Ecology

1.Pontier, D. et al. Postnatal growth rate and adult body weight in mammals: A new approach. Oecologia 80, 390–394 (1989).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Dmitriew, C. M. The evolution of growth trajectories: What limits growth rate?. Biol. Rev. 86, 97–116 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Gotthard, K., Nylin, S. & Wiklund, C. Adaptive variation in growth rate: Life history costs and consequences in the speckled wood butterfly, Pararge aegeria. Oecologia 99, 281–289 (1994).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Arendt, J. D. Adaptive intrinsic growth rates: An integration across taxa. Q. Rev. Biol. 72, 149–177 (1997).Article 

Google Scholar 
5.Gaillard, J. M. et al. Variation in growth form and precocity at birth in eutherian mammals. Proc. R. Soc. B Biol. Sci. 264, 859–868 (1997).ADS 
CAS 
Article 

Google Scholar 
6.Gillooly, J. F., Charnov, E. L., Geoffrey, B. W., Savage, V. M. & James, H. B. Effects of size and temperature on developmental time. Nature 417, 70–73 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Brown, J. H., Gillooly, J. F., Allen, P. A., Savage, V. M. & Geoffrey, B. W. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
8.Roff, D. A. The Evolution of Life Histories: Theory and Analysis (Sinauer Associates, 1992).9.Stearns, S. C. The Evolution of Life Histories (Oxford University Press, 1992).10.Ferré, P., Decaux, J. F., Issad, T. & Girard, J. Changes in energy metabolism during the suckling and weaning period in the newborn. Reprod. Nutr. Dev. 26, 619–631 (1986).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Gadgil, M. & Bossert, W. H. Life history consequences of natural selection. Am. Nat. 104, 1–24 (1970).Article 

Google Scholar 
12.Lee, A. H., Huttenlocker, A. K., Padian, K. & Woodward, H. N. Analysis of growth rates. In Bone Histology of Fossil Tetrapods (eds Padian, K. & Lamm, E.-T.) 217–264 (University of California Press, 2013).13.Amprino, R. L. structure du tissu osseux envisagée comme expression de différences dans la vitesse de l’accroisement. Arch. Biol. (Liege) 58, 315–330 (1947).
Google Scholar 
14.Nacarino-Meneses, C. & Köhler, M. Limb bone histology records birth in mammals. PLoS One 13, 20 (2018).
Google Scholar 
15.Morris, P. A. A method for determining absolute age in the hedgehog. Notes Mammal Soc. 20, 277–280 (1970).
Google Scholar 
16.Castanet, et al. Lines of arrested growth in bone and age estimation in a small primate: Microcebus murinus. J. Zool. 263, 31–39 (2004).Article 

Google Scholar 
17.Klevezal, G. A. & Kleinenberg, S. E. Age determination of mammals by layered structures of teeth and bones. (1967).18.Barker, J. M., Boonstra, R. & Schulte-Hostedde, A. I. Age determination in yellow-pine chipmunks (Tamias amoenus): A comparison of eye lens masses and bone sections. Can. J. Zool. 81, 1774–1779 (2003).Article 

Google Scholar 
19.Amson, E., Kolb, C., Scheyer, T. M. & Sánchez-Villagra, M. R. Growth and life history of Middle Miocene deer (Mammalia, Cervidae) based on bone histology. C.R. Palevol 14, 637–645 (2015).Article 

Google Scholar 
20.Kolb, C. et al. Growth in fossil and extant deer and implications for body size and life history evolution. BMC Evol. Biol. 15, 19 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
21.de Buffrénil, V. & Pascal, M. Croissance et morphogénèse postnatales de la mandibule du vison (Mustela vison Schreiber): Données sur la dynamique et l’interprétation fonctionnelle des dépôts osseux mandibulaires. Can. J. Zool. 62, 2026–2037 (1984).Article 

Google Scholar 
22.Castanet, J., CurryRogers, K., Cubo, J. & Jacques-Boisard, J. Periosteal bone growth rates in extant ratites (ostriche and emu). Implications for assessing growth in dinosaurs. Comptes Rendus Acad. Sci. Ser. III Sci. Vie 323, 543–550 (2000).CAS 

Google Scholar 
23.Starck, J. M. & Chinsamy, A. Bone microstructure and developmental plasticity in birds and other dinosaurs. J. Morphol. 254, 232–246 (2002).PubMed 
Article 
PubMed Central 

Google Scholar 
24.de Margerie, E., Cubo, J. & Castanet, J. Bone typology and growth rate: Testing and quantifying ‘Amprino’s rule’ in the mallard (Anas platyrhynchos). Comptes Rendus Biol. 325, 221–230 (2002).Article 

Google Scholar 
25.de Margerie, E. et al. Assessing a relationship between bone microstructure and growth rate: A fluorescent labelling study in the king penguin chick (Aptenodytes patagonicus). J. Exp. Biol. 207, 869–879 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Montoya-Sanhueza, G., Bennett, N. C., Oosthuizen, M. K., Dengler-Crish, C. M. & Chinsamy, A. Bone remodeling in the longest living rodent, the naked mole-rat: Interelement variation and the effects of reproduction. J. Anat. https://doi.org/10.1111/joa.13404 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Smith, T. M. Experimental determination of the periodicity of incremental features in enamel. J. Anat. 208, 99–113 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
28.Kierdorf, H., Kierdorf, U., Frölich, K. & Witzel, C. Lines of evidence-incremental markings in molar enamel of Soay sheep as revealed by a fluorochrome labeling and backscattered electron imaging study. PLoS One 8, 20 (2013).
Google Scholar 
29.Witzel, C., Kierdorf, U., Frölich, K. & Kierdorf, H. The pay-off of hypsodonty—timing and dynamics of crown growth and wear in molars of Soay sheep. BMC Evol. Biol. 18, 1–14 (2018).Article 

Google Scholar 
30.Kahle, P., Witzel, C., Kierdorf, U., Frölich, K. & Kierdorf, H. Mineral apposition rates in coronal dentine of mandibular first molars in Soay sheep: Results of a fluorochrome labeling study. Anat. Rec. 301, 902–912 (2018).CAS 
Article 

Google Scholar 
31.van Gaalen, S. M. et al. Use of fluorochrome labels in in vivo bone tissue engineering research. Tissue Eng. Part B. Rev. 16, 209–217 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Shim, M.-J. Bone changes in femoral bone of mice using calcein labeling. Korean J. Clin. Lab. Sci. 48, 114–117 (2016).Article 

Google Scholar 
33.Klevezal, G. A. Recording Structures of Mammals (Balkema Publishers, 1996).34.Klevezal, G. A. & Mina, M. V. Tetracycline labelling as a method of field studies of individual growth and population structure in rodents. Lynx (Praha) 22, 67–78 (1984).
Google Scholar 
35.Smith, T. M., Reid, D. J. & Sirianni, J. E. The accuracy of histological assessments of dental development and age at death. J. Anat. 208, 125–138 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
36.Curtin, A. J. et al. Noninvasive histological comparison of bone growth patterns among fossil and extant neonatal elephantids using synchrotron radiation X-ray microtomography. J. Vertebr. Paleontol. 32, 939–955 (2012).Article 

Google Scholar 
37.Hugi, J. & Snchez-Villagra, M. R. Life history and skeletal adaptations in the galapagos marine iguana (Amblyrhynchus cristatus) as reconstructed with bone histological dataa comparative study of iguanines. J. Herpetol. 46, 312–324 (2012).Article 

Google Scholar 
38.Chinsamy, A. & Hurum, J. H. Bone microstructure and growth patterns of early mammals. Acta Palaeontol. Pol. 51, 325–338 (2006).
Google Scholar 
39.Teagasc. Development of the Calf Digestive System. Teagasc Calf Rearing Manual: Best Practice from Birth to Three Months 59–76 (2017).40.Warren, L. K., Lawrence, L. M., Parker, A. L., Barnes, T. & Griffin, A. S. The effect of weaning age on foal growth and radiographic bone density. J. Equine Vet. Sci. 18, 335–340 (1998).Article 

Google Scholar 
41.Holland, J. L. et al. Weaning stress is affected by nutrition and weaning methods. Pferdeheilkunde 12, 257–260 (1996).Article 

Google Scholar 
42.Enríquez, D., Hötzel, M. J. & Ungerfeld, R. Minimising the stress of weaning of beef calves: A review. Acta Vet. Scand. 53, 1–8 (2011).Article 

Google Scholar 
43.Pollard, J. C., Asher, G. W. & Littlejohn, R. P. Weaning date affects calf growth rates and hind conception dates in farmed red deer (Cervus elaphus). Anim. Sci. 74, 111–116 (2002).Article 

Google Scholar 
44.Wolter, B. F. & Ellis, M. The effects of weaning weight and rate of growth immediately after weaning on subsequent pig growth performance and carcass characteristics. Can. J. Anim. Sci. 81, 363–369 (2001).Article 

Google Scholar 
45.Pluske, J. R., Dividich, J. L. & Verstegen, M. W. A. Weaning the pig. Concepts and Consequences Weaning the Pig (Wageningen Academic Publishers, 2003). https://doi.org/10.3920/978-90-8686-513-0.46.Landete-Castillejos, T. et al. Milk production and composition in captive Iberian red deer (Cervus elaphus hispanicus): Effect of birth date. The online version of this article, along with updated information and services, is located on the World Wide Web at: Milk production. J. Anim. Sci. 78, 2771–2777 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Wang, Y., Bekhit, A. E. D. A., Morton, J. D. & Mason, S. Nutritional value of deer milk. In Nutrients in Dairy and Their Implications for Health and Disease 363–375 (2017). https://doi.org/10.1016/B978-0-12-809762-5.00028-048.Stein, K. & Prondvai, E. Rethinking the nature of fibrolamellar bone: An integrative biological revision of sauropod plexiform bone formation. Biol. Rev. 89, 24–47 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
49.Clutton-Brock, T. H., Guiness, F. E. & Albon, S. D. Red Deer: Behaviour and Ecology of Two Sexes (The University of Chicago Press, 1982). https://doi.org/10.1016/0006-3207(83)90010-1.50.Festa-bianchet, M., Jorgenson, J. T. & Réale, D. Early development, adult mass, and reproductive success in bighorn sheep. Behav. Ecol. 11, 633–639 (2000).Article 

Google Scholar 
51.Cook, J. G. et al. Effects of summer–autumn nutrition and parturition date on reproduction and survival of elk. Wildl. Monogr. 20, 1–61 (2004).
Google Scholar 
52.Moore, G. H., Littlejohn, R. P. & Cowie, G. M. Liveweights, growth rates, and mortality of farmed red deer at Invermay. N. Z. J. Agric. Res. 31, 293–300 (1988).Article 

Google Scholar 
53.Ozanne, S. E. & Hales, C. N. Poor fetal growth followed by rapid postnatal catch-up growth leads to premature death. Mech. Ageing Dev. 126, 852–854 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Van Eetvelde, M. & Opsomer, G. Innovative look at dairy heifer rearing: Effect of prenatal and post-natal environment on later performance. Reprod. Domest. Anim. 52, 30–36 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Calderón, T., DeMiguel, D., Arnold, W., Stalder, G. & Köhler, M. Calibration of life history traits with epiphyseal closure, dental eruption and bone histology in captive and wild red deer. J. Anat. 20, 205–216. https://doi.org/10.1111/joa.13016 (2019).Article 

Google Scholar 
56.Horner, J. R., De Ricqlès, A. & Padian, K. Long bone histology of the hadrosaurid dinosaur Maiasaura peeblesorum: Growth dynamics and physiology based on an ontogenetic series of skeletal elements. J. Vertebr. Paleontol. 20, 115–129 (2000).Article 

Google Scholar 
57.Padian, K., De Ricqlès, A. J. & Horner, J. R. Dinosaurian growth rates and bird-origins. Nature 412, 405–408 (2001).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Woodward, H. N., Padian, K. & Lee, A. H. Skeletochronology. In Bone Histology of Fossil Tetrapods (eds Padian, K. & Lamm, E.-T.) 195–216 (University of California Press, 2013).59.Pratt, I. V. & Cooper, D. M. L. The effect of growth rate on the three-dimensional orientation of vascular canals in the cortical bone of broiler chickens. J. Anat. 233, 531–541 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
60.Enlow, D. H. A study of the post-natal growth and remodelling of bone. Am. J. Anat. 110, 79–101 (1962).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Chinsamy-Turan, A. The Microstructure of Dinosaur Bone (The Johns Hopkins University Press, 2005).62.de Buffrénil, V. & Quilhac, A. Bone tissue types: A brief account of currently used categories. in Vertebrate Skeletal Histology and Paleohistology (eds. de Buffrénil, V., de Riclès, J. A., Zylbeberg, L. & Padian, K.) 148–192 (CRC Press, 2021).63.Padian, K., Lamm, E.-T. & Werning, S. Selection of specimens. In Bone Histology of Fossil Tetrapods (eds Padian, K. & Lamm, E.-T.) 35–54 (University of California Press, 2013).64.Montoya-Sanhueza, G., Bennett, N. C., Oosthuizen, M. K., Dengler-Crish, C. M. & Chinsamy, A. Long bone histomorphogenesis of the naked mole-rat: Histodiversity and intraspecific variation. J. Anat. https://doi.org/10.1111/joa.13381 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
65.Calderón, T., DeMiguel, D., Arnold, W., Stalder, G. & Köhler, M. Calibration of life history traits with epiphyseal closure, dental eruption and bone histology in captive and wild red deer. J. Anat. https://doi.org/10.1111/joa.13016 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
66.Prondvai, E., Stein, K. H. W., de Ricqlès, A. & Cubo, J. Development-based revision of bone tissue classification: The importance of semantics for science. Biol. J. Linn. Soc. 112, 799–816 (2014).Article 

Google Scholar 
67.Francillon-Vieillot, H. et al. Microstructural and mineralization of vertebral skeletal tissues. In Skeletal Biommineralization: Patterns, Processes and Evolutionary Trends (ed. Carter, J. G.) (Van Nostrand Reinhold, 1990).68.Montes, L. et al. Relationships between bone growth rate, body mass and resting metabolic rate in growing amniotes: A phylogenetic approach. Biol. J. Linn. Soc. 92, 63–76 (2007).Article 

Google Scholar 
69.Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.Team, Rs. RStudio: Integrated Development for R. (2019).71.Muggeo, V. M. R. Interval estimation for the breakpoint in segmented regression: A smoothed score-based approach. Aust. N. Z. J. Stat. 59, 311–322 (2017).MathSciNet 
MATH 
Article 

Google Scholar  More

Centro de Investigação em Biodiversidade e Recursos Genéticos/Research Network in Biodiversity and Evolutionary Biology, Campus Agrário de Vairão, Universidade do Porto, Vairão, PortugalNuno Queiroz, Ana Couto, Marisa Vedor, Ivo da Costa, Gonzalo Mucientes & António M. SantosMarine Biological Association of the United Kingdom, Plymouth, UKNuno Queiroz, Nicolas E. Humphries, Lara L. Sousa, Samantha J. Simpson, Emily J. Southall & David W. SimsDepartamento de Biologia, Faculdade de Ciências da Universidade do Porto, Porto, PortugalMarisa Vedor & António M. SantosUWA Oceans Institute, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSchool of Biological Sciences, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSpanish Institute of Oceanography, Santa Cruz de Tenerife, SpainFrancisco J. AbascalAbercrombie and Fish, Port Jefferson Station, NY, USADebra L. AbercrombieMarine Biology and Aquaculture Unit, College of Science and Engineering, James Cook University, Cairns, Queensland, AustraliaKatya Abrantes, Adam Barnett, Richard Fitzpatrick & Marcus SheavesInstitute of Natural and Mathematical Sciences, Massey University, Palmerston North, New ZealandDavid Acuña-MarreroUniversidade Federal Rural de Pernambuco (UFRPE), Departamento de Pesca e Aquicultura, Recife, BrazilAndré S. Afonso, Natalia P. A. Bezerra, Fábio H. V. Hazin, Fernanda O. Lana, Bruno C. L. Macena & Paulo TravassosMARE, Marine and Environmental Sciences Centre, Instituto Politécnico de Leiria, Peniche, PortugalAndré S. AfonsoMARE, Laboratório Marítimo da Guia, Faculdade de Ciências da Universidade de Lisboa, Cascais, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreInstitute of Marine Research (IMAR), Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes, Bruno C. L. Macena & Frederic VandeperreOkeanos – Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreDepartment of Environmental Affairs, Oceans and Coasts Research, Cape Town, South AfricaDarrell Anders, Michael A. Meÿer, Sarika Singh & Laurenne B. SnydersLarge Marine Vertebrates Research Institute Philippines, Jagna, PhilippinesGonzalo AraujoFins Attached Marine Research and Conservation, Colorado Springs, CO, USARandall ArauzPrograma Restauración de Tortugas Marinas PRETOMA, San José, Costa RicaRandall ArauzMigraMar, Olema, CA, USARandall Arauz, Sandra Bessudo Lion, Eduardo Espinoza, Alex R. Hearn, Mauricio Hoyos, James T. Ketchum, A. Peter Klimley, Cesar Peñaherrera-Palma, George Shillinger & German SolerInstitut de Recherche pour le Développement, UMR MARBEC (IRD, Ifremer, Univ. Montpellier, CNRS), Sète, FrancePascal Bach, Antonin V. Blaison, Laurent Dagorn, John D. Filmalter, Fabien Forget, Francois Poisson, Marc Soria & Mariana T. TolottiBiology Department, University of Massachusetts Dartmouth, Dartmouth, MA, USADiego Bernal & Heather MarshallRed Sea Research Center, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaMichael L. Berumen, Jesse E. M. Cochran & Carlos M. DuarteFundación Malpelo y Otros Ecosistemas Marinos, Bogota, ColombiaSandra Bessudo Lion, Felipe Ladino, Lina Maria Quintero & German SolerHopkins Marine Station of Stanford University, Pacific Grove, CA, USABarbara A. Block, Taylor K. Chapple, George Shillinger & Timothy D. WhiteDepartment of Biological Sciences, Florida International University, North Miami, FL, USAMark E. Bond, Demian D. Chapman & Yannis P. PapastamatiouInstituto de Ciências do Mar, Universidade Federal do Ceará, Fortaleza, BrazilRamon BonfilCSIRO Oceans and Atmosphere, Hobart, Tasmania, AustraliaRussell W. Bradford & Barry D. BruceSchool of Aquatic and Fishery Sciences, University of Washington, Seattle, WA, USACamrin D. BraunBiology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USACamrin D. Braun & Simon R. ThorroldShark Research and Conservation Program, Cape Eleuthera Institute, Eleuthera, BahamasEdward J. Brooks, Annabelle Brooks & Sean WilliamsUniversity of Exeter, Exeter, UKAnnabelle BrooksSouth Atlantic Environmental Research Institute, Stanley, Falkland IslandsJudith BrownDepartment of Biological Sciences, The Guy Harvey Research Institute, Nova Southeastern University, Dania Beach, FL, USAMichael E. Byrne, Mahmood Shivji, Jeremy J. Vaudo & Bradley M. WetherbeeSchool of Natural Resources, University of Missouri, Columbia, MO, USAMichael E. ByrneLife and Environmental Sciences, University of Iceland, Reykjavik, IcelandSteven E. CampanaSchool of Marine Science and Policy, University of Delaware, Lewes, DE, USAAaron B. Carlisle & Gregory B. SkomalMassachusetts Division of Marine Fisheries, New Bedford, MA, USAJohn ChisholmMarine Research Facility, Jeddah, Saudi ArabiaChristopher R. Clarke & James S. E. LeaPSL, Labex CORAIL, CRIOBE USR3278 EPHE-CNRS-UPVD, Papetoai, French PolynesiaEric G. CluaAgence de Recherche pour la Biodiversité à la Réunion (ARBRE), Réunion, Marseille, FranceEstelle C. CrocheletInstitut de Recherche pour le Développement, UMR 228 ESPACE-DEV, Réunion, Marseille, FranceEstelle C. CrocheletSave Our Seas Foundation–D’Arros Research Centre (SOSF-DRC), Geneva, SwitzerlandRyan Daly & Clare A. Keating DalySouth African Institute for Aquatic Biodiversity (SAIAB), Grahamstown, South AfricaRyan Daly, John D. Filmalter, Enrico Gennari & Alison A. KockDepartment of Fisheries Evaluation, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChileDaniel Devia CortésSchool of Biological, Earth and Environmental Sciences, University College Cork, Cork, IrelandThomas K. Doyle & Luke HarmanMaREI Centre, Environmental Research Institute, University College Cork, Cork, IrelandThomas K. DoyleCollege of Science and Engineering, Flinders University, Adelaide, South Australia, AustraliaMichael Drew, Matthew Heard & Charlie HuveneersDepartment of Conservation, Auckland, New ZealandClinton A. J. DuffySouth African Institute for Aquatic Biodiversity, Geological Sciences, UKZN, Durban, South AfricaThor EriksonDireccion Parque Nacional Galapagos, Puerto Ayora, Galapagos, EcuadorEduardo EspinozaAustralian Institute of Marine Science, Indian Ocean Marine Research Centre (UWA), Crawley, Western Australia, AustraliaLuciana C. Ferreira, Mark G. Meekan & Michele ThumsDepartment of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USAFrancesco FerrettiOCEARCH, Park City, UT, USAG. Chris FischerBedford Institute of Oceanography, Dartmouth, Nova Scotia, CanadaMark Fowler, Warren Joyce & Anna MacDonnellNational Institute of Water and Atmospheric Research, Wellington, New ZealandMalcolm P. Francis & Warrick S. LyonBeneath the Waves, Herndon, VA, USAAustin J. GallagherRosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USAAustin J. Gallagher, Neil Hammerschlag & Emily R. NelsonOceans Research Institute, Mossel Bay, South AfricaEnrico GennariDepartment of Ichthyology and Fisheries Science, Rhodes University, Grahamstown, South AfricaEnrico Gennari & Alison TownerSARDI Aquatic Sciences, Adelaide, South Australia, AustraliaSimon D. Goldsworthy & Paul J. RogersZoological Society of London, London, UKMatthew J. Gollock & Fiona LlewellynGalapagos Whale Shark Project, Puerto Ayora, Galapagos, EcuadorJonathan R. GreenGriffith Centre for Coastal Management, Griffith University School of Engineering, Griffith University, Gold Coast, Queensland, AustraliaJohan A. GustafsonSaving the Blue, Cooper City, FL, USATristan L. GuttridgeSmithsonian Tropical Research Institute, Panama City, PanamaHector M. GuzmanLeonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USANeil HammerschlagGalapagos Science Center, San Cristobal, Galapagos, EcuadorAlex R. HearnUniversidad San Francisco de Quito, Quito, EcuadorAlex R. HearnBlue Water Marine Research, Tutukaka, New ZealandJohn C. HoldsworthUniversity of Queensland, Brisbane, Queensland, AustraliaBonnie J. HolmesMicrowave Telemetry, Columbia, MD, USALucy A. Howey & Lance K. B. JordanPelagios-Kakunja, La Paz, MexicoMauricio Hoyos & James T. KetchumMote Marine Laboratory, Center for Shark Research, Sarasota, FL, USARobert E. Hueter, John J. Morris & John P. TyminskiBiological Sciences, University of Windsor, Windsor, Ontario, CanadaNigel E. HusseyCape Research and Diver Development, Simon’s Town, South AfricaDylan T. IrionInstitute of Zoology, Zoological Society of London, London, UKDavid M. P. JacobyCentre for Sustainable Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Perth, Western Australia, AustraliaOliver J. D. JewellDyer Island Conservation Trust, Western Cape, South AfricaOliver J. D. Jewell & Alison TownerBlue Wilderness Research Unit, Scottburgh, South AfricaRyan JohnsonUniversity of California Davis, Davis, CA, USAA. Peter KlimleyCape Research Centre, South African National Parks, Steenberg, South AfricaAlison A. KockShark Spotters, Fish Hoek, South AfricaAlison A. KockInstitute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Rondebosch, South AfricaAlison A. KockWestern Cape Department of Agriculture, Veterinary Services, Elsenburg, South AfricaPieter KoenDepartamento de Biologia Marinha, Universidade Federal Fluminense (UFF), Niterói, BrazilFernanda O. LanaDepartment of Zoology, University of Cambridge, Cambridge, UKJames S. E. LeaAtlantic White Shark Conservancy, Chatham, MA, USAHeather MarshallFisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, AustraliaJaime D. McAllister, Jayson M. Semmens, German Soler & Kilian M. StehfestPontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, EcuadorCesar Peñaherrera-PalmaMarine Megafauna Foundation, Truckee, CA, USASimon J. Pierce & Christoph A. RohnerConservation and Fisheries Department, Ascension Island Government, Georgetown, Ascension Island, UKAndrew J. RichardsonMarine Conservation Society Seychelles, Victoria, SeychellesDavid R. L. RowatCORDIO, East Africa, Mombasa, KenyaMelita SamoilysUpwell, Monterey, CA, USAGeorge ShillingerDepartment of Zoology and Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South AfricaMalcolm J. SmaleNational Institute of Polar Research, Tachikawa, Tokyo, JapanYuuki Y. WatanabeSOKENDAI (The Graduate University for Advanced Studies), Tachikawa, Tokyo, JapanYuuki Y. WatanabeCentre for Ecology and Conservation, University of Exeter, Penryn, UKSam B. WeberDepartment of Biological Sciences, University of Rhode Island, Kingston, RI, USABradley M. WetherbeeDepartment of Oceanography and Environment, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChilePatricia M. ZárateDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaRobert HarcourtSchool of Life and Environmental Sciences, Deakin University, Geelong, Victoria, AustraliaGraeme C. HaysAZTI – BRTA, Pasaia, SpainXabier IrigoienIKERBASQUE, Basque Foundation for Science, Bilbao, SpainXabier IrigoienInstituto de Fisica Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Cientificas, University of the Balearic Islands, Palma de Mallorca, SpainVictor M. EguiluzWildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney, UKLara L. SousaOcean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UKSamantha J. Simpson & David W. SimsCentre for Biological Sciences, University of Southampton, Southampton, UKDavid W. SimsN.Q. and D.W.S. planned the data analysis. N.Q. led the data analysis with contributions from M.V., A.M.M.S. and D.W.S. N.E.H. contributed analysis tools. A.M.M.S. undertook linear-regression modelling. D.W.S. led the manuscript writing with contributions from N.Q., N.E.H., A.M.M.S and all authors. Six of the original authors were not included in the Reply authorship; two authors retired from science and the remaining four, although supportive of our Reply, declined to join the authorship due to potential conflicts of interest with the authors of the Comment and/or their institutions. More

Study areaThe Pearl River Delta (112°45′–113°50′ E, 21°31′–23°10′ N) is located in the central and southern parts of Guangdong Province, including the lower reaches of the Pearl River, adjacent to Hong Kong and Macao, and facing Southeast Asia across the sea with convenient land and sea transportation. As shown in Fig. 1, the Pearl River Delta region includes nine prefecture-level cities, namely Guangzhou, Shenzhen, Zhongshan, Zhuhai, Dongguan, Zhaoqing, Foshan, Huizhou, and Jiangmen.Figure 1Geographical location of Pearl River Delta drawn in ArcGIS 10.6.Full size imageData sourceThe research framework of this paper is shown in Fig. 2, and the data sources are as follows. Taking the basin as the research unit, the raster data of 30 m and 1 km were analyzed by zoning statistics:

(1)

China’s land-use raster data for 1990, 2000, 2010, and 2018 were obtained from the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn), with a spatial resolution of 30 m. According to land resources and their utilization attributes, the dataset divides land cover types into six first-level categories: cultivated land, woodland, grassland, water area, construction land, unused land, and land reclamation from ocean. The land urbanization rate (LUR) refers to the proportion of construction land in the whole region, which is calculated by dividing the area of construction land by the area of all land use types.

(2)

Raster data of population density (POP) from 1990, 2000, 2010, and 2015 were obtained from the Environment and Resources Data Cloud Platform of the Chinese Academy of Sciences, with a spatial resolution of 1 km. Owing to the stable growth of population density under normal circumstances, the population density data of 2018 were obtained by linear fitting based on POP data from 2010 and 2015.

(3)

Nighttime Light (NTL) raster data from 1992 to 2018 were obtained from the Nature journal data (https://doi.org/10.6084/m9.figshare.9828827.v2) with a spatial resolution of 500 m45 Calibration was performed to eliminate the differences in the DMSP (1992–2013) and VIIRS (2012–2018) data, generating a complete and consistent NTL dataset on a global scale.

Figure 2Research framework.Full size imageLand-use information TUPUThe land-use information graph is a geospatial analysis model combining attributes, processes, and spaces, which can reflect the spatial differences and temporal changes in land-use types46. In its function expression, let the state variables be (pleft( {p_{1} ,p_{2} ,p_{3} , ldots ,p_{n} } right)), and then set p as a function of spatial position r and time t, as follows:$$ begin{array}{*{20}c} {p = fleft( {r,t} right)} \ end{array} $$
(1)
where (p) represents land-use characteristics. (1) To realize the spatial description of land attributes, when t is constant, the function relation of (p) changing with (r) is constructed. (2) The process description of land attributes can be realized, and when (r) is constant, the function relation of (p) changing with (t) can be constructed. The combination of these two functions can form a conceptual model of the land-use information graph and realize a composite study of land space, process, and attributes.Habitat qualityHabitat quality evaluationWe used InVEST-HQ to evaluate the habitat quality in the Pearl River Delta region. Based on land-use types, InVEST-HQ calculated the habitat degradation degree and habitat quality index by using threat factors, the sensitivity of different habitat types to threat factors, and habitat suitability15. The InVEST-HQ model was co-developed by Stanford University, the Nature Conservancy, and the World Wide Fund for Nature15. InVEST-HQ has a low demand for data and a better spatial visualization effect, which is widely used in the field of urban ecology47,48,49. For example, The InVEST-HQ model has been used to assess dynamic changes in habitat quality in Scottish11, China50,51 and Portugal47. Habitat degradation and habitat quality were calculated using the following formulas:$$ begin{array}{*{20}c} {Q_{{xj}} = ~H_{j} left[ {1 – left( {frac{{D_{{xj}}^{2} }}{{D_{{xj}}^{2} + k^{2} )}}} right)} right]} \ end{array} $$
(2)
$$ begin{array}{*{20}c} {D_{{xj}} = ~mathop sum limits_{{r = 1}}^{r} mathop sum limits_{{y = 1}}^{y} left( {frac{{w_{r} }}{{mathop sum nolimits_{{r = 1}}^{r} w_{r} }}} right)r_{y} i_{{rxy}} beta _{x} S_{{jr}} } \ end{array} $$
(3)
where (Q_{{xj}}) is the habitat quality of grid x in land-use type j, (H_{j}) is the habitat suitability of land-use type j, (D_{{xj}}) is the habitat degradation degree of grid x in land-use type j, k is the half-satiety sum constant, r is the number of threat factors, and y is the relative sensitivity of threat sources. (r_{y} ,w_{r}), and (i_{{rxy}}) are, respectively, the interference intensity and weight of the grid where the threat factor r is located, and the interference generated by the habitat. (beta _{x} ,S_{{jr}}) are the anti-disturbance ability of habitat type x and its relative sensitivity to various threat sources, respectively.The value range of habitat degradation degree is [0, 1], and the larger the value, the more serious the habitat degradation. The value of habitat quality is between 0 and 1, and the higher the value, the better the habitat quality.$$ begin{array}{*{20}c} {Linear,attenuation:~i_{{rxy}} = 1 – left( {d_{{xy}} /d_{{r,max}} } right)} \ end{array} $$
(4)
$$ begin{array}{*{20}c} {Exponential,decay:~i_{{rxy}} = expleft[ { – 2.99d_{{xy}} /d_{{r{text{~}}max}} } right]} \ end{array} $$
(5)

where (d_{{xy}}) is the straight-line distance between grids x and y, and (d_{{r,max}}) is the maximum threat distance of threat factor r.Five categories of documentation are prepared before using InVEST-HQ: LULC maps, threat factor data, threat sources, accessibility of degradation sources, habitat types and their sensitivity to each threat. Threat sources were divided into Cropland, City/town, Rural settlements, Other construction land, Unused land, and land applications. The maps of threat sources are generated in ArcGIS. For example, in the map of threat sources of cultivated land, the raster value of cultivated land is set to 1, and the raster value of other land types is set to 0. Distance between habitats and threat sources, weight of threat factors, decay type of threats factors, habitat suitability and the sensitivity of different habitat types to threat factors were derived from previous studies in similar regions2,25,38,39,50 and user guide manual of InVEST model15, as shown in Tables 1 and 2.Table 1 Threat factors and related coefficients.Full size tableTable 2  Sensitivity of habitat types to each threat factor.Full size tableHabitat quality change index and contribution indexThe CI was used to analyze the causes of the changes in habitat quality, and the following formula was used to qu2,25,38,39,50antitatively represent the contribution of land-use conversion to habitat quality change. In this study, the total value of habitat quality loss caused by land transfer in areas related to construction land expansion from 1990 to 2018 can be expressed as follows:$$ begin{array}{*{20}c} {CI~ = ~frac{{mathop sum nolimits_{1}^{n} left( {Q_{{ij2018}} – Q_{{xj1990}} } right)}}{n}} \ end{array} $$
(6)

where n is the grid number of cultivated land transferred to construction land.To analyze the relationship between land-use change and habitat quality, the HQCI was constructed to describe the mean value of habitat quality reduction caused by land transfer in the areas related to construction land expansion during the study period. The formula is as follows:$$ begin{array}{*{20}c} {HQCI~ = CI_{{ij}} /S_{{ij}} } \ end{array} $$
(7)
where (CI_{{ij}}) represents the total value of habitat quality change when land-use type (i) is converted into land-use type (j), and (S_{{ij}}) represents the area converted from land-use type (i) into land-use type (j). The positive and negative values of HQCI, respectively, represent the positive and negative impacts of land-use change on the habitat, and the higher the absolute value of HQCI, the greater the impact.Correlation analysisGeographically weighted regressionBased on traditional OLS, GWR establishes local spatial regression and considers spatial location factors, which can effectively analyze the spatial heterogeneity of various elements at different locations52. The calculation formula is as follows:$$ Y_{i} = ~beta _{0} left( {mu _{i} ,v_{i} } right) + sum kbeta _{k} left( {mu _{i} ,v_{i} } right)X_{{ik}} + varepsilon _{i} $$where (Y_{i}) is the coupling coordination degree of the ith sample point, (left( {mu _{i} ,v_{i} } right)) is the spatial position coordinate of the ith sample point, (beta _{k} left( {mu _{i} ,v_{i} } right)) is the value of the continuous function (beta _{k} left( {mu ,v} right)) at (left( {mu _{i} ,v_{i} } right)), (X_{{ik}}) is the independent variable, (varepsilon _{i}) is the random error term, and k is the number of spatial units.To simplify the complicated urbanization process, it was divided into three aspects: economic urbanization, population urbanization, and land urbanization according to the existing research38. The NTL, POP, and LUR were used to represent the economic development, population scale, and land urbanization level of the city.The research unit is a river basin, which has both natural and social attributes. It is a relatively independent and complete system, which can connect and explain the coupling phenomenon of society, economy, and nature53. The hydrological analysis module in ArcGIS was used to divide the research area into 374 small basins. When calculating the cumulative flow of the grid, 100,000 was used as the threshold value, and basins less than 5 km2 were combined with the adjacent basins.Zone classification using the Self-organizing feature mapping neural networkThe SOFM neural network was proposed by Kohonen, a Finnish scholar, and constructed by simulating a “lateral inhibition” phenomenon in the human cerebral cortex. It has been widely applied in classification research in geographic and land system science42,43. The advantages of the SOFM neural network in classifying the coupling relationship between urbanization and habitat quality are as follows : (1) it simulates human brain neurons through unsupervised learning, which is objective and reliable. (2) It maintains the data topology during self-learning, training, and simulation to obtain reasonable partition results and identify the differences between different basins. (3) For massive data, the SOFM network has a good clustering function while maintaining its characteristics and uses the weight vector of the output node to represent the original input. The SOFM neural network can compress the data while maintaining a high similarity between the compression results and the original input data54. We exported the data from ArcGIS, and conducted cluster analysis on the four factors of NTL, POP, LUR and habitat quality using SOFM. Finally, the analysis results are imported into ArcGIS for display. More

An umbrella-shaped structure of unknown function crowns a recently described species of fairy lantern. Credit: Siti Munirah Mat Yunoh et al./PhytoKeys (CC BY 4.0)

Conservation biology
07 July 2021
Newfound ‘fairy lantern’ could soon be snuffed out forever

Wild boars have destroyed three of the four known specimens of a bizarre plant in the forests of Malaysia.

Share on Twitter
Share on Twitter

Share on Facebook
Share on Facebook

Share via E-Mail
Share via E-Mail

Researchers have discovered a new species of ‘fairy lantern’, leafless plants that look like tiny glowing lights. Sadly, however, the organism might already be on the verge of extinction.Plants in the genus Thismia, colloquially called ‘fairy lanterns’, draw nutrients from underground fungi and grow in parts of Asia, Australasia and the Americas. Siti Munirah Mat Yunoh at the Forest Research Institute Malaysia in Kepong and her colleagues described a new species of Thismia that was first found in 2019 in a Malaysian rain forest. The scientists named the plant Thismia sitimeriamiae after the mother of the local explorer who discovered it, in honour of her support for her son’s nature-conservation efforts.Thismia sitimeriamiae is only about two centimetres tall, and sports an orange flower shaped like a funnel with an umbrella-like structure on top. The plant seems to be so rare that it should be considered critically endangered: just four individuals of T. sitimeriamiae have ever been seen, and wild boars have destroyed all but one of these, the authors say.

PhytoKeys (2021)

Conservation biology More

Centro de Investigação em Biodiversidade e Recursos Genéticos/Research Network in Biodiversity and Evolutionary Biology, Campus Agrário de Vairão, Universidade do Porto, Vairão, PortugalNuno Queiroz, Ana Couto, Marisa Vedor, Ivo da Costa, Gonzalo Mucientes & António M. SantosMarine Biological Association of the United Kingdom, Plymouth, UKNuno Queiroz, Nicolas E. Humphries, Lara L. Sousa, Samantha J. Simpson, Emily J. Southall & David W. SimsDepartamento de Biologia, Faculdade de Ciências da Universidade do Porto, Porto, PortugalMarisa Vedor & António M. SantosUWA Oceans Institute, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSchool of Biological Sciences, University of Western Australia, Crawley, Western Australia, AustraliaAna M. M. SequeiraSpanish Institute of Oceanography, Santa Cruz de Tenerife, SpainFrancisco J. AbascalAbercrombie and Fish, Port Jefferson Station, NY, USADebra L. AbercrombieMarine Biology and Aquaculture Unit, College of Science and Engineering, James Cook University, Cairns, Queensland, AustraliaKatya Abrantes, Adam Barnett, Richard Fitzpatrick & Marcus SheavesInstitute of Natural and Mathematical Sciences, Massey University, Palmerston North, New ZealandDavid Acuña-MarreroUniversidade Federal Rural de Pernambuco (UFRPE), Departamento de Pesca e Aquicultura, Recife, BrazilAndré S. Afonso, Natalia P. A. Bezerra, Fábio H. V. Hazin, Fernanda O. Lana, Bruno C. L. Macena & Paulo TravassosMARE, Marine and Environmental Sciences Centre, Instituto Politécnico de Leiria, Peniche, PortugalAndré S. AfonsoMARE, Laboratório Marítimo da Guia, Faculdade de Ciências da Universidade de Lisboa, Cascais, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreInstitute of Marine Research (IMAR), Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes, Bruno C. L. Macena & Frederic VandeperreOkeanos – Departamento de Oceanografia e Pescas, Universidade dos Açores, Horta, PortugalPedro Afonso, Jorge Fontes & Frederic VandeperreDepartment of Environmental Affairs, Oceans and Coasts Research, Cape Town, South AfricaDarrell Anders, Michael A. Meÿer, Sarika Singh & Laurenne B. SnydersLarge Marine Vertebrates Research Institute Philippines, Jagna, PhilippinesGonzalo AraujoFins Attached Marine Research and Conservation, Colorado Springs, CO, USARandall ArauzPrograma Restauración de Tortugas Marinas PRETOMA, San José, Costa RicaRandall ArauzMigraMar, Olema, CA, USARandall Arauz, Sandra Bessudo Lion, Eduardo Espinoza, Alex R. Hearn, Mauricio Hoyos, James T. Ketchum, A. Peter Klimley, Cesar Peñaherrera-Palma, George Shillinger, German Soler & Patricia M. ZárateInstitut de Recherche pour le Développement, UMR MARBEC (IRD, Ifremer, Univ. Montpellier, CNRS), Sète, FrancePascal Bach, Antonin V. Blaison, Laurent Dagorn, John D. Filmalter, Fabien Forget, Francois Poisson, Marc Soria & Mariana T. TolottiBiology Department, University of Massachusetts Dartmouth, Dartmouth, MA, USADiego Bernal & Heather MarshallRed Sea Research Center, Division of Biological and Environmental Science and Engineering, King Abdullah University of Science and Technology, Thuwal, Saudi ArabiaMichael L. Berumen, Jesse E. M. Cochran & Carlos M. DuarteFundación Malpelo y Otros Ecosistemas Marinos, Bogota, ColombiaSandra Bessudo Lion, Felipe Ladino, Lina Maria Quintero & German SolerHopkins Marine Station of Stanford University, Pacific Grove, CA, USABarbara A. Block, Taylor K. Chapple, George Shillinger & Timothy D. WhiteDepartment of Biological Sciences, Florida International University, North Miami, FL, USAMark E. Bond, Demian D. Chapman & Yannis P. PapastamatiouInstituto de Ciências do Mar, Universidade Federal do Ceará, Fortaleza, BrazilRamon BonfilSchool of Fishery and Aquatic Sciences, University of Washington, Seattle, WA, USACamrin D. BraunBiology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USACamrin D. Braun & Simon R. ThorroldShark Research and Conservation Program, Cape Eleuthera Institute, Eleuthera, BahamasEdward J. Brooks, Annabelle Brooks & Sean WilliamsUniversity of Exeter, Exeter, UKAnnabelle BrooksSouth Atlantic Environmental Research Institute, Stanley, Falkland IslandsJudith BrownDepartment of Biological Sciences, The Guy Harvey Research Institute, Nova Southeastern University, Dania Beach, FL, USAMichael E. Byrne, Mahmood Shivji, Jeremy J. Vaudo & Bradley M. WetherbeeSchool of Natural Resources, University of Missouri, Columbia, MO, USAMichael E. ByrneLife and Environmental Sciences, University of Iceland, Reykjavik, IcelandSteven E. CampanaSchool of Marine Science and Policy, University of Delaware, Lewes, DE, USAAaron B. CarlisleMassachusetts Division of Marine Fisheries, New Bedford, MA, USAJohn Chisholm & Gregory B. SkomalMarine Research Facility, Jeddah, Saudi ArabiaChristopher R. Clarke & James S. E. LeaPSL, Labex CORAIL, CRIOBE USR3278 EPHE-CNRS-UPVD, Papetoai, French PolynesiaEric G. CluaAgence de Recherche pour la Biodiversité à la Réunion (ARBRE), Réunion, Marseille, FranceEstelle C. CrocheletInstitut de Recherche pour le Développement, UMR 228 ESPACE-DEV, Réunion, Marseille, FranceEstelle C. CrocheletSave Our Seas Foundation–D’Arros Research Centre (SOSF-DRC), Geneva, SwitzerlandRyan Daly & Clare A. Keating DalySouth African Institute for Aquatic Biodiversity (SAIAB), Grahamstown, South AfricaRyan Daly, John D. Filmalter, Enrico Gennari & Alison A. KockDepartment of Fisheries Evaluation, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChileDaniel Devia CortésSchool of Biological, Earth and Environmental Sciences, University College Cork, Cork, IrelandThomas K. Doyle & Luke HarmanMaREI Centre, Environmental Research Institute, University College Cork, Cork, IrelandThomas K. DoyleCollege of Science and Engineering, Flinders University, Adelaide, South Australia, AustraliaMichael Drew, Matthew Heard & Charlie HuveneersDepartment of Conservation, Auckland, New ZealandClinton A. J. DuffySouth African Institute for Aquatic Biodiversity, Geological Sciences, UKZN, Durban, South AfricaThor EriksonDireccion Parque Nacional Galapagos, Puerto Ayora, Galapagos, EcuadorEduardo EspinozaAustralian Institute of Marine Science, Indian Ocean Marine Research Centre (UWA), Crawley Western Australia, Crawley, AustraliaLuciana C. Ferreira, Mark G. Meekan & Michele ThumsDepartment of Fish and Wildlife Conservation, Virginia Tech, Blacksburg, VA, USAFrancesco FerrettiOCEARCH, Park City, Utah, USAG. Chris FischerBedford Institute of Oceanography, Dartmouth, Nova Scotia, CanadaMark Fowler, Warren Joyce & Anna MacDonnellNational Institute of Water and Atmospheric Research, Wellington, New ZealandMalcolm P. Francis & Warrick S. LyonBeneath the Waves, Herndon, VA, USAAustin J. GallagherRosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL, USAAustin J. Gallagher, Neil Hammerschlag & Emily R. NelsonOceans Research Institute, Mossel Bay, South AfricaEnrico GennariDepartment of Ichthyology and Fisheries Science, Rhodes University, Grahamstown, South AfricaEnrico Gennari & Alison TownerSARDI Aquatic Sciences, Adelaide, South Australia, AustraliaSimon D. Goldsworthy & Paul J. RogersZoological Society of London, London, UKMatthew J. Gollock & Fiona LlewellynGalapagos Whale Shark Project, Puerto Ayora, Galapagos, EcuadorJonathan R. GreenGriffith Centre for Coastal Management, Griffith University School of Engineering, Griffith University, Gold Coast, Queensland, AustraliaJohan A. GustafsonSaving the Blue, Cooper City, FL, USATristan L. GuttridgeSmithsonian Tropical Research Institute, Panama City, PanamaHector M. GuzmanLeonard and Jayne Abess Center for Ecosystem Science and Policy, University of Miami, Coral Gables, FL, USANeil HammerschlagGalapagos Science Center, San Cristobal, Galapagos, EcuadorAlex R. HearnUniversidad San Francisco de Quito, Quito, EcuadorAlex R. HearnBlue Water Marine Research, Tutukaka, New ZealandJohn C. HoldsworthUniversity of QueenslandBrisbane, Queensland, AustraliaBonnie J. HolmesMicrowave Telemetry, Columbia, MD, USALucy A. Howey & Lance K. B. JordanPelagios-Kakunja, La Paz, MexicoMauricio Hoyos & James T. KetchumMote Marine Laboratory, Center for Shark Research, Sarasota, FL, USARobert E. Hueter, John J. Morris & John P. TyminskiBiological Sciences, University of Windsor, Windsor, Ontario, CanadaNigel E. HusseyCape Research and Diver Development, Simon’s Town, South AfricaDylan T. IrionInstitute of Zoology, Zoological Society of London, London, UKDavid M. P. JacobyCentre for Sustainable Aquatic Ecosystems, Harry Butler Institute, Murdoch University, Perth, Western Australia, AustraliaOliver J. D. JewellDyer Island Conservation Trust, Western Cape, South AfricaOliver J. D. Jewell & Alison TownerBlue Wilderness Research Unit, Scottburgh, South AfricaRyan JohnsonUniversity of California Davis, Davis, CA, USAA. Peter KlimleyCape Research Centre, South African National Parks, Steenberg, South AfricaAlison A. KockShark Spotters, Fish Hoek, South AfricaAlison A. KockInstitute for Communities and Wildlife in Africa, Department of Biological Sciences, University of Cape Town, Rondebosch, South AfricaAlison A. KockWestern Cape Department of Agriculture, Veterinary Services, Elsenburg, South AfricaPieter KoenDepartamento de Biologia Marinha, Universidade Federal Fluminense (UFF), Niterói, BrazilFernanda O. LanaDepartment of Zoology, University of Cambridge, Cambridge, UKJames S. E. LeaAtlantic White Shark Conservancy, Chatham, MA, USAHeather MarshallFisheries and Aquaculture Centre, Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Tasmania, AustraliaJaime D. McAllister, Jayson M. Semmens, German Soler & Kilian M. StehfestPontificia Universidad Católica del Ecuador Sede Manabi, Portoviejo, EcuadorCesar Peñaherrera-PalmaMarine Megafauna Foundation, Truckee, CA, USASimon J. Pierce & Christoph A. RohnerConservation and Fisheries Department, Ascension Island Government, Georgetown, Ascension Island, UKAndrew J. RichardsonMarine Conservation Society Seychelles, Victoria, SeychellesDavid R. L. RowatCORDIO, East Africa, Mombasa, KenyaMelita SamoilysUpwell, Monterey, CA, USAGeorge ShillingerDepartment of Zoology and Institute for Coastal and Marine Research, Nelson Mandela University, Port Elizabeth, South AfricaMalcolm J. SmaleNational Institute of Polar Research, Tachikawa, Tokyo, JapanYuuki Y. WatanabeSOKENDAI (The Graduate University for Advanced Studies), Tachikawa, Tokyo, JapanYuuki Y. WatanabeCentre for Ecology and Conservation, University of Exeter, Penryn, UKSam B. WeberDepartment of Biological Sciences, University of Rhode Island, Kingston, RI, USABradley M. WetherbeeDepartment of Oceanography and Environment, Fisheries Research Division, Instituto de Fomento Pesquero (IFOP), Valparaíso, ChilePatricia M. ZárateDepartment of Biological Sciences, Macquarie University, Sydney, New South Wales, AustraliaRobert HarcourtSchool of Life and Environmental Sciences, Deakin University, Geelong, Victoria, AustraliaGraeme C. HaysAZTI – BRTA, Pasaia, SpainXabier IrigoienIKERBASQUE, Basque Foundation for Science, Bilbao, SpainXabier IrigoienInstituto de Fisica Interdisciplinar y Sistemas Complejos, Consejo Superior de Investigaciones Cientificas, University of the Balearic Islands, Palma de Mallorca, SpainVictor M. EguiluzWildlife Conservation Research Unit, Department of Zoology, University of Oxford, Tubney, UKLara L. SousaOcean and Earth Science, National Oceanography Centre Southampton, University of Southampton, Southampton, UKSamantha J. Simpson & David W. SimsCentre for Biological Sciences, University of Southampton, Southampton, UKDavid W. SimsN.Q. and D.W.S. planned the data analysis. N.Q. led the data analysis with contributions from M.V. and D.W.S. N.E.H. contributed analysis tools. D.W.S. led the manuscript writing with contributions from N.Q., N.E.H. and all authors. Seven of the original authors were not included in the Reply authorship; two authors retired from science and the remaining five, although supportive of our Reply, declined to join the authorship due to potential conflicts of interest with the authors of the Comment and/or their institutions. More

1.Gilbert, S. F. Ecological Developmental Biology. in eLS 1–8 (Wiley, 2017). https://doi.org/10.1002/9780470015902.a0020479.pub2.2.Bateson, P., Gluckman, P. & Hanson, M. The biology of developmental plasticity and the predictive adaptive response hypothesis. J. Physiol. https://doi.org/10.1113/jphysiol.2014.271460 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
3.Emlen, D. J. & Nijhout, H. F. The development and evolution of exaggerated morphologies in insects. Annu. Rev. Entomol. https://doi.org/10.1146/annurev.ento.45.1.661 (2000).Article 
PubMed 

Google Scholar 
4.Koyama, T., Mendes, C. C. & Mirth, C. K. Mechanisms regulating nutrition-dependent developmental plasticity through organ-specific effects in insects. Front. Physiol. https://doi.org/10.3389/fphys.2013.00263 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Wilson, E. O. The Insect Societies (Harvard University Press, 1971).
Google Scholar 
6.Gluckman, P. D., Hanson, M. A., Cooper, C. & Thornburg, K. L. Effect of in utero and early-life conditions on adult health and disease. N. Engl. J. Med. https://doi.org/10.1056/nejmra0708473 (2008).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Lummaa, V. & Clutton-Brock, T. Early development, survival and reproduction in humans. Trends Ecol. Evol. 17, 141–147 (2002).Article 

Google Scholar 
8.Griffin, R. M., Hayward, A. D., Bolund, E., Maklakov, A. A. & Lummaa, V. Sex differences in adult mortality rate mediated by early-life environmental conditions. Ecol. Lett. https://doi.org/10.1111/ele.12888 (2018).Article 
PubMed 

Google Scholar 
9.Briga, M., Koetsier, E., Boonekamp, J. J., Jimeno, B. & Verhulst, S. Food availability affects adult survival trajectories depending on early developmental conditions. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2016.2287 (2017).Article 

Google Scholar 
10.Barrett, E. L. B., Hunt, J., Moore, A. J. & Moore, P. J. Separate and combined effects of nutrition during juvenile and sexual development on female life-history trajectories: The thrifty phenotype in a cockroach. Proc. R. Soc. B Biol. Sci. 276, 3257–3264 (2009).Article 

Google Scholar 
11.Kriengwatana, B., Wada, H., Macmillan, A. & MacDougall-Shackleton, S. A. Juvenile nutritional stress affects growth rate, adult organ mass, and innate immune function in zebra finches (Taeniopygia guttata). Physiol. Biochem. Zool. 86, 769–781 (2013).Article 

Google Scholar 
12.Birkhead, T. R., Fletcher, F. & Pellatt, E. J. Nestling diet, secondary sexual traits and fitness in the zebra finch. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.1999.0649 (1999).Article 

Google Scholar 
13.Tella, J. L. et al. Offspring body condition and immunocompetence are negatively affected by high breeding densities in a colonial seabird: A multiscale approach. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2001.1688 (2001).Article 

Google Scholar 
14.Naguib, M., Amrhein, V. & Kunc, H. P. Effects of territorial intrusions on eavesdropping neighbors: Communication networks in nightingales. Behav. Ecol. https://doi.org/10.1093/beheco/arh108 (2004).Article 

Google Scholar 
15.Stjernman, M., Råberg, L. & Nilsson, J. Å. Long-term effects of nestling condition on blood parasite resistance in blue tits (Cyanistes caeruleus). Can. J. Zool. https://doi.org/10.1139/Z08-071 (2008).Article 

Google Scholar 
16.Butler, M. W. & McGraw, K. J. Past or present? Relative contributions of developmental and adult conditions to adult immune function and coloration in mallard ducks (Anas platyrhynchos). J. Comp. Physiol. B. https://doi.org/10.1007/s00360-010-0529-z (2011).Article 
PubMed 

Google Scholar 
17.De Coster, G. et al. Effects of early developmental conditions on innate immunity are only evident under favourable adult conditions in zebra finches. Naturwissenschaften https://doi.org/10.1007/s00114-011-0863-3 (2011).Article 
PubMed 

Google Scholar 
18.Albon, S. D., Clutton-Brock, T. H. & Guinness, F. E. Early development and population dynamics in red deer. II. Density-independent effects and cohort variation. J. Anim. Ecol. https://doi.org/10.2307/4800 (1987).Article 

Google Scholar 
19.Meikle, D. & Westberg, M. Maternal nutrition and reproduction of daughters in wild house mice (Mus musculus). Reproduction https://doi.org/10.1530/rep.0.1220437 (2001).Article 
PubMed 

Google Scholar 
20.Burton, T. & Metcalfe, N. B. Can environmental conditions experienced in early life influence future generations?. Proc. R. Soc. B Biol. Sci. 281, 20140311 (2014).Article 

Google Scholar 
21.Kucharski, R., Maleszka, J., Foret, S. & Maleszka, R. Nutritional control of reproductive status in honeybees via DNA methylation. Science https://doi.org/10.1126/science.1153069 (2008).Article 
PubMed 

Google Scholar 
22.Roth, A. et al. A genetic switch for worker nutritionmediated traits in honeybees. PLoS Biol. https://doi.org/10.1371/journal.pbio.3000171 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
23.Slater, G. P., Yocum, G. D. & Bowsher, J. H. Diet quantity influences caste determination in honeybees (Apis mellifera). Proc. Biol. Sci. https://doi.org/10.1098/rspb.2020.0614 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
24.Rembold, H., Lackner, B. & Geistbeck, I. The chemical basis of honeybee, Apis mellifera, caste formation: Partial purification of queen bee determinator from royal jelly. J. Insect Physiol. https://doi.org/10.1016/0022-1910(74)90063-8 (1974).Article 
PubMed 

Google Scholar 
25.Mutti, N. S. et al. IRS and tor nutrient-signaling pathways act via juvenile hormone to influence honey bee caste fate. J. Exp. Biol. https://doi.org/10.1242/jeb.061499 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
26.Scofield, H. N. & Mattila, H. R. Honey bee workers that are pollen stressed as larvae become poor foragers and waggle dancers as adults. PLoS ONE https://doi.org/10.1371/journal.pone.0121731 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
27.Rittschof, C. C., Coombs, C. B., Frazier, M., Grozinger, C. M. & Robinson, G. E. Early-life experience affects honey bee aggression and resilience to immune challenge. Sci. Rep. https://doi.org/10.1038/srep15572 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Walton, A., Dolezal, A. G., Bakken, M. A. & Toth, A. L. Hungry for the queen: Honeybee nutritional environment affects worker pheromone response in a life stage-dependent manner. Funct. Ecol. https://doi.org/10.1111/1365-2435.13222 (2018).Article 

Google Scholar 
29.Dolezal, A. G. et al. Interacting stressors matter: Diet quality and virus infection in honeybee health. R. Soc. Open Sci. https://doi.org/10.1098/rsos.181803 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
30.Alaux, C. et al. A ‘Landscape physiology’ approach for assessing bee health highlights the benefits of floral landscape enrichment and semi-natural habitats. Sci. Rep. https://doi.org/10.1038/srep40568 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
31.Naug, D. Nutritional stress due to habitat loss may explain recent honeybee colony collapses. Biol. Conserv. https://doi.org/10.1016/j.biocon.2009.04.007 (2009).Article 

Google Scholar 
32.Dolezal, A. G. & Toth, A. L. Feedbacks between nutrition and disease in honey bee health. Curr. Opin. Insect Sci. https://doi.org/10.1016/j.cois.2018.02.006 (2018).Article 
PubMed 

Google Scholar 
33.Alaux, C., Ducloz, F., Crauser, D. & Le Conte, Y. Diet effects on honeybee immunocompetence. Biol. Lett. https://doi.org/10.1098/rsbl.2009.0986 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
34.Jack, C. J., Uppala, S. S., Lucas, H. M. & Sagili, R. R. Effects of pollen dilution on infection of Nosema ceranae in honey bees. J. Insect Physiol. 87, 12–19 (2016).CAS 
Article 

Google Scholar 
35.Di Pasquale, G. et al. Influence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8, e72016 (2013).ADS 
Article 

Google Scholar 
36.Ramsey, S. D. et al. Varroa destructor feeds primarily on honey bee fat body tissue and not hemolymph. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.1818371116 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
37.Grozinger, C. M. & Flenniken, M. L. Bee viruses: Ecology, pathogenicity, and impacts. Annu. Rev. Entomol. https://doi.org/10.1146/annurev-ento-011118-111942 (2019).Article 
PubMed 

Google Scholar 
38.Traynor, K. S. et al. Varroa destructor: A complex parasite, crippling honey bees worldwide. Trends Parasitol. https://doi.org/10.1016/j.pt.2020.04.004 (2020).Article 
PubMed 

Google Scholar 
39.DeGrandi-Hoffman, G., Chen, Y., Huang, E. & Huang, M. H. The effect of diet on protein concentration, hypopharyngeal gland development and virus load in worker honey bees (Apis mellifera L.). J. Insect Physiol. https://doi.org/10.1016/j.jinsphys.2010.03.017 (2010).Article 
PubMed 

Google Scholar 
40.Hsieh, E. M., Berenbaum, M. R. & Dolezal, A. G. Ameliorative effects of phytochemical ingestion on viral infection in honey bees. Insects https://doi.org/10.3390/insects11100698 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
41.Rutter, L. et al. Transcriptomic responses to diet quality and viral infection in Apis mellifera. BMC Genomics https://doi.org/10.1186/s12864-019-5767-1 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
42.Chen, Y. P. et al. Israeli acute paralysis virus: Epidemiology, pathogenesis and implications for honey bee health. PLoS Pathog. https://doi.org/10.1371/journal.ppat.1004261 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
43.Cox-Foster, D. L. et al. A metagenomic survey of microbes in honey bee colony collapse disorder. Science https://doi.org/10.1126/science.1146498 (2007).Article 
PubMed 

Google Scholar 
44.Maori, E. et al. IAPV, a bee-affecting virus associated with colony collapse disorder can be silenced by dsRNA ingestion. Insect Mol. Biol. https://doi.org/10.1111/j.1365-2583.2009.00847.x (2009).Article 
PubMed 

Google Scholar 
45.Hsieh, E. M., Carrillo-Tripp, J. & Dolezal, A. G. Preparation of virus-enriched inoculum for oral infection of honey bees (Apis Mellifera). J. Vis. Exp. https://doi.org/10.3791/61725 (2020).Article 
PubMed 

Google Scholar 
46.Wang, Y., Kaftanoglu, O., Fondrk, M. K. & Page, R. E. Nurse bee behaviour manipulates worker honeybee (Apis mellifera L.) reproductive development. Anim. Behav. https://doi.org/10.1016/j.anbehav.2014.02.012 (2014).Article 

Google Scholar 
47.Wang, Y. et al. Larval starvation improves metabolic response to adult starvation in honey bees (Apis mellifera L.). J. Exp. Biol. 219, 960–968 (2016).Article 

Google Scholar 
48.Wang, Y., Kaftanoglu, O., Brent, C. S., Page, R. E. & Amdam, G. V. Starvation stress during larval development facilitates an adaptive response in adult worker honey bees (Apis mellifera L.). J. Exp. Biol. https://doi.org/10.1242/jeb.130435 (2016).Article 
PubMed 

Google Scholar 
49.Toth, A. L. & Robinson, G. E. Worker nutrition and division of labour in honeybees. Anim. Behav. 69, 427–435 (2005).Article 

Google Scholar 
50.Dolezal, A. G., Carrillo-Tripp, J., Miller, W. A., Bonning, B. C. & Toth, A. L. Pollen contaminated with field-relevant levels of cyhalothrin affects honey bee survival, nutritional physiology, and pollen consumption behavior. J. Econ. Entomol. https://doi.org/10.1093/jee/tov301 (2016).Article 
PubMed 

Google Scholar 
51.Carrillo-Tripp, J. et al. In vivo and in vitro infection dynamics of honey bee viruses. Sci. Rep. https://doi.org/10.1038/srep22265 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
52.Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. Improving bioscience research reporting: The arrive guidelines for reporting animal research. PLoS Biol. https://doi.org/10.1371/journal.pbio.1000412 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
53.Geffre, A. C. et al. Honey bee virus causes context-dependent changes in host social behavior. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2002268117 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
54.Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods https://doi.org/10.1006/meth.2001.1262 (2001).Article 
PubMed 

Google Scholar 
55.Richard, F. J., Holt, H. L. & Grozinger, C. M. Effects of immunostimulation on social behavior, chemical communication and genome-wide gene expression in honey bee workers (Apis mellifera). BMC Genomics https://doi.org/10.1186/1471-2164-13-558 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
56.Evans, J. D. et al. Immune pathways and defence mechanisms in honey bees Apis mellifera. Insect Mol. Biol. https://doi.org/10.1111/j.1365-2583.2006.00682.x (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
57.Ryabov, E. V., Fannon, J. M., Moore, J. D., Wood, G. R. & Evans, D. J. The Iflaviruses Sacbrood virus and Deformed wing virus evoke different transcriptional responses in the honeybee which may facilitate their horizontal or vertical transmission. PeerJ https://doi.org/10.7717/peerj.1591 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
58.Cerutti, H. & Casas-Mollano, J. A. On the origin and functions of RNA-mediated silencing: From protists to man. Curr. Genet. https://doi.org/10.1007/s00294-006-0078-x (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
59.Harwood, G. P., Ihle, K. E., Salmela, H. & Amdam, G. V. Regulation of honeybee worker (Apis mellifera) life histories by Vitellogenin. in Hormones, Brain and Behavior: Third Edition (2017). https://doi.org/10.1016/B978-0-12-803592-4.00036-5.60.Team, R. C. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing (2016).61.Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. R Core Team (2014). nlme: linear and nonlinear mixed effects models. R package version 3.1–117. http://cran.r-project.org/web/packages/nlme/index.html (2014).62.Lenth, R., Singmann, H., Love, J., Buerkner, P. & Herve, M. emmeans: Estimated marginal means, aka least-squares means. R package version 1.15–15 (2020) https://doi.org/10.1080/00031305.1980.10483031 >.License.63.Crailsheim, K., Riessberger, U., Blaschon, B., Nowogrodzki, R. & Hrassnigg, N. Short-term effects of simulated bad weather conditions upon the behaviour of food-storer honeybees during day and night (Apis mellifera carnica Pollmann). Apidologie https://doi.org/10.1051/apido:19990406 (1999).Article 

Google Scholar 
64.McMullan, J. B. & Brown, M. J. F. The influence of small-cell brood combs on the morphometry of honeybees (Apis mellifera). Apidologie https://doi.org/10.1051/apido:2006041 (2006).Article 

Google Scholar 
65.Teicher, M. H. et al. The neurobiological consequences of early stress and childhood maltreatment. Neurosci. Biobehav. Rev. https://doi.org/10.1016/S0149-7634(03)00007-1 (2003).Article 
PubMed 

Google Scholar 
66.Harlow, H. F., Dodsworth, R. O. & Harlow, M. K. Total social isolation in monkeys. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.54.1.90 (1965).Article 
PubMed 
PubMed Central 

Google Scholar 
67.Toth, A. L., Kantarovich, S., Meisel, A. F. & Robinson, G. E. Nutritional status influences socially regulated foraging ontogeny in honey bees. J. Exp. Biol. https://doi.org/10.1242/jeb.01956 (2005).Article 
PubMed 

Google Scholar 
68.St Clair, A. L., Zhang, G., Dolezal, A. G., O’Neal, M. E. & Toth, A. L. Diversified farming in a monoculture landscape: Effects on honey bee health and wild bee communities. Environ. Entomol. https://doi.org/10.1093/ee/nvaa031 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
69.Dolezal, A. G., Clair, A. L. S., Zhang, G., Toth, A. L. & O’Neal, M. E. Native habitat mitigates feast–famine conditions faced by honey bees in an agricultural landscape. Proc. Natl. Acad. Sci. USA. 116, 25147–25155 (2019).CAS 
Article 

Google Scholar 
70.Smart, M. D., Otto, C. R. V. & Lundgren, J. G. Nutritional status of honey bee (Apis mellifera L.) workers across an agricultural land-use gradient. Sci. Rep. https://doi.org/10.1038/s41598-019-52485-y (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
71.Schmidt, J. O., Thoenes, S. C. & Levin, M. D. Survival of honey bees, Apis mellifera (Hymenoptera: Apidae), fed various pollen sources. Ann. Entomol. Soc. Am. https://doi.org/10.1093/aesa/80.2.176 (1987).Article 

Google Scholar 
72.Schmidt, L. S., Schmidt, J. O., Hima, R., Wang, W. & Xu, L. Feeding preference and survival of young worker honey bees (Hymenoptera: Apidae) fed rape, sesame, and sunflower pollen. J. Econ. Entomol. https://doi.org/10.1093/jee/88.6.1591 (1995).Article 

Google Scholar 
73.Dolezal, A. G., Carrillo-Tripp, J., Allen Miller, W., Bonning, B. C. & Toth, A. L. Intensively cultivated landscape and varroa mite infestation are associated with reduced honey bee nutritional state. PLoS ONE https://doi.org/10.1371/journal.pone.0153531 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
74.Failla, M. L. Trace elements and host defense: Recent advances and continuing challenges. J. Nutr. https://doi.org/10.1093/jn/133.5.1443s (2003).Article 
PubMed 

Google Scholar 
75.Filipiak, M. et al. Ecological stoichiometry of the honeybee: Pollen diversity and adequate species composition are needed to mitigate limitations imposed on the growth and development of bees by pollen quality. PLoS ONE https://doi.org/10.1371/journal.pone.0183236 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
76.Gems, D. & Partridge, L. Stress-response hormesis and aging: ‘That which does not kill us makes us stronger’. Cell Metab. https://doi.org/10.1016/j.cmet.2008.01.001 (2008).Article 
PubMed 

Google Scholar 
77.Ihle, K. E., Baker, N. A. & Amdam, G. V. Insulin-like peptide response to nutritional input in honey bee workers. J. Insect Physiol. https://doi.org/10.1016/j.jinsphys.2014.05.026 (2014).Article 
PubMed 

Google Scholar 
78.Paul, S. & Keshan, B. Ovarian development and vitellogenin gene expression under heat stress in silkworm, Bombyx mori. Psyche https://doi.org/10.1155/2016/4242317 (2016).Article 

Google Scholar 
79.Metcalfe, N. B. & Monaghan, P. Compensation for a bad start: Grow now, pay later?. Trends Ecol. Evol. https://doi.org/10.1016/S0169-5347(01)02124-3 (2001).Article 
PubMed 

Google Scholar 
80.Monaghan, P. Early growth conditions, phenotypic development and environmental change. Philos. Trans. R. Soc. B https://doi.org/10.1098/rstb.2007.0011 (2008).Article 

Google Scholar 
81.Lindström, J. Early development and fitness in birds and mammals. Trends Ecol. Evol. https://doi.org/10.1016/S0169-5347(99)01639-0 (1999).Article 
PubMed 

Google Scholar 
82.Smart, M. D., Pettis, J. S., Euliss, N. & Spivak, M. S. Land use in the Northern Great Plains region of the US influences the survival and productivity of honey bee colonies. Agric. Ecosyst. Environ. https://doi.org/10.1016/j.agee.2016.05.030 (2016).Article 

Google Scholar 
83.Otto, C. R. V., Roth, C. L., Carlson, B. L. & Smart, M. D. Land-use change reduces habitat suitability for supporting managed honey bee colonies in the Northern Great Plains. Proc. Natl. Acad. Sci. USA. https://doi.org/10.1073/pnas.1603481113 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
84.Smart, M., Pettis, J., Rice, N., Browning, Z. & Spivak, M. Linking measures of colony and individual honey bee health to survival among apiaries exposed to varying agricultural land use. PLoS ONE https://doi.org/10.1371/journal.pone.0152685 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
85.Wright, G. A., Nicolson, S. W. & Shafir, S. Nutritional physiology and ecology of honey bees. Annu. Rev. Entomol. https://doi.org/10.1146/annurev-ento-020117-043423 (2018).Article 
PubMed 

Google Scholar 
86.De Smet, L. et al. Stress indicator gene expression profiles, colony dynamics and tissue development of honey bees exposed to sub-lethal doses of imidacloprid in laboratory and field experiments. PLoS ONE https://doi.org/10.1371/journal.pone.0171529 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
87.de Graaf, D. C. et al. Heritability estimates of the novel trait ‘suppressed in ovo virus infection’ in honey bees (Apis mellifera). Sci. Rep. https://doi.org/10.6084/m9.figshare.8170925 (2020). More

1.Hu, F. et al. Improving N management through intercropping alleviates the inhibitory effect of mineral N on nodulation in pea. Plant Soil 412, 235–251 (2017).CAS 
Article 

Google Scholar 
2.Akhtar, K., Wang, W., Ren, G., Khan, A. & Wang, H. Integrated use of straw mulch with nitrogen fertilizer improves soil functionality and soybean production. Environ. Int. 132, 105092 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Khan, I. et al. Yield gap analysis of major food crops in Pakistan: Prospects for food security. Environ. Sci. Pollut. R. 28, 1–18 (2020).
Google Scholar 
4.Khan, I. et al. Farm households’ risk perception, attitude and adaptation strategies in dealing with climate change: Promise and perils from rural Pakistan. Land Use Policy 91, 104395 (2020).Article 

Google Scholar 
5.Gan, Y., Chang, L., Wang, X. & Mcconkey, B. Lowering carbon footprint of durum wheat by diversifying cropping systems. Field Crops Res. 122, 199–206 (2011).Article 

Google Scholar 
6.Linquist, B., Groenigen, K., Adviento-Borbe, M. A., Pittelkow, C. & Kessel, C. V. An agronomic assessment of greenhouse gas emissions from major cereal crops. Glob. Change Biol. 18, 194–209 (2015).ADS 
Article 

Google Scholar 
7.Hu, L. A. et al. Modelling impacts of alternative farming management practices on greenhouse gas emissions from a winter wheat–maize rotation system in China. Agric. Ecosyst. Environ. 135, 24–33 (2010).Article 
CAS 

Google Scholar 
8.Yang, X., Gao, W., Min, Z., Chen, Y. & Peng, S. Reducing agricultural carbon footprint through diversified crop rotation systems in the North China Plain. J. Clean. Prod. 76, 131–139 (2014).Article 

Google Scholar 
9.Wang, W. et al. Impact of straw management on seasonal soil carbon dioxide emissions, soil water content, and temperature in a semi-arid region of China. Sci. Total Environ. 652, 471–482 (2019).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
10.Liu, C., Cutforth, H., Chai, Q. & Gan, Y. Farming tactics to reduce the carbon footprint of crop cultivation in semiarid areas. A review. Agron. Sustain. Dev. 36, 69 (2016).Article 
CAS 

Google Scholar 
11.Chai, Q., Qin, A., Gan, Y. & Yu, A. Higher yield and lower carbon emission by intercropping maize with rape, pea, and wheat in arid irrigation areas. Agron. Sustain. Dev. 34, 535–543 (2014).CAS 
Article 

Google Scholar 
12.Mariela, et al. Conservation agriculture, increased organic carbon in the top-soil macro-aggregates and reduced soil CO2 emissions. Plant Soil 355, 183–197 (2012).Article 
CAS 

Google Scholar 
13.Akhtar, K., Wang, W., Ren, G., Khan, A. & Wang, H. Straw mulching with inorganic nitrogen fertilizer reduces soil CO2 and N2O emissions and improves wheat yield. Sci. Total Environ. 741, 140488 (2020).14.Hu, F. et al. Less carbon emissions of wheat–maize intercropping under reduced tillage in arid areas. Agron. Sustain. Dev. 35, 701–711 (2015).Article 
CAS 

Google Scholar 
15.Cong, W. F. et al. Intercropping enhances soil carbon and nitrogen. Glob. Change Biol. 21, 1715–1726 (2015).ADS 
Article 

Google Scholar 
16.Beedy, T. L., Snapp, S. S., Akinni Fe Si, F. K. & Sileshi, G. W. Impact of Gliricidia sepium intercropping on soil organic matter fractions in a maize-based cropping system. Agric. Ecosyst. Environ. 138, 139–146 (2010).Article 

Google Scholar 
17.Lithourgidis, A. S., Dhima, K. V., Vasilakoglou, I. B., Dordas, C. A. & Yiakoulaki, M. Sustainable production of barley and wheat by intercropping common vetch. Agron. Sustain. Dev. 27, 95–99 (2007).CAS 
Article 

Google Scholar 
18.Fan, Z. et al. Yield and water consumption characteristics of wheat/maize intercropping with reduced tillage in an Oasis region. Europ. J. Agron. 45, 52–58 (2013).Article 

Google Scholar 
19.Qin, A. Z., Huang, G. B., Chai, Q., Yu, A. Z. & Huang, P. Grain yield and soil respiratory response to intercropping systems on arid land. Field Crops Res. 144, 1–10 (2013).Article 

Google Scholar 
20.Hu, F. et al. Integration of wheat–maize intercropping with conservation practices reduces CO2 emissions and enhances water use in dry areas. Soil Till. Res. 169, 44–53 (2017).Article 

Google Scholar 
21.Yin, W. et al. Reducing carbon emissions and enhancing crop productivity through strip intercropping with improved agricultural practices in an arid area. J. Clean. Prod. 166, 197–208 (2017).Article 

Google Scholar 
22.Hou, R., Zhu, O., Wilson, G. V., Li, Y. & Li, H. Response of carbon dioxide emissions to warming under no-till and conventional till systems. Soil Sci. Soc. Am. J. 78, 1434–1441 (2014).Article 
CAS 

Google Scholar 
23.Yin, W. et al. Integrated double mulching practices optimizes soil temperature and improves soil water utilization in arid environments. Int. J. Biometeorol. 60, 1423–1437 (2016).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Lu, X., Lu, X., Tanveer, S. K., Wen, X. & Liao, Y. Effects of tillage management on soil CO2 emission and wheat yield under rain-fed conditions. Soil Res. 54, 38–48 (2016).25.Luo, Z., Wang, E. & Sun, O. J. Can no-tillage stimulate carbon sequestration in agricultural soils? A meta-analysis of paired experiments. Agr. Ecosyst. Environ. 139, 224–231 (2010).CAS 
Article 

Google Scholar 
26.Akhtar, K., Wang, W., Ren, G., Khan, A. & Yang, G. Changes in soil enzymes, soil properties, and maize crop productivity under wheat straw mulching in Guanzhong, China. Soil Till. Res. 182, 94–102 (2018).27.Yang, C., Huang, G., Qiang, C. & Luo, Z. Water use and yield of wheat/maize intercropping under alternate irrigation in the oasis field of northwest China. Field Crops Res. 124, 426–432 (2011).Article 

Google Scholar 
28.Zhou, L. et al. Ridge-furrow and plastic-mulching tillage enhances maize–soil interactions: Opportunities and challenges in a semiarid agroecosystem. Field Crops Res. 126, 181–188 (2012).Article 

Google Scholar 
29.Zhou, L., Li, F., Jin, S. & Song, Y. How two ridges and the furrow mulched with plastic film affect soil water, soil temperature and yield of maize on the semiarid Loess Plateau of China. Field Crops Res. 113, 41–47 (2009).Article 

Google Scholar 
30.Cuello, J. P., Hwang, H. Y., Gutierrez, J., Kim, S. Y. & Kim, P. J. Impact of plastic film mulching on increasing greenhouse gas emissions in temperate upland soil during maize cultivation. Appl. Soil Ecol. 91, 48–57 (2015).Article 

Google Scholar 
31.Bu, L. D. et al. Source–sink capacity responsible for higher maize yield with removal of plastic film. Agron. J. 105, 591–598 (2013).Article 

Google Scholar 
32.Li, Y. S. et al. Influence of continuous plastic film mulching on yield, water use efficiency and soil properties of rice fields under non-flooding condition. Soil Till. Res. 93, 370–378 (2007).Article 

Google Scholar 
33.Liu, Q. et al. Plastic-film mulching and urea types affect soil CO2 emissions and grain yield in spring maize on the Loess Plateau, China. Sci. Rep. 6, 28150 (2016).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
34.Sial, T. et al. Co-application of milk tea waste and NPK fertilizers to improve sandy soil biochemical properties and wheat growth. Molecules 24, 423–440 (2019).35.Willey, R. W. Resource use in intercropping systems. Agric. Water Manage. 17, 215–231 (2007).Article 

Google Scholar 
36.Li, L. et al. Wheat/maize or wheat/soybean strip intercropping: I. Yield advantage and interspecific interactions on nutrients. Field Crops Res. 71, 123–137 (2001).37.Yin, W. et al. Straw retention combined with plastic mulching improves compensation of intercropped maize in arid environment. Field Crops Res. 204, 42–51 (2017).Article 

Google Scholar 
38.Kashif, A., Wang, W., Ahmad, K., Ren, G. & Yang, G. Wheat straw mulching with fertilizer nitrogen: An approach for improving soil water storage and maize crop productivity. Plant Soil Environ. 64, 330–337 (2018).39.Ussiri, D. & Lal, R. Long-term tillage effects on soil carbon storage and carbon dioxide emissions in continuous corn cropping system from an alfisol in Ohio. Soil Till. Res. 104, 39–47 (2009).Article 

Google Scholar 
40.Wu, Y., Huang, F., Jia, Z., Ren, X. & Cai, T. Response of soil water, temperature, and maize (Zea may L.) production to different plastic film mulching patterns in semi-arid areas of northwest China. Soil Till. Res. 166, 113–121 (2017).Article 

Google Scholar 
41.Liu, J. et al. Response of nitrous oxide emission to soil mulching and nitrogen fertilization in semi-arid farmland. Agric. Ecosyst. Environ. 188, 20–28 (2014).42.Ullah, A., Khan, D., Khan, I. & Zheng, S. Does agricultural ecosystem cause environmental pollution in Pakistan? Promise and menace. Environ. Sci. Pollut. R. 25, 13938–13955 (2018).CAS 
Article 

Google Scholar 
43.Allison, S. D., Wallenstein, M. & Bradford, M. A. Soil-carbon response to warming dependent on microbial physiology. Nat. Geosci. 3, 336–340 (2010).ADS 
CAS 
Article 

Google Scholar 
44.Chang, S. X., Zheng, S. & Thomas, B. R. Soil respiration and its temperature sensitivity in agricultural and afforested poplar plantation systems in northern Alberta. Biol. Fert. Soils 52, 629–641 (2016).CAS 
Article 

Google Scholar 
45.Ding, W., Yan, C., Cai, Z., Yagi, K. & Zheng, X. Soil respiration under maize crops: Effects of water, temperature, and nitrogen fertilization. Soil Sci. Soc. Am. J. 71, 944–951 (2007).ADS 
CAS 
Article 

Google Scholar 
46.Li, L. J. et al. Soil CO2 emissions from a cultivated Mollisol: Effects of organic amendments, soil temperature, and moisture. Eur. J. Soil Bio. 55, 83–90 (2013).Article 
CAS 

Google Scholar 
47.Kong, D., Liu, N., Wang, W., Akhtar, K. & Ren, G. Soil respiration from fields under three crop rotation treatments and three straw retention treatments. PLoS ONE 14, e0219253 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Chen, C. R., Condron, L. M., Xu, Z. H., Davis, M. R. & Sherlock, R. R. Root, rhizosphere and root-free respiration in soils under grassland and forest plants. Eur. J. Agron. 57, 58–66 (2010).
Google Scholar 
49.Zhou, Z. et al. Predicting soil respiration using carbon stock in roots, litter and soil organic matter in forests of Loess Plateau in China. Soil Biol. Biochem. 57, 135–143 (2013).CAS 
Article 

Google Scholar 
50.Zhang, F., Li, M., Zhang, W., Li, F. & Qi, J. Ridge–furrow mulched with plastic film increases little in carbon dioxide efflux but much significant in biomass in a semiarid rainfed farming system. Agric. Forest Meteorol. 244–245, 33–41 (2017).ADS 
Article 

Google Scholar 
51.Malhi, S. S., Lemke, R., Wang, Z. H., Chhabra, B. S. J. S. & Research, T. Tillage, nitrogen and crop residue effects on crop yield, nutrient uptake, soil quality, and greenhouse gas emissions. Soil Till. Res. 90, 171–183 (2006).Article 

Google Scholar 
52.Khan, I., Lei, H., Shah, A. A., Khan, I. & Muhammad, I. Climate change impact assessment, flood management, and mitigation strategies in Pakistan for sustainable future. Environ. Sci. Pollut. R. 28, 29720–29731 (2021).53.Gan, Y. T., Siddique, K., Turner, N. C., Li, X. G. & Liu, L. P. Ridge-furrow mulching systems—An innovative technique for boosting crop productivity in semiarid rain-fed environments. Adv. Agron. 118, 429–476 (2013).Article 

Google Scholar 
54.Ramakrishna, A., Tam, H. M., Wani, S. P. & Long, T. D. Effect of mulch on soil temperature, moisture, weed infestation and yield of groundnut in northern Vietnam. Field Crops Res. 95, 115–125 (2006).Article 

Google Scholar 
55.Liu, X. E., Li, X. G., Long, H., Yong, P. W. & Li, F. M. Film-mulched ridge–furrow management increases maize productivity and sustains soil organic carbon in a dryland cropping system. Soil Sci. Soc. Am. J. 78, 1434–1441 (2014).ADS 
Article 
CAS 

Google Scholar  More

1.Madsen, E. L. Identifying microorganisms responsible for ecologically significant biogeochemical processes. Nat. Rev. Micro. 3, 439 (2005).CAS 
Article 

Google Scholar 
2.Bell, T., Newman, J. A., Silverman, B. W., Turner, S. L. & Lilley, A. K. The contribution of species richness and composition to bacterial services. Nature 436, 1157–1160 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

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

Google Scholar 
4.Galand, P. E., Pereira, O., Hochart, C., Auguet, J. C. & Debroas, D. A strong link between marine microbial community composition and function challenges the idea of functional redundancy. ISME J. 12, 2470 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Galand, P. E., Salter, I. & Kalenitchenko, D. Ecosystem productivity is associated with bacterial phylogenetic distance in surface marine waters. Mol. Ecol. 24, 5785–5795 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Chase, J. M. Community assembly: When should history matter?. Oecologia 136, 489–498 (2003).ADS 
PubMed 
Article 
PubMed Central 

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

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

Google Scholar 
9.Hanson, C. A., Fuhrman, J. A., Horner-Devine, M. C. & Martiny, J. B. Beyond biogeographic patterns: Processes shaping the microbial landscape. Nat. Rev. Micro. 10, 497 (2012).CAS 
Article 

Google Scholar 
10.Sunagawa, S. et al. Structure and function of the global ocean microbiome. Science 348, 1261359 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
11.Fukami, T. Historical contingency in community assembly: Integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).Article 

Google Scholar 
12.Martiny, J. B. H. et al. Microbial biogeography: Putting microorganisms on the map. Nat. Rev. Micro. 4, 102 (2006).CAS 
Article 

Google Scholar 
13.Hawkes, C. V. & Keitt, T. H. Resilience vs. historical contingency in microbial responses to environmental change. Ecol. Lett. 18, 612–625 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Bouskill, N. J. et al. Pre-exposure to drought increases the resistance of tropical forest soil bacterial communities to extended drought. ISME J. 7, 384 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Ge, Y. et al. Differences in soil bacterial diversity: Driven by contemporary disturbances or historical contingencies?. ISME J. 2, 254 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Rousk, J., Smith, A. R. & Jones, D. L. Investigating the long-term legacy of drought and warming on the soil microbial community across five European shrubland ecosystems. Glob. Change Biol. 19, 3872–3884 (2013).ADS 
Article 

Google Scholar 
17.Langenheder, S., Lindström, E. S. & Tranvik, L. J. Structure and function of bacterial communities emerging from different sources under identical conditions. Appl. Environ. Microbiol. 72, 212–220 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Langenheder, S., Lindström, E. S. & Tranvik, L. J. Weak coupling between community composition and functioning of aquatic bacteria. Limnol. Oceanogr. 50, 957–967 (2005).ADS 
Article 

Google Scholar 
19.Vass, M. & Langenheder, S. The legacy of the past: Effects of historical processes on microbial metacommunities. Aquat. Microb. Ecol. 79, 13–19 (2017).Article 

Google Scholar 
20.Svoboda, P., Lindström, E. S., Osman, O. A. & Langenheder, S. Dispersal timing determines the importance of priority effects in bacterial communities. ISME J. 12, 644 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Rummens, K., De Meester, L. & Souffreau, C. Inoculation history affects community composition in experimental freshwater bacterioplankton communities. Environ. Microbiol. 20, 1120–1133 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Andersson, M. G., Berga, M., Lindström, E. S. & Langenheder, S. The spatial structure of bacterial communities is influenced by historical environmental conditions. Ecology 95, 1134–1140 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Vyverman, W. et al. Historical processes constrain patterns in global diatom diversity. Ecology 88, 1924–1931 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
24.Sefbom, J., Sassenhagen, I., Rengefors, K. & Godhe, A. Priority effects in a planktonic bloom-forming marine diatom. Biol. Lett. 11, 20150184 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
25.Kalenitchenko, D. et al. Ecological succession leads to chemosynthesis in mats colonizing wood in sea water. ISME J. 10, 2246–2258 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Kalenitchenko, D., Le Bris, N., Peru, E. & Galand, P. E. Ultrarare marine microbes contribute to key sulphur-related ecosystem functions. Mol. Ecol. 27, 1494–1504 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Ghiglione, J. F. et al. Role of environmental factors for the vertical distribution (0–1000 m) of marine bacterial communities in the NW Mediterranean Sea. Biogeosciences 5, 1751–1764 (2008).ADS 
CAS 
Article 

Google Scholar 
28.Ghiglione, J.-F. et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc. Natl. Acad. Sci. USA 109, 17633–17638 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell 179, 1068-1083.e1021 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Kalenitchenko, D. et al. The early conversion of deep-sea wood falls into chemosynthetic hotspots revealed by in situ monitoring. Sci. Rep. 8, 907. https://doi.org/10.1038/s41598-017-17463-2 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
31.Kalenitchenko, D. et al. Temporal and spatial constraints on community assembly during microbial colonization of wood in seawater. ISME J. 9, 2657–2670 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
32.Kalenitchenko, D. et al. Bacteria alone establish the chemical basis of the wood-fall chemosynthetic ecosystem in the deep-sea. ISME J. 12, 367–379 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
33.Galand, P., Salter, I. & Kalenitchenko, D. Microbial productivity is associated with phylogenetic distance in surface marine waters. Mol. Ecol. 24, 5785–5795 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196. https://doi.org/10.1093/nar/gkm864 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Cox, M. P., Peterson, D. A. & Biggs, P. J. SolexaQA: At-a-glance quality assessment of Illumina second-generation sequencing data. BMC Bioinform. 11, 485. https://doi.org/10.1186/1471-2105-11-485 (2010).Article 

Google Scholar 
37.Rho, M., Tang, H. & Ye, Y. FragGeneScan: Predicting genes in short and error-prone reads. Nucleic Acids Res. https://doi.org/10.1093/nar/gkq747 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Wilke, A. et al. The M5nr: A novel non-redundant database containing protein sequences and annotations from multiple sources and associated tools. BMC Bioinform. 13, 141. https://doi.org/10.1186/1471-2105-13-141 (2012).CAS 
Article 

Google Scholar 
39.Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M. & Tanabe, M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 44, D457–D462 (2016).CAS 
Article 

Google Scholar 
40.Anders, S. & Huber, W. Differential expression analysis for sequence count data. Nat Précéd 1–1 https://doi.org/10.1038/npre.2010.4282.1 (2010).Article 

Google Scholar 
41.Meyer, F. et al. The metagenomics RAST server—A public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 9, 386. https://doi.org/10.1186/1471-2105-9-386 (2008).CAS 
Article 

Google Scholar 
42.Dixon, P. VEGAN, a package of R functions for community ecology. J Veg Sci 14, 927–930 (2003).Article 

Google Scholar 
43.Blanchette, R. A., Nilsson, T., Daniel, G. & Abad, A. Biological Degradation of Wood. in vol. 225, 141–174 (American Chemical Society, 1989).
Google Scholar 
44.Fagervold, S. K. et al. Microbial communities associated with the degradation of oak wood in the Blanes submarine canyon and its adjacent open slope (NW Mediterranean). Prog. Oceanogr. 118, 137–143. https://doi.org/10.1016/j.pocean.2013.07.012 (2013).ADS 
Article 

Google Scholar 
45.Sommer, U. Convergent succession of phytoplankton in microcosms with different inoculum species composition. Oecologia 87, 171–179 (1991).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Weiher, E. & Keddy, P. A. The assembly of experimental wetland plant communities. Oikos 73, 323–335 (1995).Article 

Google Scholar 
47.Wilson, J. B. et al. A test of community reassembly using the exotic communities of New Zealand roadsides in comparison to British roadsides. J. Ecol. 88, 757–764 (2000).Article 

Google Scholar 
48.Kodric-Brown, A. & Brown, J. H. Highly structured fish communities in Australian desert springs. Ecology 74, 1847–1855 (1993).Article 

Google Scholar 
49.Grover, J. P. & Lawton, J. H. Experimental studies on community convergence and alternative stable states: Comments on a paper by Drake et al. J. Anim. Ecol. 63, 484–487 (1994).Article 

Google Scholar 
50.Lawler, S. P. Direct and indirect effects in microcosm communities of protists. Oecologia 93, 184–190 (1993).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Chase, J. M. Experimental evidence for alternative stable equilibria in a benthic pond food web. Ecol. Lett. 6, 733–741 (2003).Article 

Google Scholar 
52.Petraitis, P. S. & Latham, R. E. The importance of scale in testing the origins of alternative community states. Ecology 80, 429–442 (1999).Article 

Google Scholar 
53.Hiscox, J. et al. Priority effects during fungal community establishment in beech wood. ISME J. 9, 2246 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
54.Fukami, T. et al. Assembly history dictates ecosystem functioning: evidence from wood decomposer communities. Ecol. Lett. 13, 675–684 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
55.Dhami, M. K., Hartwig, T. & Fukami, T. Genetic basis of priority effects: Insights from nectar yeast. Proc. R. Soc. Lond. B. 283, 20161455 (2016).
Google Scholar 
56.Fukami, T. & Morin, P. J. Productivity–biodiversity relationships depend on the history of community assembly. Nature 424, 423 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Khelaifia, S. et al. Desulfovibrio piezophilus sp. nov., a piezophilic, sulfate-reducing bacterium isolated from wood falls in the Mediterranean Sea. Int. J. Syst. Evol. Micr. 61, 2706–2711 (2011).CAS 
Article 

Google Scholar 
58.Sievert, S. M., Wieringa, E. B., Wirsen, C. O. & Taylor, C. D. Growth and mechanism of filamentous-sulfur formation by Candidatus Arcobacter sulfidicus in opposing oxygen-sulfide gradients. Environ. Microbiol. 9, 271–276 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More