservation scallop strategy. More