Philippa Kaur

The data were recorded under the DarwinCore standard55,56 in a matrix named “Benthic biota of CIMAR-Fiordos and Southern Ice Field Cruises”58. The occurrence dataset contains direct basic information (description, scope [temporal, geographic and taxonomic], methodology, bibliography, contacts, data description, GBIF registration and citation), project details, metrics (taxonomy and occurrences classification), activity (citations and download events) and download options. The following data fields were occupied:Column 1: “occurrenceID” (single indicator of the biological record indicating the cruise and correlative record).Column 2: “basisOfRecord” (“PreservedSpecimen” for occurrence records with catalogue number of scientific collection, “MaterialCitation” for any literature record).Column 3: “institutionCode” (The acronym in use by the institution having custody of the sample or information referred to in the record).Column 4: “collectionCode” (The name of the cruise).Column 5: “catalogNumber” (The repository number in museums or correlative number).Column 6: “type” (All records entered as “text”).Column 7: “language” (Spanish, English or both).Column 8: “institutionID” (The identifier for the institution having custody of the sample or information referred to in the record).Column 9: “collectionID” (The identifier for the collection or dataset from which the record was derived).Column 10: “datasetID” (The code “CONA-benthic-biota-database” for entire database).Column 11: “recordedBy” (Author/s who recorded the original occurrence [publication source]).Column 12: “individualCount” (Number of individuals recorded).Column 13: “associatedReferences” (Publication source [report and/or paper/s] for each record).Column 14: “samplingProtocol” (The sampling gear for each record).Column 15: “eventDate” (The date-time or interval during which the record occurred).Column 16: “eventRemarks” (Comments or notes about the event).Column 17: “continent” (Location).Column 18: “country” (Location).Column 19: “countryCode” (The standard code for the country in which the location occurs).Column 20: “stateProvince” (Location, refers to the Administrative Region of Chile).Column 21: “county” (Location, refers to the Administrative Province of Chile).Column 22: “municipality” (Location, refers to the Administrative Commune of Chile).Column 23: “locality” (The specific name of the place).Column 24: “verbatimLocality” (The original textual description of the place).Column 25: “verbatimDepth” (The original description of the depth).Column 26: “minimumDepthInMeters” (The shallowest depth of a range of depths).Column 27: “maximumDepthInMeters” (The deepest depth of a range of depths).Column 28: “locationRemarks” (The name of the sample station of the cruise).Column 29: “verbatimLatitude” (The verbatim original latitude of the location).Column 30: “verbatimLongitude” (The verbatim original longitude of the location).Column 31: “verbatimCoordinateSystem” (The coordinate format for the “verbatimLatitude” and “verbatimLongitude” or the “verbatimCoordinates” of the location).Column 32: “verbatimSRS” (The spatial reference system [SRS] upon which coordinates given in “verbatimLatitude” and “verbatimLongitude” are based)Column 33: “decimalLatitude” (The geographic latitude in decimal degrees).Column 34: “decimalLongitude” (The geographic longitude in decimal degrees).Column 35: “geodeticDatum” (The spatial reference system [SRS] upon which the geographic coordinates given in “decimalLatitude” and “decimalLongitude” was based).Column 36: “coordinateUncertaintyInMeters” (The horizontal distance from the given “decimalLatitude” and “decimalLongitude” describing the smallest circle containing the whole of the location).Column 37: “georeferenceRemarks” (Notes about the spatial description determination).Column 38: “identifiedBy” (Responsible for recording the original occurrence [publication source]).Column 39: “dateIdentified” (The date-time or interval during which the identification occurred.)Column 40: “identificationQualifier” (A taxonomic determination [e.g., “sp.”, “cf.”]).Column 41: “scientificNameID” (An identifier for the nomenclatural details of a scientific name).Column 42: “scientificName” (The name of species or taxon of the occurrence record).Column 43: “kingdom” (The scientific name of the kingdom in which the taxon is classified).Column 44: “phylum” (The scientific name of the phylum or division in which the taxon is classified).Column 45: “class” (The scientific name of the class in which the taxon is classified).Column 46: “order” (The scientific name of the order in which the taxon is classified).Column 47: “family” (The scientific name of the family in which the taxon is classified).Column 48: “genus” (The scientific name of the genus in which the taxon is classified).Column 49: “subgenus” (The scientific name of the subgenus in which the taxon is classified).Column 50: “specificEpithet” (The name of the first or species epithet of the “scientificName”).Column 51: “infraspecificEpithet” (The name of the lowest or terminal infraspecific epithet of the “scientificName”).Column 52: “taxonRank” (The taxonomic rank of the most specific name in the “scientificName”).Column 53: “scientificNameAuthorship” (The authorship information for the “scientificName” formatted according to the conventions of the applicable nomenclatural Code).Column 54: “verbatimIdentification” (A string representing the taxonomic identification as it appeared in the original record).The information sources (see Fig. 2b) provided a total of 107 publications (22 cruise reports and 85 scientific papers; see Fig. 2c). Nineteen of the 22 cruise reports reviewed provided species occurrence records8,28,29,30,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46, one provided qualitative or descriptive data, with no recorded occurrences31, and two did not provide information on benthic biota (CIMAR-9 and −23 cruises). Of all the scientific papers reviewed, 74 provided records of species occurrences (Table 2), while 11 did not provide any record, as they were data without occurrences of geographically referenced species or with descriptive or qualitative information: Foraminifera59,60,61,62, Annelida63,64,65,66, Fishes67, Mollusca68 and Echinodermata69. The phyla with the highest number of publications were the following: Annelida (present in 18 reports and 21 papers), Mollusca (in 14 and 20), Arthropoda (in 10 and 18), Echinodermata (in 10 and 9), Chordata (in 10 and 9) and Foraminifera (in 4 and 10).Table 2 Publications with >100 occurrences, indicating the main recorded taxa.Full size tableThe information registry includes data on occurrences and number of individuals for 8,854 records (files in the database), representing 1,225 species (Fig. 3). The main taxa in terms of occurrence and number of species were Annelida (mainly Polychaeta), Foraminifera, Mollusca and Arthopoda (mainly Crustacea), together accumulating ~70% of total occurrences and ~73% of the total species (Fig. 3). The large number of recorded occurrences of Myzozoa (10%) should be highlighted, which, however, only represent about 32 species. Echinodermata represented ~8% of occurrences and 7% of species.Fig. 3Occurrences and total species by taxon, considering large taxonomic groups of the benthic biota recorded in the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences and species are represented in parentheses.Full size imageThe cruises with the highest number of occurrences were CIMAR-2 (with 1,424), followed by CIMAR-8 (1,040) and CIMAR-16 (813) (Fig. 4). Three dominant taxonomic groups were recorded in most cruises, except for cruises CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 (Fig. 4). The cruises with the highest number of species recorded were CIMAR-2 (with 335), CIMAR-3 (328) and CIMAR-8 (323) (Fig. 5). Three or fewer dominant taxonomic groups were recorded only in the CIMAR-1, CIMAR-4, CIMAR-17, CIMAR-18 and CIMAR-24 cruises (Fig. 5).Fig. 4Total occurrences and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of occurrences per dominant taxon are represented in parentheses.Full size imageFig. 5Total species and percentages per dominant taxon recorded in each of the CIMAR 1 to 25 and CDHS-1995 cruises. The absolute values of species per dominant taxon are represented in parentheses.Full size imageThe latitudinal bands 42°S and 45°S are those with the highest number of occurrences (Fig. 6), while the 56°S and 46°S bands had the fewest. The highest number of species was recorded in the 52°S and 50°S latitudinal bands, while, as with the occurrences, the lowest values corresponded to the 56°S and 46°S latitudinal bands (Fig. 6).Fig. 6Occurrences and number of species recorded by latitudinal band from the CIMAR 1 to 25 and CDHS-1995 cruises. SEP: South-eastern Pacific.Full size image More

Pimentel, D., Zuniga, R. & Morrison, D. Update on the environmental and economic costs associated with alien-invasive species in the United States. Ecol. Econ. https://doi.org/10.1016/j.ecolecon.2004.10.002 (2005).Article 

Google Scholar 
Linders, T. E. W. et al. Direct and indirect effects of invasive species: Biodiversity loss is a major mechanism by which an invasive tree affects ecosystem functioning. J. Ecol. https://doi.org/10.1111/1365-2745.13268 (2019).Article 

Google Scholar 
Campbell, F. T. The science of risk assessment for phytosanitary regulation and the impact of changing trade regulations. Bioscience https://doi.org/10.1641/0006-3568(2001)051[0148:TSORAF]2.0.CO;2 (2001).Article 

Google Scholar 
Paini, D. R. et al. Global threat to agriculture from invasive species. Proc. Natl. Acad. Sci. U. S. A. https://doi.org/10.1073/pnas.1602205113 (2016).Article 

Google Scholar 
Westphal, M. I., Browne, M., MacKinnon, K. & Noble, I. The link between international trade and the global distribution of invasive alien species. Biol. Invasions https://doi.org/10.1007/s10530-007-9138-5 (2008).Article 

Google Scholar 
Hennessey, M. et al. Phytosanitary Treatments. In The Handbook of Plant Biosecurity (eds Gordh, G. & Mckirdy, S.) 269–308 (Springer, Dordrecht, 2014).
Google Scholar 
Melvin Couey, H. & Chew, V. Confidence limits and sample size in quarantine research. J. Econ. Entomol. 79, 887–890 (1986).
Google Scholar 
Schortemeyer, M. et al. Appropriateness of probit-9 in the development of quarantine treatments for timber and timber commodities. J. Econ. Entomol. 104, 717–731 (2011).CAS 

Google Scholar 
Haack, R. A., Uzunovic, A., Hoover, K. & Cook, J. A. Seeking alternatives to probit 9 when developing treatments for wood packaging materials under ISPM No. 15. EPPO Bull. 41, 39–45 (2011).
Google Scholar 
Liqudio, N. J., Griffin, R. L. & Vick, K. W. Quarantine security for commodities: current approaches and potential strategies. In Proceedings of Joint Workshops of the Agricultural Research Service and the Animal and Plant Health Inspection Service, June 5–9 and July 31 -August 5, 1995 56 (1997).Follett, P. A. Phytosanitary irradiation for fresh horticultural commodities: Generic treatments, current issues, and next steps. Stewart Postharvest Rev. 3, 1–7 (2014).MathSciNet 

Google Scholar 
Hallman, G. J. & Loaharanu, P. Generic ionizing radiation quarantine treatments against fruit flies (Diptera: Tephritidae) proposed. J. Econ. Entomol. 95, 893–901 (2002).
Google Scholar 
Follett, P. A. & Armstrong, J. W. Revised irradiation doses to control melon fly, mediterranean fruit fly, and oriental fruit fly (Diptera: Tephritidae) and a generic dose for tephritid fruit flies. J. Econ. Entomol. 97, 1254–1262 (2004).
Google Scholar 
Follett, P. A. & Snook, K. Irradiation for quarantine control of the invasive light brown apple moth (Lepidoptera: Tortricidae) and a generic dose for tortricid eggs and larvae. J. Econ. Entomol. 105, 1971–1978 (2013).
Google Scholar 
Hallman, G. J., Arthur, V., Blackburn, C. M. & Parker, A. G. The case for a generic phytosanitary irradiation dose of 250Gy for Lepidoptera eggs and larvae. Radiat. Phys. Chem. 89, 70–75 (2013).ADS 
CAS 

Google Scholar 
Hallman, G. J. Generic phytosanitary irradiation dose of 300 Gy proposed for the Insecta excluding pupal and adult Lepidoptera. Florida Entomol. 99, 206–210 (2016).
Google Scholar 
IPPC. ISPM 28. Annex 39. Irradiation treatment for the genus Anastrepha. 1–6 (2021).IPPC. ISPM 28. Annex 7. Irradiation Treatment for fruit flies of the family Tephritidae (generic). 1–6 (2021).Posthuma, L., Suter, G. W. & Traas, T. P. Species sensitivity distributions in ecotoxicology. Species sensitivity distributions in ecotoxicology (CRC Press, 2002). https://doi.org/10.1201/9781420032314.Book 

Google Scholar 
Newman, M. C. et al. Applying species-sensitivity distributions in ecological risk assessment: Assumptions of distribution type and sufficient numbers of species. Environ. Toxicol. Chem. 19, 508–515 (2000).CAS 

Google Scholar 
van Straalen, N. M. & van Leeuwen, C. J. European history of species sensitivity distributions. In Species Sensitivity Distributions in Ecotoxicology 43–60 (CRC Press, 2001). Doi:https://doi.org/10.1201/9781420032314.ch3.ANZECC & ARMCANZ. Australian and New Zealand guidelines for fresh and marine water quality. aquatic ecosystems. Aust. New Zeal. Environ. Conserv. Counc. Agric. Resour. Manag. Counc. Aust. New Zeal. 1–103 (2000).Aldenberg, T. & Jaworska, J. S. Uncertainty of the hazardous concentration and fraction affected for normal species sensitivity distributions. Ecotoxicol. Environ. Saf. 46, 1–18 (2000).CAS 

Google Scholar 
Hallman, G. J. Generic phytosanitary irradiation treatment for “true weevils” (Coleoptera: Curculionidae) infesting fresh commodities. Florida Entomol. 99, 197–201 (2016).
Google Scholar 
Follett, P. A. Irradiation for quarantine control of coffee berry borer, hypothenemus hampei (coleoptera: Curculionidae: Scolytinae) in coffee and a proposed generic dose for snout beetles (coleoptera: Curculionoidea). J. Econ. Entomol. 111, 1633–1637 (2018).CAS 

Google Scholar 
Earle, N. W., Simmons, L. A. & Nilakhe, S. S. Laboratory studies of sterility and competitiveness of boll weevils irradiated in an atmosphere of nitrogen, carbon dioxide, or air. J. Econ. Entomol. 72, 687–691 (1979).
Google Scholar 
Follett, P. A., McQuate, G. T., Sylva, C. D. & Swedman, A. Sensitivity of the quarantine pest rough Sweetpotato weevil, Blosyrus asellus to postharvest irradiation treatment. Proc. Hawaiian Entomol. Soc. 48, 23–27 (2016).
Google Scholar 
Hallman, G. J. Ionizing irradiation quarantine treatment against plum curculio (Coleoptera: Curculionidae). J. Econ. Entomol. 96, 1399–1404 (2003).
Google Scholar 
Jacklin, S. W., Richardson, E. C. & Yonce, C. E. Substerilizing doses of gamma irradiation to produce population suppression in plum curculio1. J. Econ. Entomol. 63, 1053–1057 (1970).
Google Scholar 
Yoshida, T., Fukami, J. I., Fukunaga, K. & Matsuyama, A. Control of harmful insects in timbers by irradiation: doses required for sterilization and inhibition of emergence of the minute pine bark beetle, Cryphalus fulvus. Jpn. J. Appl. Entomol. Zool. 18, 52–58 (1974).
Google Scholar 
Follett, P. A. Irradiation as a methyl bromide alternative for postharvest control of Omphisa anastomosalis (Lepidoptera: Pyralidae) and euscepes postfasciatus and cylas formicarius elegantulus (Coleoptera: Curculionidae) in sweet potatoes. J. Econ. Entomol. 99, 32–37 (2006).
Google Scholar 
Gould, W. P. & Hallman, G. J. Irradiation disinfestation of diaprepes root weevil (Coleoptera: Curculionidae) and papaya fruit fly (Diptera: Tephritidae). Florida Entomol. 87, 391–392 (2004).
Google Scholar 
van Haandel, A. et al. Tolerance of Hylurgus ligniperda (F.) (Coleoptera: Scolytinae) and Arhopalus ferus (Mulsant) (Coleoptera: Cerambycidae) to ionising radiation: a comparison with existing generic radiation phytosanitary treatments. New Zeal. J. For. Sci. 47, 1–9 (2017).Burgess, E. E. & Bennett, S. E. Sterilization of the male alfalfa weevil (Hypera postica: Curculionidae) by X-Radiation. J. Econ. Entomol. 59, 268–270 (1966).
Google Scholar 
Wood, D. L. & Stark, R. W. The effects of gamma radiation on the biology and behavior of adult ips confusus (LeConte) (Coleoptera: Scolytidae). Can. Entomol. 98, 1–10 (1966).
Google Scholar 
Wang, X. et al. Effect of X-ray (9 MeV) irradiation on the development and propagation of Ips sexdentatus. Plant Quar. 25, 28–31 (2011).
Google Scholar 
Zhan, G. et al. Effect of irradiation on development and propagation of larch bark beetle (Coleoptera: Scolytoidea). J. Nucl. Agric. Sci. 25, 1200–1205 (2011).
Google Scholar 
Gerstle, C. & Sazo, L. Efecto de las radiaciones de Cesio 137 sobre la fertilidad de hembras de Naupactus xanthographus (Germar) (Coleoptera: Curculionidae). Cienc. e Investig. Agrar. 16, 69–73 (1989).
Google Scholar 
Manoto, E. C., Obra, G. B., Reyes, M. R. & Resilva, S. S. Irradiation as a quarantine treatment for ornamentals. IAEA-Tecdoc 1082, 81–91 (1999).
Google Scholar 
Duvenhage, A. J. & Johnson, S. A. The potential of irradiation as a postharvest disinfestation treatment against phlyctinus callosus (Coleoptera: Curculionidae). J. Econ. Entomol. 107, 154–160 (2014).CAS 

Google Scholar 
Jaynes, A. & Godwin, P. A. Sterilization of the white-pine weevil with gamma radiation. J. Econ. Entomol. 50, 393–395 (1957).CAS 

Google Scholar 
Aldryhim, Y. N. & Adam, E. E. Efficacy of gamma irradiation against Sitophilus granarius (L.) (Coleoptera: Curculionidae). J. Stored Prod. Res. 35, 225–232 (1999).
Google Scholar 
Follett, P. A. et al. Irradiation quarantine treatment for control of Sitophilus oryzae (Coleoptera: Curculionidae) in rice. J. Stored Prod. Res. 52, 63–67 (2013).
Google Scholar 
Hu, T., Chen, C. C. & Peng, W. K. Lethal effect of gamma irradiation on Sitophilus zeamais (Coleoptera: Curculionidae). Formos. Entomol. 23, 145–150 (2003).
Google Scholar 
Arthur, V. & Wiendl, F. M. Comportamento e competitividade sexual de adultos de Sphenophorus levis Vaurie, 1978 (col., Curculionidae), uma praga da cana-de-açucar, irradiados com radiações gama do cobaldo-60. Brazilian J. Agric. 68, 57–66 (1993).
Google Scholar 
Obra, G. B., Resilva, S. S., Follett, P. A. & Lorenzana, L. R. J. Large-scale confirmatory tests of a phytosanitary irradiation treatment against Sternochetus frigidus (Coleoptera: Curculionidae) in Philippine mango. J. Econ. Entomol. 107, 161–165 (2014).
Google Scholar 
Seo, S. T. et al. Mango weevil: Cobalt-60 γ-irradiation of packaged mangoes. J. Econ. Entomol. 67, 504–505 (1974).
Google Scholar 
Yoshida, T., Fukami, J. I., Fukunaga, K. & Matsuyama, A. Effects of gamma radiation on Xyleborus perforans (Wollaston) pupae and adults. J. Pestic. Sci. 2, 413–420 (1977).
Google Scholar 
Yoshida, T., Fukami, J. I., Fukunaga, K. & Matsuyama, A. Control of the harmful insects in timbers by irradiation: Doses required for kill, sterilization and inhibition of emergence in three species of ambrosia beetles (Xyleborini) in Japan. Jpn. J. Appl. Entomol. Zool. 19, 193–202 (1975).
Google Scholar 
Follett, P. A. & McQuate, G. T. Accelerated development of quarantine treatments for insects on poor hosts. J. Econ. Entomol. https://doi.org/10.1603/0022-0493-94.5.1005 (2001).Article 

Google Scholar 
Plazzi, F., Ferrucci, R. R. & Passamonti, M. Phylogenetic representativeness: A new method for evaluating taxon sampling in evolutionary studies. BMC Bioinform. 11, 1–15 (2010).
Google Scholar 
Moore, D. R. J., Priest, C. D., Galic, N., Brain, R. A. & Rodney, S. I. Correcting for phylogenetic autocorrelation in species sensitivity distributions. Integr. Environ. Assess. Manag. 16, (2020).Carr, G. J. & Belanger, S. E. SSDs revisited: Part I—A framework for sample size guidance on species sensitivity distribution analysis. Environ. Toxicol. Chem. 38, 1514–1525 (2019).CAS 

Google Scholar 
Wheeler, J. R., Grist, E. P. M., Leung, K. M. Y., Morritt, D. & Crane, M. Species sensitivity distributions: Data and model choice. Mar. Pollut. Bull. 45, 192–202 (2002).CAS 

Google Scholar 
Duboudin, C., Ciffroy, P. & Magaud, H. Acute-to-chronic species sensitivity distribution extrapolation. Environ. Toxicol. Chem. 23, 1774–1785 (2004).CAS 

Google Scholar 
Esteves, S. M. et al. Can we predict diatoms herbicide sensitivities with phylogeny? Influence of intraspecific and interspecific variability. Ecotoxicology 26, 1065–1077 (2017).CAS 

Google Scholar 
Hiki, K. & Iwasaki, Y. Can we reasonably predict chronic species sensitivity distributions from acute species sensitivity distributions?. Environ. Sci. Technol. 54, 13131–13136 (2020).ADS 
CAS 

Google Scholar 
Baird, D. J. & Van den Brink, P. J. Using biological traits to predict species sensitivity to toxic substances. Ecotoxicol. Environ. Saf. 67, 296–301 (2007).CAS 

Google Scholar 
Guénard, G., von der Ohe, P. C., Walker, S. C., Lek, S. & Legendre, P. Using phylogenetic information and chemical properties to predict species tolerances to pesticides. Proc. R. Soc. B Biol. Sci. 281, 1–9 (2014).
Google Scholar 
Larras, F., Keck, F., Montuelle, B., Rimet, F. & Bouchez, A. Linking diatom sensitivity to herbicides to phylogeny: A step forward for biomonitoring?. Environ. Sci. Technol. 48, 1921–1930 (2014).ADS 
CAS 

Google Scholar 
Hayashi, T. I. & Kashiwagi, N. A bayesian method for deriving species-sensitivity distributions: Selecting the best-fit tolerance distributions of taxonomic groups. Hum. Ecol. Risk Assess. 16, 251–263 (2010).CAS 

Google Scholar 
Xu, F. L. et al. Key issues for the development and application of the species sensitivity distribution (SSD) model for ecological risk assessment. Ecol. Indic. 54, 227–237 (2015).CAS 

Google Scholar 
Dowse, R., Tang, D., Palmer, C. G. & Kefford, B. J. Risk assessment using the species sensitivity distribution method: Data quality versus data quantity. Environ. Toxicol. Chem. 32, 1360–1369 (2013).CAS 

Google Scholar 
Dias, V. S. et al. Relative tolerance of three morphotypes of the anastrepha fraterculus complex (Diptera: Tephritidae) to cold phytosanitary Treatment. J. Econ. Entomol. 113, 1176–1182 (2020).CAS 

Google Scholar 
Myers, S. W., Cancio-Martinez, E., Hallman, G. J., Fontenot, E. A. & Vreysen, M. J. B. Relative tolerance of six Bactrocera (Diptera: Tephritidae) species to phytosanitary cold treatment. J. Econ. Entomol. 109, 2341–2347 (2016).
Google Scholar 
Gazit, Y., Akiva, R. & Gavriel, S. Cold tolerance of the Mediterranean fruit fly in date and mandarin. J. Econ. Entomol. 107, 1745–1750 (2014).
Google Scholar 
Zhao, J. et al. Gamma radiation as a phytosanitary treatment against larvae and pupae of Bactrocera dorsalis (Diptera: Tephritidae) in guava fruits. Food Control 72, 360–366 (2017).
Google Scholar 
Thorley, J. & Schwarz, C. ssdtools: An R package to fit Species sensitivity distributions. J. Open Sour. Softw. 3, 1–2 (2018).
Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoritic Approach 2nd edn. (Springer, 2002). https://doi.org/10.1007/978-0-387-22456-5_7.Book 
MATH 

Google Scholar 
Mazucheli, J., Menezes, A. F. B. & Nadarajah, S. mle.tools: An R package for maximum likelihood bias correction. R. J. 9, 268–290 (2017).
Google Scholar 
Cox, D. R. & Snell, E. J. A general definition of residuals. J. R. Stat. Soc. Ser. B 30, 248–265 (1968).MathSciNet 
MATH 

Google Scholar 
Follett, P. A. Irradiation as a quarantine treatment for mango seed weevil (Coleoptera: Curculionidae). Proc. Hawaii. Entomol. Soc. 35, 95–100 (2001).
Google Scholar  More

Teuling, A. J., Seneviratne, S. I., Williams, C. & Troch, P. A. Observed timescales of evapotranspiration response to soil moisture. Geophys. Res. Lett. 33, L23403 (2006).Gao, H. et al. Climate controls how ecosystems size the root zone storage capacity at catchment scale. Geophys. Res. Lett. 41, 7916–7923 (2014).Article 

Google Scholar 
Milly, P. C. D. Climate, soil water storage, and the average annual water balance. Water Resour. Res. 30, 2143–2156 (1994).Article 

Google Scholar 
Hahm, W. J. et al. Low subsurface water storage capacity relative to annual rainfall decouples Mediterranean plant productivity and water use from rainfall variability. Geophys. Res. Lett. 46, 6544–6553 (2019).Article 

Google Scholar 
Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).Article 

Google Scholar 
Thompson, S. E. et al. Comparative hydrology across AmeriFlux sites: the variable roles of climate, vegetation, and groundwater. Water Resour. Res. 47, W00J07 (2011).Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).Article 

Google Scholar 
Hain, C. R., Crow, W. T., Anderson, M. C. & Tugrul Yilmaz, M. Diagnosing neglected soil moisture source–sink processes via a thermal infrared-based two-source energy balance model. J. Hydrometeorol. 16, 1070–1086 (2015).Article 

Google Scholar 
Rempe, D. M. & Dietrich, W. E. Direct observations of rock moisture, a hidden component of the hydrologic cycle. Proc. Natl Acad. Sci. USA 115, 2664–2669 (2018).Article 

Google Scholar 
Dawson, T. E., Jesse Hahm, W. & Crutchfield-Peters, K. Digging deeper: what the critical zone perspective adds to the study of plant ecophysiology. N. Phytol. 226, 666–671 (2020).Article 

Google Scholar 
McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).Article 

Google Scholar 
Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016).Article 

Google Scholar 
Schlemmer, L., Schär, C., Lüthi, D. & Strebel, L. A groundwater and runoff formulation for weather and climate models. J. Adv. Model. Earth Syst. 10, 1809–1832 (2018).Article 

Google Scholar 
Teuling, A. J. et al. Contrasting response of European forest and grassland energy exchange to heatwaves. Nat. Geosci. 3, 722–727 (2010).Article 

Google Scholar 
Koirala, S. et al. Global distribution of groundwater–vegetation spatial covariation. Geophys. Res. Lett. 44, 4134–4142 (2017).Article 

Google Scholar 
Esteban, E. J. L., Castilho, C. V., Melgaço, K. L. & Costa, F. R. C. The other side of droughts: wet extremes and topography as buffers of negative drought effects in an Amazonian forest. N. Phytol. 229, 1995–2006 (2021).Article 

Google Scholar 
Liu, Y., Konings, A. G., Kennedy, D. & Gentine, P. Global coordination in plant physiological and rooting strategies in response to water stress. Glob. Biogeochem. Cycles 35, e2020GB006758 (2021).Article 

Google Scholar 
Schenk, H. J. & Jackson, R. B. The global biogeography of roots. Ecol. Monogr. 72, 311–328 (2002).Article 

Google Scholar 
Canadell, J. et al. Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583–595 (1996).Article 

Google Scholar 
Weaver, J. E. & Darland, R. W. Soil–root relationships of certain native grasses in various soil types. Ecol. Monogr. 19, 303–338 (1949).Article 

Google Scholar 
Chitra-Tarak, R. et al. Hydraulically-vulnerable trees survive on deep-water access during droughts in a tropical forest. N. Phytol. 231, 1798–1813 (2021).Article 

Google Scholar 
Schenk, H. J. & Jackson, R. B. Mapping the global distribution of deep roots in relation to climate and soil characteristics. Geoderma 126, 129–140 (2005).Article 

Google Scholar 
Franklin, O. et al. Organizing principles for vegetation dynamics. Nat. Plants 6, 444–453 (2020).Article 

Google Scholar 
Kleidon, A. & Heimann, M. A method of determining rooting depth from a terrestrial biosphere model and its impacts on the global water and carbon cycle. Glob. Change Biol. 4, 275–286 (1998).Article 

Google Scholar 
Schymanski, S. J., Sivapalan, M., Roderick, M. L., Hutley, L. B. & Beringer, J. An optimality-based model of the dynamic feedbacks between natural vegetation and the water balance. Water Resour. Res. 45, W01412 (2009).Wang-Erlandsson, L. et al. Global root zone storage capacity from satellite-based evaporation. Hydrol. Earth Syst. Sci. 20, 1459–1481 (2016).Article 

Google Scholar 
Knapp, A. K. & Smith, M. D. Variation among biomes in temporal dynamics of aboveground primary production. Science 291, 481–484 (2001).Article 

Google Scholar 
Anderson, M. A two-source time-integrated model for estimating surface fluxes using thermal infrared remote sensing. Remote Sens. Environ. 60, 195–216 (1997).Article 

Google Scholar 
Hain, C. R. & Anderson, M. C. Estimating morning change in land surface temperature from MODIS day/night observations: applications for surface energy balance modeling. Geophys. Res. Lett. 44, 9723–9733 (2017).Article 

Google Scholar 
Tumber-Dávila, S. J., Schenk, H. J., Du, E. & Jackson, R. B. Plant sizes and shapes above- and belowground and their interactions with climate. New Phytol. https://nph.onlinelibrary.wiley.com/doi/abs/10.1111/nph.18031 (2022).Harmonized World Soil Database Version 1.0 (FAO, 2008).Wieder, W. Regridded Harmonized World Soil Database Version 1.2 (ORNL DAAC, 2014); https://doi.org/10.3334/ORNLDAAC/1247Balland, V., Pollacco, J. A. P. & Arp, P. A. Modeling soil hydraulic properties for a wide range of soil conditions. Ecol. Model. 219, 300–316 (2008).Article 

Google Scholar 
Agee, E. et al. Root lateral interactions drive water uptake patterns under water limitation. Adv. Water Resour. 151, 103896 (2021).Article 

Google Scholar 
Krakauer, N. Y., Li, H. & Fan, Y. Groundwater flow across spatial scales: importance for climate modeling. Environ. Res. Lett. 9, 034003 (2014).Article 

Google Scholar 
Stoy, P. C. et al. Reviews and syntheses: turning the challenges of partitioning ecosystem evaporation and transpiration into opportunities. Biogeosciences 16, 3747–3775 (2019).Article 

Google Scholar 
Jackson, R. B., Moore, L. A., Hoffmann, W. A., Pockman, W. T. & Linder, C. R. Ecosystem rooting depth determined with caves and DNA. Proc. Natl Acad. Sci. USA 96, 11387–11392 (1999).Article 

Google Scholar 
Pelletier, J. D. et al. A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling. J. Adv. Model. Earth Syst. 8, 41–65 (2016).Article 

Google Scholar 
Parmesan, C. & Hanley, M. E. Plants and climate change: complexities and surprises. Ann. Bot. 116, 849–864 (2015).Article 

Google Scholar 
Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).Siebert, S. et al. Development and validation of the global map of irrigation areas. Hydrol. Earth Syst. Sci. 9, 535–547 (2005).Article 

Google Scholar 
Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51, 933–938 (2001).Article 

Google Scholar 
Mu, Q., Heinsch, F. A., Zhao, M. & Running, S. W. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111, 519–536 (2007).Article 

Google Scholar 
Fisher, J. B. et al. ECOSTRESS: NASA’s next generation mission to measure evapotranspiration from the international space station. Water Resour. Res. 56, e2019WR026058 (2020).Article 

Google Scholar 
Davis, T. W. et al. Simple process-led algorithms for simulating habitats (SPLASH v.1.0): robust indices of radiation, evapotranspiration and plant-available moisture. Geosci. Model Dev. 10, 689–708 (2017).Article 

Google Scholar 
Weedon, G. P. et al. The WFDEI meteorological forcing data set: WATCH forcing data methodology applied to ERA-Interim reanalysis data. Water Resour. Res. 50, 7505–7514 (2014).Article 

Google Scholar 
Orth, R., Koster, R. D. & Seneviratne, S. I. Inferring soil moisture memory from streamflow observations using a simple water balance model. J. Hydrometeorol. 14, 1773–1790 (2013).Article 

Google Scholar 
Stocker, B. cwd v.1.0: R package for cumulative water deficit calculation. Zenodo https://doi.org/10.5281/zenodo.5359053 (2021).Zhang, Y. et al. Model-based analysis of the relationship between sun-induced chlorophyll fluorescence and gross primary production for remote sensing applications. Remote Sens. Environ. 187, 145–155 (2016).Article 

Google Scholar 
Duveiller, G. et al. A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity. Earth Syst. Sci. Data 12, 1101–1116 (2020).Article 

Google Scholar 
Joiner, J. et al. Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2. Atmos. Meas. Tech. 6, 2803–2823 (2013).Article 

Google Scholar 
Köhler, P., Guanter, L. & Joiner, J. A linear method for the retrieval of sun-induced chlorophyll fluorescence from GOME-2 and SCIAMACHY data. Atmos. Meas. Tech. 8, 2589–2608 (2015).Article 

Google Scholar 
Jiang, B. et al. Validation of the surface daytime net radiation product from version 4.0 GLASS product suite. IEEE Geosci. Remote Sens. Lett. 16, 509–513 (2019).Article 

Google Scholar 
Muggeo, V. M. R. Estimating regression models with unknown break-points. Stat. Med. 22, 3055–3071 (2003).Article 

Google Scholar 
Gilleland, E. & Katz, R. W. extRemes 2.0: an extreme value analysis package in R. J. Stat. Softw. 72, 1–39 (2016).Marthews, T. R., Dadson, S. J., Lehner, B., Abele, S. & Gedney, N. High-resolution global topographic index values for use in large-scale hydrological modelling. Hydrol. Earth Syst. Sci. 19, 91–104 (2015).Article 

Google Scholar 
Etopo1, Global 1 Arc-Minute Ocean Depth and Land Elevation from the US National Geophysical Data Center (NGDC) (National Geophysical Data Center, NESDIS, NOAA and US Department of Commerce, 2011); https://doi.org/10.5065/D69Z92Z5Beven, K. J. & Kirkby, M. J. A physically based, variable contributing area model of basin hydrology. Hydrol. Sci. J. 24, 43–69 (1979).Article 

Google Scholar 
Hansen, M. C., Townshend, J. R. G., DeFries, R. S. & Carroll, M. Estimation of tree cover using MODIS data at global, continental and regional/local scales. Int. J. Remote Sens. 26, 4359–4380 (2005).Article 

Google Scholar 
Stocker, B. D. Global rooting zone water storage capacity and rooting depth estimates. Zenodo https://doi.org/10.5281/zenodo.5515246 (2021).Stocker, B. stineb/mct: v3.0: re-submission to Nature Geoscience. Zenodo https://doi.org/10.5281/zenodo.6239187 (2022). More

Aylward, F. O. et al. Microbial community transcriptional networks are conserved in three domains at ocean basin scales. Proc. Natl Acad. Sci. USA 112, 5443–5448 (2015).Article 
CAS 

Google Scholar 
Fuhrman, J. A. Microbial community structure and its functional implications. Nature 459, 193–199 (2009).Article 
CAS 

Google Scholar 
Amin, S. A., Parker, M. S. & Armbrust, E. V. Interactions between diatoms and bacteria. Microbiol. Mol. Biol. Rev. 76, 667–684 (2012).Article 
CAS 

Google Scholar 
Mayali, X. Metabolic interactions between bacteria and phytoplankton. Front. Microbiol. 9, 727 (2018).Article 

Google Scholar 
Amin, S. A. et al. Photolysis of iron–siderophore chelates promotes bacterial–algal mutualism. Proc. Natl Acad. Sci. USA 106, 17071–17076 (2009).Amin, S. A. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98 (2015).Article 
CAS 

Google Scholar 
Durham, B. P. et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc. Natl Acad. Sci. USA 112, 453 (2015).Article 
CAS 

Google Scholar 
Stocker, R. Marine microbes see a sea of gradients. Science 338, 628 (2012).Article 
CAS 

Google Scholar 
Bell, W. & Mitchell, R. Chemotactic and growth responses of marine bacteria to algal extracellular products. Biol. Bull. 143, 265–277 (1972).Article 

Google Scholar 
Azam, F. & Ammerman, J. W. in Flows of Energy and Materials in Marine Ecosystems 345–360 (Springer, 1984).Mitchell, J. G., Okubo, A. & Fuhrman, J. A. Microzones surrounding phytoplankton form the basis for a stratified marine microbial ecosystem. Nature 316, 58–59 (1985).Article 
CAS 

Google Scholar 
Seymour, J. R., Amin, S. A., Raina, J.-B. & Stocker, R. Zooming in on the phycosphere: the ecological interface for phytoplankton–bacteria relationships. Nat. Microbiol. 2, 17065 (2017).Article 
CAS 

Google Scholar 
Sonnenschein, E. C., Syit, D. A., Grossart, H.-P. & Ullrich, M. S. Chemotaxis of Marinobacter adhaerens and its impact on attachment to the diatom Thalassiosira weissflogii. Appl. Environ. Microbiol. 78, 6900–6907 (2012).Article 
CAS 

Google Scholar 
Raina, J.-B., Fernandez, V., Lambert, B., Stocker, R. & Seymour, J. R. The role of microbial motility and chemotaxis in symbiosis. Nat. Rev. Microbiol. 17, 284–294 (2019).Article 
CAS 

Google Scholar 
Seymour, J. R., Ahmed, T., Durham, W. M. & Stocker, R. Chemotactic response of marine bacteria to the extracellular products of Synechococcus and Prochlorococcus. Aquat. Microb. Ecol. 59, 161–168 (2010).Article 

Google Scholar 
Smriga, S., Fernandez, V. I., Mitchell, J. G. & Stocker, R. Chemotaxis toward phytoplankton drives organic matter partitioning among marine bacteria. Proc. Natl Acad. Sci. USA 113, 1576–1581 (2016).Article 
CAS 

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

Google Scholar 
Christie-Oleza, J. A., Sousoni, D., Lloyd, M., Armengaud, J. & Scanlan, D. J. Nutrient recycling facilitates long-term stability of marine microbial phototroph–heterotroph interactions. Nat. Microbiol. 2, 17100 (2017).Article 
CAS 

Google Scholar 
Morris, J. J., Kirkegaard, R., Szul, M. J., Johnson, Z. I. & Zinser, E. R. Facilitation of robust growth of Prochlorococcus colonies and dilute liquid cultures by ‘helper’ heterotrophic bacteria. Appl. Environ. Microbiol. 74, 4530–4534 (2008).Article 
CAS 

Google Scholar 
Sher, D., Thompson, J. W., Kashtan, N., Croal, L. & Chisholm, S. W. Response of Prochlorococcus ecotypes to co-culture with diverse marine bacteria. ISME J. 5, 1125–1132 (2011).Article 
CAS 

Google Scholar 
Aharonovich, D. & Sher, D. Transcriptional response of Prochlorococcus to co-culture with a marine Alteromonas: differences between strains and the involvement of putative infochemicals. ISME J. 10, 2892–2906 (2016).Article 
CAS 

Google Scholar 
Jackson, G. A. Simulating chemosensory responses of marine microorganisms. Limnol. Oceanogr. 32, 1253–1266 (1987).Article 
CAS 

Google Scholar 
Gärdes, A., Iversen, M. H., Grossart, H.-P., Passow, U. & Ullrich, M. S. Diatom-associated bacteria are required for aggregation of Thalassiosira weissflogii. ISME J. 5, 436–445 (2011).Article 

Google Scholar 
Al-Wahaib, D., Al-Bader, D., Al-Shaikh Abdou, D. K., Eliyas, M. & Radwan, S. S. Consistent occurrence of hydrocarbonoclastic Marinobacter strains in various cultures of picocyanobacteria from the Arabian Gulf: promising associations for biodegradation of marine oil pollution. J. Mol. Microbiol. Biotechnol. 26, 261–268 (2016).CAS 

Google Scholar 
Raina, J.-B. et al. Subcellular tracking reveals the location of dimethylsulfoniopropionate in microalgae and visualises its uptake by marine bacteria. eLife 6, e23008 (2017).Article 

Google Scholar 
Brumley, D. R. et al. Cutting through the noise: bacterial chemotaxis in marine microenvironments. Front. Mar. Sci. 7, 527 (2020).Article 

Google Scholar 
Gärdes, A. et al. Complete genome sequence of Marinobacter adhaerens type strain (HP15), a diatom-interacting marine microorganism. Stand. Genom. Sci. 3, 97–107 (2010).Article 

Google Scholar 
Moore, L. R., Post, A. F., Rocap, G. & Chisholm, S. W. Utilization of different nitrogen sources by the marine cyanobacteria Prochlorococcus and Synechococcus. Limnol. Oceanogr. 47, 989–996 (2002).Article 
CAS 

Google Scholar 
Wawrik, B., Callaghan, A. V. & Bronk, D. A. Use of inorganic and organic nitrogen by Synechococcus spp. and diatoms on the West Florida shelf as measured using stable isotope probing. Appl. Environ. Microbiol. 75, 6662–6670 (2009).Article 
CAS 

Google Scholar 
Lambert, B. S. et al. A microfluidics-based in situ chemotaxis assay to study the behaviour of aquatic microbial communities. Nat. Microbiol. 2, 1344–1349 (2017).Article 
CAS 

Google Scholar 
Raina, J.-B. et al. Chemotaxis shapes the microscale organization of the ocean’s microbiome. Nature 605, 132–138 (2022).Article 
CAS 

Google Scholar 
Brumley, D. R. et al. Bacteria push the limits of chemotactic precision to navigate dynamic chemical gradients. Proc. Natl Acad. Sci. USA 116, 10792–10797 (2019).Article 
CAS 

Google Scholar 
Myklestad, S. M. in Marine Chemistry (ed. Wangersky, P. J.) 111–148 (Springer Berlin Heidelberg, 2000).Ni, B., Colin, R., Link, H., Endres, R. G. & Sourjik, V. Growth-rate dependent resource investment in bacterial motile behavior quantitatively follows potential benefit of chemotaxis. Proc. Natl Acad. Sci. USA 117, 595–601 (2020).Article 
CAS 

Google Scholar 
Stocker, R., Seymour, J. R., Samadani, A., Hunt, D. E. & Polz, M. F. Rapid chemotactic response enables marine bacteria to exploit ephemeral microscale nutrient patches. Proc. Natl Acad. Sci. USA 105, 4209–4214 (2008).Article 
CAS 

Google Scholar 
Buitenhuis, E. et al. MAREDAT: towards a world atlas of MARine Ecosystem DATa. Earth Syst. Sci. Data 5, 227–239 (2013).Article 

Google Scholar 
Raina, J.-B. et al. Symbiosis in the microbial world: from ecology to genome evolution. Biol. Open 7, bio032524 (2018).Article 

Google Scholar 
Giardina, M. et al. Quantifying inorganic nitrogen assimilation by Synechococcus using bulk and single-cell mass spectrometry: a comparative study. Front. Microbiol. 9, 2847 (2018).Article 

Google Scholar 
Berges, J. A., Franklin, D. J. & Harrison, P. J. Evolution of an artificial seawater medium: improvements in enriched seawater, artificial water over the last two decades. J. Phycol. 37, 1138–1145 (2001).Article 

Google Scholar 
Guillard, R. R. L. in Culture of Marine Invertebrate Animals: Proceedings—1st Conference on Culture of Marine Invertebrate Animals Greenport (eds Walter, L. S. & Matoira, H. C.) 29–60 (Springer US, 1975).Kaeppel, E. C., Gärdes, A., Seebah, S., Grossart, H.-P. & Ullrich, M. S. Marinobacter adhaerens sp. nov., isolated from marine aggregates formed with the diatom Thalassiosira weissflogii. Int. J. Syst. Evolut. Microbiol. 62, 124–128 (2012).Article 
CAS 

Google Scholar 
Sonnenschein, E. C. et al. Development of a genetic system for Marinobacter adhaerens HP15 involved in marine aggregate formation by interacting with diatom cells. J. Microbiol. Methods 87, 176–183 (2011).Article 
CAS 

Google Scholar 
Marie, D., Partensky, F., Jacquet, S. & Vaulot, D. Enumeration and cell cycle analysis of natural populations of marine picoplankton by flow cytometry using the nucleic acid stain SYBR Green I. Appl. Environ. Microbiol. 63, 186–193 (1997).Article 
CAS 

Google Scholar 
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).Article 
CAS 

Google Scholar 
Hillion, F., Kilburn, M., Hoppe, P., Messenger, S. & Weber, P. K. The effect of QSA on S, C, O and Si isotopic ratio measurements. Geochim. Cosmochim. Acta 72, A377 (2008).
Google Scholar 
Popa, R. et al. Carbon and nitrogen fixation and metabolite exchange in and between individual cells of Anabaena oscillarioides. ISME J. 1, 354–360 (2007).Article 
CAS 

Google Scholar 
Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).Article 
CAS 

Google Scholar 
Clerc, E. E., Raina, J.-B., Lambert, B. S., Seymour, J. & Stocker, R. In situ chemotaxis assay to examine microbial behavior in aquatic ecosystems. JoVE https://doi.org/10.3791/61062 (2020).Ihaka, R. & Gentleman, R. R: a language for data analysis and graphics. J. Comput. Graph. Stat. 5, 299–314 (1996).
Google Scholar 
Xie, L., Lu, C. & Wu, X.-L. Marine bacterial chemoresponse to a stepwise chemoattractant stimulus. Biophys. J. 108, 766–774 (2015).Article 
CAS 

Google Scholar 
Son, K., Guasto, J. S. & Stocker, R. Bacteria can exploit a flagellar buckling instability to change direction. Nat. Phys. 9, 494–498 (2013).Article 
CAS 

Google Scholar 
Lee, C. & Bada, J. L. Amino acids in equatorial Pacific Ocean water. Earth Planet. Sci. Lett. 26, 61–68 (1975).Article 
CAS 

Google Scholar 
Yamashita, Y. & Tanoue, E. Distribution and alteration of amino acids in bulk DOM along a transect from bay to oceanic waters. Mar. Chem. 82, 145–160 (2003).Article 
CAS 

Google Scholar 
Menden-Deuer, S. & Lessard, E. J. Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol. Oceanogr. 45, 569–579 (2000).Article 
CAS 

Google Scholar 
Mullin, M. M., Sloan, P. R. & Eppley, R. W. Relationship between carbon content, cell volume and area in phytoplankton. Limnol. Oceanogr. 11, 307–311 (1966).Article 

Google Scholar  More

Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Google Scholar 
Elahi, R. et al. Recent trends in local-scale marine biodiversity reflect community structure and human impacts. Curr. Biol. 25, 1938–1943 (2015).CAS 

Google Scholar 
Harley, C. D. G. Climate change, keystone predation, and biodiversity loss. Science 334, 1124–1127 (2011).ADS 
CAS 

Google Scholar 
Poloczanska, E. S. et al. Global imprint of climate change on marine life. Nat. Clim. Change 3, 919–925 (2013).ADS 

Google Scholar 
Bellwood, D. R., Hughes, T. P., Folke, C. & Nyström, M. Confronting the coral reef crisis. Nature 429, 827–833 (2004).ADS 
CAS 

Google Scholar 
Moreno-Mateos, D. et al. Anthropogenic ecosystem disturbance and the recovery debt. Nat. Commun. 8, 14163 (2017).ADS 
CAS 

Google Scholar 
Newman, E. A. Disturbance ecology in the Anthropocene. Front. Ecol. Evol. 7, 147 (2019).
Google Scholar 
Mittelbach, G. G. et al. What is the observed relationship between species richness and productivity?. Ecology 82, 2381–2396 (2001).
Google Scholar 
van Nes, E. H. & Scheffer, M. Implications of spatial heterogeneity for catastrophic regime shifts in ecosystems. Ecology 86, 1797–1807 (2005).
Google Scholar 
Tylianakis, J. M. et al. Resource heterogeneity moderates the biodiversity-function relationship in real world ecosystems. Plos Biol. 6, e122 (2008).
Google Scholar 
Loreau, M. et al. In Metacommunities: Spatial Dynamics and Ecological Communities (eds Holyoak, M. et al.) (The University of Chicago Press, 2005).
Google Scholar 
Loreau, M. From Populations to Ecosystems (Princeton University Press, 2010). https://doi.org/10.1515/9781400834167.vii.Book 

Google Scholar 
Moreira, E. F., Boscolo, D. & Viana, B. F. Spatial heterogeneity regulates plant-pollinator networks across multiple landscape scales. PLoS ONE 10, e0123628 (2015).
Google Scholar 
Costanza, J. K., Moody, A. & Peet, R. K. Multi-scale environmental heterogeneity as a predictor of plant species richness. Landsc. Ecol. 26, 851–864 (2011).
Google Scholar 
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).ADS 
CAS 

Google Scholar 
Nyström, M., Graham, N. A. J., Lokrantz, J. & Norström, A. V. Capturing the cornerstones of coral reef resilience: Linking theory to practice. Coral Reefs 27, 795–809 (2008).ADS 

Google Scholar 
Virah-Sawmy, M., Gillson, L. & Willis, K. J. How does spatial heterogeneity influence resilience to climatic changes? Ecological dynamics in southeast Madagascar. Ecol. Monogr. 79, 557–574 (2009).
Google Scholar 
Wilson, D. S. Complex interactions in metacommunities, with implications for biodiversity and higher levels of selection. Ecology 73, 1984–2000 (1992).
Google Scholar 
Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613 (2004).
Google Scholar 
Briggs, C. J. & Hoopes, M. F. Stabilizing effects in spatial parasitoid–host and predator–prey models: A review. Theor. Popul. Biol. 65, 299–315 (2004).MATH 

Google Scholar 
Wang, S., Haegeman, B. & Loreau, M. Dispersal and metapopulation stability. PeerJ 3, e1295 (2015).
Google Scholar 
Tilman, D. The ecological consequences of changes in biodiversity: A search for general principles. Ecology 80, 1455–1474 (1999).
Google Scholar 
Loreau, M., Mouquet, N. & Gonzalez, A. Biodiversity as spatial insurance in heterogeneous landscapes. Proc. Natl. Acad. Sci. 100, 12765–12770 (2003).ADS 
CAS 

Google Scholar 
Yachi, S. & Loreau, M. Biodiversity and ecosystem productivity in a fluctuating environment: The insurance hypothesis. Proc. Natl. Acad. Sci. 96, 1463–1468 (1999).ADS 
CAS 

Google Scholar 
Bouvier, T. et al. Contrasted effects of diversity and immigration on ecological insurance in marine bacterioplankton communities. PLoS ONE 7, e37620 (2012).ADS 
CAS 

Google Scholar 
Hammond, M., Loreau, M., Mazancourt, C. & Kolasa, J. Disentangling local, metapopulation, and cross-community sources of stabilization and asynchrony in metacommunities. Ecosphere 11, e03078 (2020).
Google Scholar 
Lamy, T., Legendre, P., Chancerelle, Y., Siu, G. & Claudet, J. Understanding the spatio-temporal response of coral reef fish communities to natural disturbances: Insights from beta-diversity decomposition. PLoS ONE 10, e0138696 (2015).
Google Scholar 
Lamy, T. et al. Species insurance trumps spatial insurance in stabilizing biomass of a marine macroalgal metacommunity. Ecology 100, e02719 (2019).
Google Scholar 
Stier, A. C., Shelton, A. O., Samhouri, J. F., Feist, B. E. & Levin, P. S. Fishing, environment, and the erosion of a population portfolio. Ecosphere https://doi.org/10.1002/ecs2.3283 (2020).Article 

Google Scholar 
Burgess, S. C. et al. Beyond connectivity: How empirical methods can quantify population persistence to improve marine protected-area design. Ecol. Appl. 24, 257–270 (2014).
Google Scholar 
Saenz-Agudelo, P., Jones, G. P., Thorrold, S. R. & Planes, S. Connectivity dominates larval replenishment in a coastal reef fish metapopulation. Proc. R. Soc. B Biol. Sci. 278, 2954–2961 (2011).
Google Scholar 
Wood, S., Paris, C. B., Ridgwell, A. & Hendy, E. J. Modelling dispersal and connectivity of broadcast spawning corals at the global scale. Glob. Ecol. Biogeogr. 23, 1–11 (2014).
Google Scholar 
Loreau, M. et al. Biodiversity as insurance: From concept to measurement and application. Biol. Rev. https://doi.org/10.1111/brv.12756 (2021).Article 

Google Scholar 
Thibaut, L. M. & Connolly, S. R. Understanding diversity–stability relationships: Towards a unified model of portfolio effects. Ecol. Lett. 16, 140–150 (2013).
Google Scholar 
Wilcox, K. R. et al. Asynchrony among local communities stabilises ecosystem function of metacommunities. Ecol. Lett. 20, 1534–1545 (2017).
Google Scholar 
Loreau, M. & de Mazancourt, C. Species synchrony and its drivers: Neutral and nonneutral community dynamics in fluctuating environments. Am. Nat. 172, E48–E66 (2008).
Google Scholar 
Loreau, M. & Mazancourt, C. Biodiversity and ecosystem stability: A synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115 (2013).
Google Scholar 
Gross, K. et al. Species richness and the temporal stability of biomass production: A new analysis of recent biodiversity experiments. Am. Nat. 183, 1–12 (2014).
Google Scholar 
Sullaway, G. H., Shelton, A. O. & Samhouri, J. F. Synchrony erodes spatial portfolios of an anadromous fish and alters availability for resource users. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13575 (2021).Article 

Google Scholar 
Adjeroud, M., Augustin, D., Galzin, R. & Salvat, B. Natural disturbances and interannual variability of coral reef communities on the outer slope of Tiahura (Moorea, French Polynesia): 1991 to 1997. Mar. Ecol. Prog. Ser. 237, 121–131 (2002).ADS 

Google Scholar 
Adjeroud, M. et al. Recurrent disturbances, recovery trajectories, and resilience of coral assemblages on a South Central Pacific reef. Coral Reefs 28, 775–780 (2009).ADS 

Google Scholar 
Pratchett, M. S., Trapon, M., Berumen, M. L. & Chong-Seng, K. Recent Disturbances Augment Community Shifts in Coral Assemblages in Moorea, French Polynesia (SpringerLink, 2011). https://doi.org/10.1007/s00338-010-0678-2.Book 

Google Scholar 
Kayal, M. et al. Predator crown-of-thorns starfish (Acanthaster planci) outbreak, mass mortality of corals, and cascading effects on reef fish and benthic communities. PLoS ONE 7, e47363 (2012).ADS 
CAS 

Google Scholar 
McWilliam, M., Pratchett, M. S., Hoogenboom, M. O. & Hughes, T. P. Deficits in functional trait diversity following recovery on coral reefs. Proc. R. Soc. B 287, 20192628 (2020).
Google Scholar 
Hoegh-Guldberg, O. et al. Coral reefs under rapid climate change and ocean acidification. Science 318, 1737–1742 (2007).ADS 
CAS 

Google Scholar 
Penin, L., Adjeroud, M., Schrimm, M. & Lenihan, H. S. High spatial variability in coral bleaching around Moorea (French Polynesia): Patterns across locations and water depths. C. R. Biol. 330, 171–181 (2007).
Google Scholar 
Adam, T. C. et al. Herbivory, connectivity, and ecosystem resilience: Response of a coral reef to a large-scale perturbation. PLoS ONE 6, e23717 (2011).ADS 
CAS 

Google Scholar 
Edmunds, P. et al. Why more comparative approaches are required in time-series analyses of coral reef ecosystems. Mar. Ecol. Prog. Ser. 608, 297–306 (2019).ADS 

Google Scholar 
Pérez-Rosales, G. et al. Documenting decadal disturbance dynamics reveals archipelago-specific recovery and compositional change on Polynesian reefs. Mar. Pollut. Bull. 170, 112659 (2021).
Google Scholar 
Bruno, J. F. & Selig, E. R. Regional decline of coral cover in the Indo-Pacific: Timing, extent, and subregional comparisons. PLoS ONE 2, e711 (2007).ADS 

Google Scholar 
Jackson, J. B. C. et al. Status and trends of Caribbean coral reefs. Global Coral Reef Monitoring Network, IUCN, Gland, Switzerland (2014)Edmunds, P. J. Implications of high rates of sexual recruitment in driving rapid reef recovery in Mo’orea, French Polynesia. Sci. Rep. 8, 16615 (2018).ADS 

Google Scholar 
Burgess, S. C., Johnston, E. C., Wyatt, A. S. J., Leichter, J. J. & Edmunds, P. J. Response diversity in corals: Hidden differences in bleaching mortality among cryptic Pocillopora species. Ecology https://doi.org/10.1002/ecy.3324 (2021).Article 

Google Scholar 
Holbrook, S. J. et al. Recruitment drives spatial variation in recovery rates of resilient coral reefs. Sci. Rep. 8, 7338 (2018).ADS 

Google Scholar 
Guest, J. R. et al. A framework for identifying and characterising coral reef “oases” against a backdrop of degradation. J. Appl. Ecol. 55, 2865–2875 (2018).
Google Scholar 
Hench, J. L., Leichter, J. J. & Monismith, S. G. Episodic circulation and exchange in a wave-driven coral reef and lagoon system. Limnol. Oceanogr. 53, 2681–2694 (2008).ADS 

Google Scholar 
Barry, J. P. & Dayton, P. K. Ecological heterogeneity. Ecol. Stud. https://doi.org/10.1007/978-1-4612-3062-5_14 (1991).Article 

Google Scholar 
Edmunds, P. & Bruno, J. The importance of sampling scale in ecology: Kilometer-wide variation in coral reef communities. Mar. Ecol. Prog. Ser. 143, 165–171 (1996).ADS 

Google Scholar 
Lough, J. M., Anderson, K. D. & Hughes, T. P. Increasing thermal stress for tropical coral reefs: 1871–2017. Sci. Rep. 8, 6079 (2018).ADS 
CAS 

Google Scholar 
van Oppen, M. J. H. & Lough, J. M. Coral bleaching, patterns, processes, causes and consequences. Ecol. Stud. https://doi.org/10.1007/978-3-319-75393-5_14 (2018).Article 

Google Scholar 
Monismith, S. G. Hydrodynamics of coral reefs. Annu. Rev. Fluid Mech. 39, 37–55 (2007).ADS 
MATH 

Google Scholar 
Edmunds P. Of Moorea Coral Reef LTER. MCR LTER: Coral Reef: Long-term Population and Community Dynamics: Corals, ongoing since 2005. knb-lter-mcr.4.33 https://doi.org/10.6073/pasta/1f05f1f52a2759dc096da9c24e88b1e8 (2020).Cowles, J. et al. Resilience: insights from the U.S. Long-term ecological research network. Ecosphere 12, e03434 (2021).
Google Scholar 
Beijbom, O. et al. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PLoS ONE 10, e0130312 (2015).
Google Scholar 
Veron, J. E. N. Corals of the world, v. 1–3. Australian Institute of Marine Science (2000)Washburn, L of Moorea Coral Reef LTER. MCR LTER: Coral Reef: Ocean Currents and Biogeochemistry: salinity, temperature and current at CTD and ADCP mooring FOR01 from 2004 ongoing. knb-lter-mcr.30.36doi:10.6073/pasta/124d19950c5234bf1937661989dcced7 (2021).Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1671 (2018).ADS 

Google Scholar 
Dean, R. G. & Dalrymple, R. A. Water Wave Mechanics for Engineers and Scientists. Advanced Series on Ocean Engineering Vol. 2 (World Scientific, 1991).
Google Scholar 
Carroll, A., Harrison, P. & Adjeroud, M. Sexual reproduction of Acropora reef corals at Moorea, French Polynesia. Coral Reefs 25, 93–97 (2006).ADS 

Google Scholar 
Han, X., Adam, T. C., Schmitt, R. J., Brooks, A. J. & Holbrook, S. J. Response of herbivore functional groups to sequential perturbations in Moorea, French Polynesia. Coral Reefs 35, 999–1009 (2016).ADS 

Google Scholar 
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral Ecol. 18, 117–143 (1993).
Google Scholar 
Clarke, K. R., Somerfield, P. J. & Chapman, M. G. On resemblance measures for ecological studies, including taxonomic dissimilarities and a zero-adjusted Bray–Curtis coefficient for denuded assemblages. J. Exp. Mar. Biol. Ecol. 330, 55–80 (2006).
Google Scholar 
RStudio Team. RStudio: Integrated development for R. RStudio, PBC, Boston, MA URL http://www.rstudio.com/ (2021).Oksanen J. et al. vegan: Community ecology package. R package version 2.5–7. https://CRAN.R-project.org/package=vegan (2020).Wickham, et al. Welcome to the Tidyverse. J. Open Source Softw. 4(43), 1686. https://doi.org/10.21105/joss.01686 (2019).Article 
ADS 

Google Scholar 
Corlett, R. T. The Anthropocene concept in ecology and conservation. Trends Ecol. Evol. 30, 36–41 (2015).
Google Scholar 
Williams, G. J. et al. Coral reef ecology in the Anthropocene. Funct. Ecol. 33, 1014–1022 (2019).
Google Scholar 
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).ADS 
CAS 

Google Scholar 
Walther, G.-R. Community and ecosystem responses to recent climate change. Philos. Trans. R. Soc. B Biol. Sci. 365, 2019–2024 (2010).
Google Scholar 
Cinner, J. E. et al. Bright spots among the world’s coral reefs. Nature 535, 416–419 (2016).ADS 
CAS 

Google Scholar 
Grman, E., Lau, J. A., Schoolmaster, D. R. & Gross, K. L. Mechanisms contributing to stability in ecosystem function depend on the environmental context. Ecol. Lett. 13, 1400–1410 (2010).
Google Scholar 
Schindler, D. E. et al. Population diversity and the portfolio effect in an exploited species. Nature 465, 609–612 (2010).ADS 
CAS 

Google Scholar 
Doak, D. F. et al. The statistical inevitability of stability-diversity relationships in community ecology. Am. Nat. 151, 264–276 (1998).CAS 

Google Scholar 
Isbell, F. I., Polley, H. W. & Wilsey, B. J. Biodiversity, productivity and the temporal stability of productivity: Patterns and processes. Ecol. Lett. 12, 443–451 (2009).
Google Scholar 
Connell, J. H. Diversity in tropical rain forests and coral reefs author. Science 199, 1302–1310 (1978).ADS 
CAS 

Google Scholar 
Plaisance, L., Caley, M. J., Brainard, R. E. & Knowlton, N. The diversity of coral reefs: What are we missing?. PLoS ONE 6, e25026 (2011).ADS 
CAS 

Google Scholar 
Williams, G. J. et al. Biophysical drivers of coral trophic depth zonation. Mar. Biol. 165, 60 (2018).
Google Scholar 
Moritz, C. et al. Long-term monitoring of benthic communities reveals spatial determinants of disturbance and recovery dynamics on coral reefs. Mar. Ecol. Prog. Ser. 672, 141–152 (2021).ADS 

Google Scholar 
Dietzel, A. et al. The spatial footprint and patchiness of large scale disturbances on coral reefs. Global Change Biol. 27, 4825–4838 (2021).CAS 

Google Scholar 
Leichter, J. et al. Biological and physical interactions on a tropical island coral reef: Transport and retention processes on Moorea, French Polynesia. Oceanography 26, 52–63 (2011).
Google Scholar 
Porter, J. W. et al. Population trends among Jamaican reef corals. Nature 294, 249–250 (1981).ADS 

Google Scholar 
Graham, N. A. J., Jennings, S., MacNeil, M. A., Mouillot, D. & Wilson, S. K. Predicting climate-driven regime shifts versus rebound potential in coral reefs. Nature 518, 94–97 (2015).ADS 
CAS 

Google Scholar 
Whittaker, R. H. & Levin, S. A. The role of mosaic phenomena in natural communities. Theor. Popul. Biol. 12, 117–139 (1977).CAS 

Google Scholar 
Karlson, R. H. & Hurd, L. E. Disturbance, coral reef communities, and changing ecological paradigms. Coral Reefs 12, 117–125 (1993).ADS 

Google Scholar 
Stoddart, D. R. Effects of Hurricane Hattie on the British Honduras reefs and cays, October 30–31, 1961. Atoll Res. Bull. 95, 1–142 (1963).
Google Scholar 
Witman, J. D. Physical disturbance and community structure of exposed and protected reefs: A case study from St. John U.S. Virgin Islands. Integr. Comp. Biol. 32, 641–654 (1992).
Google Scholar 
Thorson, J. T., Scheuerell, M. D., Olden, J. D. & Schindler, D. E. Spatial heterogeneity contributes more to portfolio effects than species variability in bottom-associated marine fishes. Proc. R. Soc. B 285, 20180915 (2018).
Google Scholar 
Mellin, C., MacNeil, M. A., Cheal, A. J., Emslie, M. J. & Caley, M. J. Marine protected areas increase resilience among coral reef communities. Ecol. Lett. 19, 629–637 (2016).
Google Scholar 
Beyer, H. L. et al. Risk-sensitive planning for conserving coral reefs under rapid climate change. Conserv. Lett. 11, e12587 (2018).
Google Scholar 
Harrison, H. B., Bode, M., Williamson, D. H., Berumen, M. L. & Jones, G. P. A connectivity portfolio effect stabilizes marine reserve performance. Proc. Natl. Acad. Sci. 117, 25595–25600 (2020).ADS 
CAS 

Google Scholar 
Walter, J. A. et al. The spatial synchrony of species richness and its relationship to ecosystem stability. Ecology https://doi.org/10.1002/ecy.3486 (2021).Article 

Google Scholar 
Wang, S., Lamy, T., Hallett, L. M. & Loreau, M. Stability and synchrony across ecological hierarchies in heterogeneous metacommunities: Linking theory to data. Ecography 42, 1200–1211 (2019).
Google Scholar 
Catano, C. P., Fristoe, T. S., LaManna, J. A. & Myers, J. A. Local species diversity, β-diversity and climate influence the regional stability of bird biomass across North America. Proc. R. Soc. B 287, 20192520 (2020).
Google Scholar 
Roscher, C. et al. Identifying population- and community-level mechanisms of diversity–stability relationships in experimental grasslands. J. Ecol. 99, 1460–1469 (2011).
Google Scholar 
Downing, A. L., Brown, B. L. & Leibold, M. A. Multiple diversity–stability mechanisms enhance population and community stability in aquatic food webs. Ecology 95, 173–184 (2014).
Google Scholar 
Moran, P. The statistical analysis of the Canadian Lynx cycle. Aust. J. Zool. 1, 291–298 (1953).
Google Scholar 
Townsend, D. L. & Gouhier, T. C. Spatial and interspecific differences in recruitment decouple synchrony and stability in trophic metacommunities. Theor. Ecol. 12, 319–327 (2019).
Google Scholar 
Yeager, M. E., Gouhier, T. C. & Hughes, A. R. Predicting the stability of multitrophic communities in a variable world. Ecology 101, e02992 (2020).
Google Scholar 
Hughes, T. P. et al. Emergent properties in the responses of tropical corals to recurrent climate extremes. Curr. Biol. https://doi.org/10.1016/j.cub.2021.10.046 (2021).Article 

Google Scholar 
Jackson, J. B. C. Morphological strategies of sessile animals. In Biology and Systematics of Colonial Organisms (eds Larwood, G. & Rosen, B. R.) 499–555 (Academic, 1979).
Google Scholar 
Sammarco, P. W. & Andrews, J. C. Localized dispersal and recruitment in Great Barrier Reef Corals: The helix experiment. Science 239, 1422–1424 (1988).ADS 
CAS 

Google Scholar 
Edmunds, P. J. Unusually high coral recruitment during the 2016 El Niño in Mo’orea, French Polynesia. PLoS ONE 12, e0185167 (2017).
Google Scholar 
Bull, G. Distribution and abundance of coral plankton. Coral Reefs 4, 197–200 (1986).ADS 

Google Scholar 
Hodgson, G. Abundance and distribution of planktonic coral larvae in Kaneohe Bay, Oahu, Hawaii. Mar. Ecol. Prog. Ser. 26, 61–71 (1985).ADS 

Google Scholar 
Edmunds, P. J. Vital rates of small reef corals are associated with variation in climate. Limnol. Oceanogr. 66, 901–913 (2021).ADS 

Google Scholar  More

Bjorkman, A. D. et al. Plant functional trait change across a warming tundra biome. Nature 562, 57–62 (2018).ADS 
CAS 

Google Scholar 
Sabatini, F. M. et al. Global patterns of vascular plant alpha diversity. Nat. Commun. 13, 4683 (2022).ADS 
CAS 

Google Scholar 
Lavorel, S. & Garnier, E. Predicting changes in community composition and ecosystem functioning from plant traits: revisiting the Holy Grail. Funct. Ecol. 16, 545–556 (2002).
Google Scholar 
Chapin, F. S. III et al. Consequences of changing biodiversity. Nature 405, 234–242 (2000).CAS 

Google Scholar 
Garnier, E., Navas, M.-L. & Grigulis, K. Plant functional diversity. Organism traits, community structure, and ecosystem properties (Oxford University Press, Oxford, New York, NY, 2016).Funk, J. L. et al. Revisiting the Holy Grail: using plant functional traits to understand ecological processes. Biol. Rev. Camb. Philos. Soc. 92, 1156–1173 (2017).
Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).ADS 

Google Scholar 
Adler, P. B. et al. Functional traits explain variation in plant life history strategies. Proc. Natl. Acad. Sci. U.S.A. 111, 740–745 (2014).ADS 
CAS 

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

Google Scholar 
Salguero-Gómez, R. et al. Fast-slow continuum and reproductive strategies structure plant life-history variation worldwide. Proc. Natl. Acad. Sci. U. S. A. 113, 230–235 (2016).ADS 

Google Scholar 
Bergmann, J. et al. The fungal collaboration gradient dominates the root economics space in plants. Sci. Adv. 6, eaba3756 (2020).ADS 
CAS 

Google Scholar 
Shipley, B. et al. Reinforcing loose foundation stones in trait-based plant ecology. Oecologia 180, 923–931 (2016).ADS 

Google Scholar 
Bruelheide, H. et al. Global trait-environment relationships of plant communities. Nat. Ecol. Evol. 2, 1906–1917 (2018).
Google Scholar 
McGill, B. J., Enquist, B. J., Weiher, E. & Westoby, M. Rebuilding community ecology from functional traits. Trends Ecol. Evol. 21, 178–185 (2006).
Google Scholar 
Miller, J. E. D., Damschen, E. I. & Ives, A. R. Functional traits and community composition: A comparison among community‐weighted means, weighted correlations, and multilevel models. Methods Ecol. Evol. 10, 415–425 (2019).
Google Scholar 
Guerin, G. R. et al. Environmental associations of abundance-weighted functional traits in Australian plant communities. Basic Appl. Ecol. 58, 98–109 (2021).
Google Scholar 
Walter, H. Vegetation of the earth and ecological systems of the geo-biosphere (Springer-Verlag, Berlin, Germany, 1985).Ordoñez, J. C. et al. A global study of relationships between leaf traits, climate and soil measures of nutrient fertility. Glob. Ecol. Biogeogr. 18, 137–149 (2009).
Google Scholar 
Simpson, A. H., Richardson, S. J. & Laughlin, D. C. Soil-climate interactions explain variation in foliar, stem, root and reproductive traits across temperate forests. Glob. Ecol. Biogeogr. 25, 964–978 (2016).
Google Scholar 
Cubino, J. P. et al. The leaf economic and plant size spectra of European forest understory vegetation. Ecography 44, 1311–1324 (2021).
Google Scholar 
Garnier, E. et al. Assessing the effects of land-use change on plant traits, communities and ecosystem functioning in grasslands: a standardized methodology and lessons from an application to 11 European sites. Ann. Bot. 99, 967–985 (2007).
Google Scholar 
Herben, T., Klimešová, J. & Chytrý, M. Effects of disturbance frequency and severity on plant traits: An assessment across a temperate flora. Funct. Ecol. 32, 799–808 (2018).
Google Scholar 
Linder, H. P. et al. Biotic modifiers, environmental modulation and species distribution models. J. Biogeogr. 39, 2179–2190 (2012).
Google Scholar 
Gross, N. et al. Linking individual response to biotic interactions with community structure: a trait-based framework. Funct. Ecol. 23, 1167–1178 (2009).
Google Scholar 
Ordonez, A. & Svenning, J.-C. Consistent role of Quaternary climate change in shaping current plant functional diversity patterns across European plant orders. Sci. Rep. 7, 42988 (2017).ADS 
CAS 

Google Scholar 
Kemppinen, J. et al. Consistent trait–environment relationships within and across tundra plant communities. Nat. Ecol. Evol. 5, 458–467 (2021).
Google Scholar 
Chytrý, M. et al. European Vegetation Archive (EVA): an integrated database of European vegetation plots. Appl. Veg. Sci. 19, 173–180 (2016).
Google Scholar 
Karger, D. N. et al. Data from: Climatologies at high resolution for the earth’s land surface areas. EnviDat, https://doi.org/10.16904/envidat.228 (2018).Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).
Google Scholar 
Kattge, J. et al. TRY plant trait database – enhanced coverage and open access. Glob. Change. Biol. 26, 119–188 (2020).ADS 

Google Scholar 
Laughlin, D. C., Leppert, J. J., Moore, M. M. & Sieg, C. H. A multi-trait test of the leaf-height-seed plant strategy scheme with 133 species from a pine forest flora. Funct. Ecol. 24, 493–501 (2010).
Google Scholar 
Davies, C. E., Moss, D. & Hill, M. O. EUNIS Habitat Classification Revised 2004. Report to: European Environment Agency, European Topic Centre on Nature Protection and Biodiversity, 2004.Chytrý, M. et al. EUNIS Habitat Classification: Expert system, characteristic species combinations and distribution maps of European habitats. Appl. Veg. Sci. 23, 648–675 (2020).
Google Scholar 
Pausas, J. G. & Bond, W. J. Humboldt and the reinvention of nature. J. Ecol. 107, 1031–1037 (2019).
Google Scholar 
Meng, T.-T. et al. Responses of leaf traits to climatic gradients: adaptive variation versus compositional shifts. Biogeosciences 12, 5339–5352 (2015).ADS 

Google Scholar 
Fang, J. et al. Precipitation patterns alter growth of temperate vegetation. Geophys. Res. Lett. 32, 81 (2005).
Google Scholar 
Butler, E. E. et al. Mapping local and global variability in plant trait distributions. Proc. Natl. Acad. Sci. U.S.A. 114, E10937–E10946 (2017).ADS 
CAS 

Google Scholar 
Gong, H. & Gao, J. Soil and climatic drivers of plant SLA (specific leaf area). Glob. Ecol. Conserv. 20, e00696 (2019).
Google Scholar 
Laughlin, D. C. et al. Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs. Nat. Ecol. Evol. 5, 1–12 (2021).
Google Scholar 
Carmona, C. P. et al. Fine-root traits in the global spectrum of plant form and function. Nature 597, 683–687 (2021).ADS 
CAS 

Google Scholar 
Ding, J., Travers, S. K. & Eldridge, D. J. Occurrence of Australian woody species is driven by soil moisture and available phosphorus across a climatic gradient. J. Veg. Sci. 32, e13095 (2021).
Google Scholar 
Falster, D. S. & Westoby, M. Plant height and evolutionary games. Trends Ecol. Evol. 18, 337–343 (2003).
Google Scholar 
Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).ADS 
CAS 

Google Scholar 
McLachlan, A. & Brown, A. C. Coastal Dune Ecosystems and Dune/Beach Interactions. In The Ecology of Sandy Shores (Elsevier), 251–271 (2006).Cui, E., Weng, E., Yan, E. & Xia, J. Robust leaf trait relationships across species under global environmental changes. Nat. Commun. 11, 1–9 (2020).ADS 

Google Scholar 
Cain, S. A. Life-Forms and Phytoclimate. Bot. Rev. 16, 1–32 (1950).
Google Scholar 
Yu, S. et al. Shift of seed mass and fruit type spectra along longitudinal gradient: high water availability and growth allometry. Biogeosciences 18, 655–667 (2021).ADS 

Google Scholar 
Murray, B. R., Brown, A. H. D., Dickman, C. R. & Crowther, M. S. Geographical gradients in seed mass in relation to climate. J. Biogeogr. 31, 379–388 (2004).
Google Scholar 
Metz, J. et al. Plant survival in relation to seed size along environmental gradients: a long-term study from semi-arid and Mediterranean annual plant communities. J. Ecol. 98, 697–704 (2010).
Google Scholar 
Tao, S., Guo, Q., Li, C., Wang, Z. & Fang, J. Global patterns and determinants of forest canopy height. Ecology 97, 3265–3270 (2016).
Google Scholar 
Gonzalez, P., Neilson, R. P., Lenihan, J. M. & Drapek, R. J. Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Glob. Ecol. Biogeogr. 19, 755–768 (2010).
Google Scholar 
Feeley, K. J., Bravo-Avila, C., Fadrique, B., Perez, T. M. & Zuleta, D. Climate-driven changes in the composition of New World plant communities. Nat. Clim. Chang. 10, 965–970 (2020).ADS 
CAS 

Google Scholar 
Bruelheide, H. et al. sPlot—A new tool for global vegetation analyses. J. Veg. Sci. 30, 161–186 (2019).
Google Scholar 
Schrodt, F. et al. BHPMF—a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography. Glob. Ecol. Biogeogr. 24, 1510–1521 (2015).
Google Scholar 
Shan, H. et al. Gap filling in the plant kingdom—trait prediction using hierarchical probabilistic matrix factorization (Proceedings of the 29 th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012).Chytrý, M. et al. EUNIS-ESy, version 2021-06-01, https://doi.org/10.5281/zenodo.4812736 (2021).Wood, S. N., Pya, N. & Säfken, B. Smoothing Parameter and Model Selection for General Smooth Models. J. Am. Stat. Assoc. 111, 1548–1563 (2016).MathSciNet 
CAS 

Google Scholar 
Wood, S. N. Generalized Additive Models. An Introduction with R, Second Edition (CRC Press, Portland, Oregon, USA, 2017).Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).
Google Scholar 
Johnson, P. C. Extension of Nakagawa & Schielzeth’s R2GLMM to random slopes models. Methods Ecol. Evol. 5, 944–946 (2014).
Google Scholar 
R. Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2022).Lenth, R. V. et al. emmeans: estimated marginal means, aka least-squares means; R package version 1.6.2-1 (2021).Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772 (2018).ADS 

Google Scholar 
Hijmans, R.J., Phillips, S., Leathwick, J. & Elith, J. dismo: species distribution modelling; R package version 1.3-3 (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag New York, 2016).Kambach, S. Habitat-specificity of climate-trait relationships in plant communities across Europe. github.com/StephanKambach, version 1.0; https://doi.org/10.5281/zenodo.7404176 (2022).Moles, A. T. et al. Global patterns in plant height. J. Ecol. 97, 923–932 (2009).
Google Scholar 
Moles, A. T. et al. Global patterns in seed size. Glob. Ecol. Biogeogr. 16, 109–116 (2007).
Google Scholar 
Zheng, J., Guo, Z. & Wang, X. Seed mass of angiosperm woody plants better explained by life history traits than climate across China. Sci. Rep. 7, 2741 (2017).ADS 

Google Scholar 
Saatkamp, A. et al. A research agenda for seed-trait functional ecology. N. Phytol. 221, 1764–1775 (2019).
Google Scholar 
Freschet, G. T. et al. Climate, soil and plant functional types as drivers of global fine‐root trait variation. J. Ecol. 105, 1182–1196 (2017).
Google Scholar 
Weigelt, A. et al. An integrated framework of plant form and function: The belowground perspective. N. Phytol. 232, 42–59 (2021).
Google Scholar  More

WHO. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021; https://apps.who.int/iris/bitstream/handle/10665/341666/9789240027336-eng.pdfVan Boeckel TP, Glennon EE, Chen D, Gilbert M, Robinson TP, Grenfell BT, et al. Reducing antimicrobial use in food animals. Science. 2017;357:1350–2. https://doi.org/10.1126/science.aao1495Article 
CAS 

Google Scholar 
ONeill J. Antimicrobials in agriculture and the environment: reducing unnecessary use and waste. Rev Antimicrob Resistance; 2015:1–28.Van Boeckel TP, Brower C, Gilbert M, Grenfell BT, Levin SA, Robinson TP, et al. Global trends in antimicrobial use in food animals. Proc Natl Acad Sci USA. 2015;112:5649–54. https://doi.org/10.1073/pnas.1503141112Article 
CAS 

Google Scholar 
Managaki S, Murata A, Takada H, Tuyen BC, Chiem NH. Distribution of macrolides, sulfonamides, and trimethoprim in tropical waters: ubiquitous occurrence of veterinary antibiotics in the Mekong Delta. Environ Sci Technol. 2007;41:8004–10. https://doi.org/10.1021/es0709021Article 
CAS 

Google Scholar 
Woolhouse M, Ward M, van Bunnik B, Farrar J. Antimicrobial resistance in humans, livestock and the wider environment. Philos Trans R Soc Lond B Biol Sci. 2015;370:20140083 https://doi.org/10.1098/rstb.2014.0083Article 
CAS 

Google Scholar 
Noyes NR, Yang X, Linke LM, Magnuson RJ, Cook SR, Zaheer R, et al. Characterization of the resistome in manure, soil and wastewater from dairy and beef production systems. Sci Rep. 2016;6:24645. https://doi.org/10.1038/srep24645Article 
CAS 

Google Scholar 
Agga GE, Cook KL, Netthisinghe AMP, Gilfillen RA, Woosley PB, Sistani KR. Persistence of antibiotic resistance genes in beef cattle backgrounding environment over two years after cessation of operation. PLoS One. 2019;14:e0212510. https://doi.org/10.1371/journal.pone.0212510Article 
CAS 

Google Scholar 
Hudson JA, Frewer LJ, Jones G, Brereton PA, Whittingham MJ, Stewart G. The agri-food chain and antimicrobial resistance: a review. Trends Food Sci Technol. 2017;69:131–47. https://doi.org/10.1016/j.tifs.2017.09.007Article 
CAS 

Google Scholar 
Gillings MR. Lateral gene transfer, bacterial genome evolution, and the Anthropocene. Ann NY Acad Sci. 2017;1389:20–36. https://doi.org/10.1111/nyas.13213Article 

Google Scholar 
Rodríguez-Beltrán J, DelaFuente J, León-Sampedro R, MacLean RC, San Millán Á. Beyond horizontal gene transfer: the role of plasmids in bacterial evolution. Nat Rev Microbiol. 2021;6:347–59. https://doi.org/10.1038/s41579-020-00497-1Article 
CAS 

Google Scholar 
Zhang T, Zhang XX, Ye L. Plasmid metagenome reveals high levels of antibiotic resistance genes and mobile genetic elements in activated sludge. PLoS One. 2011;6:e26041. https://doi.org/10.1371/journal.pone.0026041Article 
CAS 

Google Scholar 
Li AD, Li LG, Zhang T. Exploring antibiotic resistance genes and metal resistance genes in plasmid metagenomes from wastewater treatment plants. Front Microbiol. 2015;6:1025. https://doi.org/10.3389/fmicb.2015.01025Article 

Google Scholar 
Bukowski M, Piwowarczyk R, Madry A, Zagorski-Przybylo R, Hydzik M, Wladyka B. Prevalence of antibiotic and heavy metal resistance determinants and virulence-related genetic elements in plasmids of Staphylococcus aureus. Front Microbiol. 2019;10:805. https://doi.org/10.3389/fmicb.2019.00805Article 

Google Scholar 
Ramírez-Díaz MI, Díaz-Magaña A, Meza-Carmen V, Johnstone L, Cervantes C, Rensing C. Nucleotide sequence of Pseudomonas aeruginosa conjugative plasmid pUM505 containing virulence and heavy-metal resistance genes. Plasmid. 2011;66:7–18. https://doi.org/10.1016/j.plasmid.2011.03.002Article 
CAS 

Google Scholar 
Haenni M, Poirel L, Kieffer N, Châtre P, Saras E, Métayer V, et al. Co-occurrence of extended spectrum β lactamase and MCR-1 encoding genes on plasmids. Lancet Infect Dis. 2016;16:281–2. https://doi.org/10.1016/S1473-3099(16)00007-4Article 
CAS 

Google Scholar 
Peter S, Bosio M, Gross C, Bezdan D, Gutierrez J, Oberhettinger P, et al. Tracking of antibiotic resistance transfer and rapid plasmid evolution in a hospital setting by nanopore sequencing. mSphere. 2020;5. https://doi.org/10.1128/mSphere.00525-20Halary S, Leigh JW, Cheaib B, Lopez P, Bapteste E. Network analyses structure genetic diversity in independent genetic worlds. Proc Natl Acad Sci USA. 2010;107:127–32. https://doi.org/10.1073/pnas.0908978107Article 

Google Scholar 
Bosi E, Fani R, Fondi M. The mosaicism of plasmids revealed by atypical genes detection and analysis. BMC Genom. 2011;12:403. https://doi.org/10.1186/1471-2164-12-403Article 
CAS 

Google Scholar 
Pesesky MW, Tilley R, Beck DAC. Mosaic plasmids are abundant and unevenly distributed across prokaryotic taxa. Plasmid. 2019;102:10–18. https://doi.org/10.1016/j.plasmid.2019.02.003Article 
CAS 

Google Scholar 
Casjens SR, Gilcrease EB, Vujadinovic M, Mongodin EF, Luft BJ, Schutzer SE, et al. Plasmid diversity and phylogenetic consistency in the Lyme disease agent Borrelia burgdorferi. BMC Genom. 2017;18:165. https://doi.org/10.1186/s12864-017-3553-5Article 
CAS 

Google Scholar 
Madec JY, Haenni M. Antimicrobial resistance plasmid reservoir in food and food-producing animals. Plasmid. 2018;99:72–81. https://doi.org/10.1016/j.plasmid.2018.09.001Article 
CAS 

Google Scholar 
Ceccarelli D, Kant A, van Essen-Zandbergen A, Dierikx C, Hordijk J, Wit B, et al. Diversity of plasmids and genes encoding resistance to extended spectrum cephalosporins in commensal escherichia coli from dutch livestock in 2007–2017. Front Microbiol. 2019;10. https://doi.org/10.3389/fmicb.2019.00076Auffret MD, Dewhurst RJ, Duthie CA, Rooke JA, John Wallace R, Freeman TC, et al. The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle. Microbiome. 2017;5:159. https://doi.org/10.1186/s40168-017-0378-zArticle 

Google Scholar 
Sabino YNV, Santana MF, Oyama LB, Santos FG, Moreira AJS, Huws SA, et al. Characterization of antibiotic resistance genes in the species of the rumen microbiota. Nat Commun. 2019;10:5252. https://doi.org/10.1038/s41467-019-13118-0Article 
CAS 

Google Scholar 
Brown Kav A, Benhar I, Mizrahi I. Rumen plasmids. In: Gophna U, editor. Lateral gene transfer in evolution. New York, NY: Springer New York; 2013. p. 105–20.Mizrahi I, Wallace RJ, Moraïs S. The rumen microbiome: balancing food security and environmental impacts. Nat Rev Microbiol. 2021;19:553–66. https://doi.org/10.1038/s41579-021-00543-6Article 
CAS 

Google Scholar 
Dionisio F, Zilhão R, Gama JA. Interactions between plasmids and other mobile genetic elements affect their transmission and persistence. Plasmid. 2019;102:29–36. https://doi.org/10.1016/j.plasmid.2019.01.003Article 
CAS 

Google Scholar 
Brown Kav A, Sasson G, Jami E, Doron-Faigenboim A, Benhar I, Mizrahi I. Insights into the bovine rumen plasmidome. Proc Natl Acad Sci USA. 2012;109:5452–7. https://doi.org/10.1073/pnas.1116410109Article 

Google Scholar 
Kav AB, Rozov R, Bogumil D, Sørensen SJ, Hansen LH, Benhar I, et al. Unravelling plasmidome distribution and interaction with its hosting microbiome. Environ Microbiol. 2020;22:32–44. https://doi.org/10.1111/1462-2920.14813Article 

Google Scholar 
Jørgensen TS, Xu Z, Hansen MA, Sørensen SJ, Hansen LH. Hundreds of circular novel plasmids and DNA elements identified in a rat cecum metamobilome. PLoS One. 2014;9:e87924. https://doi.org/10.1371/journal.pone.0087924Article 
CAS 

Google Scholar 
He Q, Pilosof S, Tiedje KE, Ruybal-Pesántez S, Artzy-Randrup Y, Baskerville EB, et al. Networks of genetic similarity reveal non-neutral processes shape strain structure in Plasmodium falciparum. Nat Commun. 2018;9:1817. https://doi.org/10.1038/s41467-018-04219-3Article 
CAS 

Google Scholar 
Acman M, van Dorp L, Santini JM, Balloux F. Large-scale network analysis captures biological features of bacterial plasmids. Nat Commun. 2020;11:2452. https://doi.org/10.1038/s41467-020-16282-wArticle 
CAS 

Google Scholar 
Redondo-Salvo S, Fernández-López R, Ruiz R, Vielva L, de Toro M, Rocha EPC, et al. Pathways for horizontal gene transfer in bacteria revealed by a global map of their plasmids. Nat Commun. 2020;11:3602. https://doi.org/10.1038/s41467-020-17278-2Article 
CAS 

Google Scholar 
Savary P, Foltête JC, Moal H, Vuidel G, Garnier S. Analysing landscape effects on dispersal networks and gene flow with genetic graphs. Mol Ecol Resour. 2021;21:1167–85. https://doi.org/10.1111/1755-0998.13333Article 

Google Scholar 
Pilosof S, He Q, Tiedje KE, Ruybal-Pesántez S, Day KP, Pascual M. Competition for hosts modulates vast antigenic diversity to generate persistent strain structure in Plasmodium falciparum. PLoS Biol. 2019;17:e3000336. https://doi.org/10.1371/journal.pbio.3000336Article 
CAS 

Google Scholar 
Brilli M, Mengoni A, Fondi M, Bazzicalupo M, Liò P, Fani R. Analysis of plasmid genes by phylogenetic profiling and visualization of homology relationships using Blast2Network. BMC Bioinform. 2008;9:551. https://doi.org/10.1186/1471-2105-9-551Article 
CAS 

Google Scholar 
Fondi M, Karkman A, Tamminen MV, Bosi E, Virta M, Fani R, et al. “Every gene is everywhere but the environment selects”: global geolocalization of gene sharing in environmental samples through network analysis. Genome Biol Evol. 2016;8:1388–1400. https://doi.org/10.1093/gbe/evw077Article 

Google Scholar 
Tamminen M, Virta M, Fani R, Fondi M. Large-scale analysis of plasmid relationships through gene-sharing networks. Mol Biol Evol. 2012;29:1225–40. https://doi.org/10.1093/molbev/msr292Article 
CAS 

Google Scholar 
Yamashita A, Sekizuka T, Kuroda M. Characterization of antimicrobial resistance dissemination across plasmid communities classified by network analysis. Pathogens. 2014;3:356–76. https://doi.org/10.3390/pathogens3020356Article 
CAS 

Google Scholar 
Pastor-Satorras R, Castellano C, Van Mieghem P, Vespignani A. Epidemic processes in complex networks. Rev Mod Phys. 2015;87:925–79. https://doi.org/10.1103/RevModPhys.87.925Article 

Google Scholar 
Pilosof S, Morand S, Krasnov BR, Nunn CL. Potential parasite transmission in multi-host networks based on parasite sharing. PLoS One. 2015;10:e0117909 https://doi.org/10.1371/journal.pone.0117909Article 
CAS 

Google Scholar 
VanderWaal KL, Atwill ER, Isbell LA, McCowan B.Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).J Anim Ecol.2014;83:406–14. https://doi.org/10.1111/1365-2656.12137Article 

Google Scholar 
Kauffman K, Werner CS, Titcomb G, Pender M, Rabezara JY, Herrera JP, et al. Comparing transmission potential networks based on social network surveys, close contacts and environmental overlap in rural Madagascar. J R Soc Interface. 2022;19:20210690. https://doi.org/10.1098/rsif.2021.0690Article 

Google Scholar 
Dallas TA, Han BA, Nunn CL, Park AW, Stephens PR, Drake JM. Host traits associated with species roles in parasite sharing networks. Oikos. 2019;128:23–32. https://doi.org/10.1111/oik.05602Article 

Google Scholar 
Matlock W, Chau KK, AbuOun M, Stubberfield E, Barker L, Kavanagh J, et al. Genomic network analysis of environmental and livestock F-type plasmid populations. ISME J. 2021;15:2322–35. https://doi.org/10.1038/s41396-021-00926-wArticle 
CAS 

Google Scholar 
Pilosof S, Porter MA, Pascual M, Kéfi S. The multilayer nature of ecological networks. Nat Ecol Evol. 2017;1:0101. https://doi.org/10.1038/s41559-017-0101Article 

Google Scholar 
Paull SH, Song S, McClure KM, Sackett LC, Kilpatrick AM, Johnson PTJ. From superspreaders to disease hotspots: linking transmission across hosts and space. Front Ecol Environ. 2012;10:75–82. https://doi.org/10.1890/110111Article 

Google Scholar 
Hutchinson MC, Bramon Mora B, Pilosof S, Barner AK, Kéfi S, Thébault E, et al. Seeing the forest for the trees: putting multilayer networks to work for community ecology. Funct Ecol. 2019;33:206–17. https://doi.org/10.1111/1365-2435.13237Article 

Google Scholar 
Kivelä M, Arenas A, Barthelemy M, Gleeson JP, Moreno Y, Porter MA. Multilayer networks. J Complex Netw. 2014;2:203–71. https://doi.org/10.1093/comnet/cnu016Article 

Google Scholar 
Lloyd-Smith JO, Schreiber SJ, Kopp PE, Getz WM. Superspreading and the effect of individual variation on disease emergence. Nature. 2005;438:355–9. https://doi.org/10.1038/nature04153Article 
CAS 

Google Scholar 
Fortuna MA, Popa-Lisseanu AG, Ibáñez C, Bascompte J. The roosting spatial network of a bird-predator bat. Ecology. 2009;90:934–44. https://doi.org/10.1890/08-0174.1Article 

Google Scholar 
Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys. 2004;69:026113. https://doi.org/10.1103/PhysRevE.69.026113Article 
CAS 

Google Scholar 
Rosvall M, Bergstrom CT. Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA. 2008;105:1118–23. https://doi.org/10.1073/pnas.0706851105Article 

Google Scholar 
De Domenico M, Lancichinetti A, Arenas A, Rosvall M. Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Phys Rev X. 2015;5:011027. https://doi.org/10.1103/PhysRevX.5.011027Article 
CAS 

Google Scholar 
Farage C, Edler D, Eklöf A, Rosvall M, Pilosof S. Identifying flow modules in ecological networks using Infomap. Methods Ecol Evol. 2021;12:778–86. https://doi.org/10.1111/2041-210x.13569Article 

Google Scholar 
Popa O, Hazkani-Covo E, Landan G, Martin W, Dagan T. Directed networks reveal genomic barriers and DNA repair bypasses to lateral gene transfer among prokaryotes. Genome Res. 2011;21:599–609. https://doi.org/10.1101/gr.115592.110Article 
CAS 

Google Scholar 
Smillie C, Garcillán-Barcia MP, Francia MV, Rocha EPC, de la Cruz F. Mobility of plasmids. Microbiol Mol Biol Rev. 2010;74:434–52. https://doi.org/10.1128/MMBR.00020-10Article 
CAS 

Google Scholar 
Garcillán-Barcia MP, Francia MV, de la Cruz F. The diversity of conjugative relaxases and its application in plasmid classification. FEMS Microbiol Rev. 2009;33:657–87. https://doi.org/10.1111/j.1574-6976.2009.00168.xArticle 
CAS 

Google Scholar 
Coluzzi C, Guédon G, Devignes MD, Ambroset C, Loux V, Lacroix T, et al. A glimpse into the world of integrative and mobilizable elements in streptococci reveals an unexpected diversity and novel families of mobilization proteins. Front Microbiol. 2017;8:443. https://doi.org/10.3389/fmicb.2017.00443Article 

Google Scholar 
Moraïs S, Mizrahi I. Islands in the stream: from individual to communal fiber degradation in the rumen ecosystem. FEMS Microbiol Rev. 2019;43:362–79. https://doi.org/10.1093/femsre/fuz007Article 
CAS 

Google Scholar 
León-Sampedro R, DelaFuente J, Díaz-Agero C, Crellen T, Musicha P, Rodríguez-Beltrán J, et al. Pervasive transmission of a carbapenem resistance plasmid in the gut microbiota of hospitalized patients. Nat Microbiol. 2021;6:606–16. https://doi.org/10.1038/s41564-021-00879-yArticle 
CAS 

Google Scholar 
Rocha LEC, Singh V, Esch M, Lenaerts T, Liljeros F, Thorson A. Dynamic contact networks of patients and MRSA spread in hospitals. Sci Rep. 2020;10:9336. https://doi.org/10.1038/s41598-020-66270-9Article 
CAS 

Google Scholar 
Lerner A, Adler A, Abu-Hanna J, Cohen Percia S, Kazma Matalon M, Carmeli Y. Spread of KPC-producing carbapenem-resistant Enterobacteriaceae: the importance of super-spreaders and rectal KPC concentration. Clin Microbiol Infect. 2015;21:470.e1–7. https://doi.org/10.1016/j.cmi.2014.12.015Article 
CAS 

Google Scholar 
Stein RA, Katz DE. Escherichia coli, cattle and the propagation of disease. FEMS Microbiol Lett. 2017;364. https://doi.org/10.1093/femsle/fnx050.de Freslon I, Martínez-López B, Belkhiria J, Strappini A, Monti G. Use of social network analysis to improve the understanding of social behaviour in dairy cattle and its impact on disease transmission. Appl Anim Behav Sci. 2019;213:47–54. https://doi.org/10.1016/j.applanim.2019.01.006Article 

Google Scholar 
Rushmore J, Caillaud D, Hall RJ, Stumpf RM, Meyers LA, Altizer S. Network-based vaccination improves prospects for disease control in wild chimpanzees. J R Soc Interface. 2014;11:20140349. https://doi.org/10.1098/rsif.2014.0349Article 

Google Scholar 
Xue H, Cordero OX, Camas FM, Trimble W, Meyer F, Guglielmini J, et al. Eco-evolutionary dynamics of episomes among ecologically cohesive bacterial populations. MBio. 2015;6:e00552–15. https://doi.org/10.1128/mBio.00552-15Article 
CAS 

Google Scholar 
Evans DR, Griffith MP, Sundermann AJ, Shutt KA, Saul MI, Mustapha MM, et al. Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital. Elife. 2020;9. https://doi.org/10.7554/eLife.53886Abe R, Oyama F, Akeda Y, Nozaki M, Hatachi T, Okamoto Y, et al. Hospital-wide outbreaks of carbapenem-resistant Enterobacteriaceae horizontally spread through a clonal plasmid harbouring bla IMP-1 in children’s hospitals in Japan. J Antimicrob Chemother. 2021;76:3314–7.Article 
CAS 

Google Scholar 
Bingen EH, Desjardins P, Arlet G, Bourgeois F, Mariani-Kurkdjian P, Lambert-Zechovsky NY, et al. Molecular epidemiology of plasmid spread among extended broad-spectrum beta-lactamase-producing Klebsiella pneumoniae isolates in a pediatric hospital. J Clin Microbiol. 1993;31:179–84. https://doi.org/10.1128/jcm.31.2.179-184.1993.Bai H, He LY, Wu DL, Gao FZ, Zhang M, Zou HY, et al. Spread of airborne antibiotic resistance from animal farms to the environment: dispersal pattern and exposure risk. Environ Int. 2022;158:106927 https://doi.org/10.1016/j.envint.2021.106927Article 
CAS 

Google Scholar 
Boyland NK, Mlynski DT, James R, Brent LJN, Croft DP. The social network structure of a dynamic group of dairy cows: from individual to group level patterns. Appl Anim Behav Sci. 2016;174:1–10. https://doi.org/10.1016/j.applanim.2015.11.016Article 

Google Scholar 
Björk JR, Dasari M, Grieneisen L, Archie EA. Primate microbiomes over time: longitudinal answers to standing questions in microbiome research. Am J Primatol. 2019;81:e22970. https://doi.org/10.1002/ajp.22970Article 

Google Scholar 
Dib JR, Wagenknecht M, Farías ME, Meinhardt F. Strategies and approaches in plasmidome studies—uncovering plasmid diversity disregarding of linear elements? Front Microbiol. 2015;6. https://doi.org/10.3389/fmicb.2015.00463Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19:455–77. https://doi.org/10.1089/cmb.2012.0021Article 
CAS 

Google Scholar 
Rozov R, Brown Kav A, Bogumil D, Shterzer N, Halperin E, Mizrahi I, et al. Recycler: an algorithm for detecting plasmids from de novo assembly graphs. Bioinformatics. 2017;33:475–82. https://doi.org/10.1093/bioinformatics/btw651Article 
CAS 

Google Scholar 
Orlek A, Stoesser N, Anjum MF, Doumith M, Ellington MJ, Peto T, et al. Plasmid classification in an era of whole-genome sequencing: application in studies of antibiotic resistance epidemiology. Front Microbiol. 2017;8:182. https://doi.org/10.3389/fmicb.2017.00182Article 

Google Scholar 
Komsta L, Novomestky F. Moments, cumulants, skewness, kurtosis and related tests. R package version. 2015;14.Rosvall M, Axelsson D, Bergstrom CT. The map equation. Eur Phys J Spec Top. 2010;178:13–23. https://doi.org/10.1140/epjst/e2010-01179-1Article 

Google Scholar 
Bascompte J, Jordano P, Melián CJ, Olesen JM. The nested assembly of plant–animal mutualistic networks. Proc Natl Acad Sci USA. 2003;100:9383–7. https://doi.org/10.1073/pnas.1633576100Article 
CAS 

Google Scholar 
Vázquez DP, Poulin R, Krasnov BR, Shenbrot GI. Species abundance and the distribution of specialization in host–parasite interaction networks. J Anim Ecol. 2005;74:946–55.Article 

Google Scholar 
Fortuna MA, Stouffer DB, Olesen JM, Jordano P, Mouillot D, Krasnov BR, et al. Nestedness versus modularity in ecological networks: two sides of the same coin? J Anim Ecol. 2010;79:811–7. https://doi.org/10.1111/j.1365-2656.2010.01688.xArticle 

Google Scholar 
Gillespie DT. Exact stochastic simulation of coupled chemical reactions. J Phys Chem. 1977;81:2340–61. https://doi.org/10.1021/j100540a008Article 
CAS 

Google Scholar 
R Core Team. R: a language and environment for statistical computing; 2021. More

Rufener, C. et al. Keel bone fractures are associated with individual mobility of laying hens in an aviary system. Appl. Anim. Behav. Sci. 217, 48–56 (2019).
Google Scholar 
Rentsch, A. K., Rufener, C. B., Spadavecchia, C., Stratmann, A. & Toscano, M. J. Laying hen’s mobility is impaired by keel bone fractures and does not improve with paracetamol treatment. Appl. Anim. Behav. Sci. 216, 19–25 (2019).
Google Scholar 
Rodriguez-Aurrekoetxea, A. & Estevez, I. Use of space and its impact on the welfare of laying hens in a commercial free-range system. Poult. Sci. 95, 2503–2513 (2016).CAS 

Google Scholar 
Fagan, W. F. et al. Spatial memory and animal movement. Ecol. Lett. 16, 1316–1329 (2013).
Google Scholar 
Campbell, D. L. M., Talk, A. C., Loh, Z. A., Dyall, T. R. & Lee, C. Spatial cognition and range use in free-range laying hens. Animals 8, 26 (2018).
Google Scholar 
de Jager, M., Weissing, F. J., Herman, P. M. J., Nolet, B. A. & van de Koppel, J. Lévy walks evolve through interaction between movement and environmental complexity. Science 1979(332), 1551–1553 (2011).
Google Scholar 
Krause, J., James, R. & Croft, D. P. Personality in the context of social networks. Philos. Trans. R. Soc. B Biol. Sci. 365, 4099–4106 (2010).CAS 

Google Scholar 
Ihwagi, F. W. et al. Poaching lowers elephant path tortuosity: Implications for conservation. J. Wildl. Manag. 83, 1022–1031 (2019).
Google Scholar 
Shaw, A. K. Causes and consequences of individual variation in animal movement. Mov. Ecol. 8, 1–12 (2020).
Google Scholar 
Matthews, S. G., Miller, A. L., Plötz, T. & Kyriazakis, I. Automated tracking to measure behavioural changes in pigs for health and welfare monitoring. Sci. Rep. 7, 1–12 (2017).CAS 

Google Scholar 
Berger-Tal, O. & Saltz, D. Using the movement patterns of reintroduced animals to improve reintroduction success. Curr. Zool. 60, 515–526 (2014).
Google Scholar 
Stuber, E. F., Carlson, B. S. & Jesmer, B. R. Spatial personalities: A meta-analysis of consistent individual differences in spatial behavior. Behav. Ecol. https://doi.org/10.1093/BEHECO/ARAB147 (2022).Article 

Google Scholar 
Sirovnik, J., Würbel, H. & Toscano, M. J. Feeder space affects access to the feeder, aggression, and feed conversion in laying hens in an aviary system. Appl. Anim. Behav. Sci. 198, 75–82 (2018).
Google Scholar 
Sirovnik, J., Voelkl, B., Keeling, L. J., Würbel, H. & Toscano, M. J. Breakdown of the ideal free distribution under conditions of severe and low competition. Behav. Ecol. Sociobiol. 75, 1–11 (2021).
Google Scholar 
Becot, L., Bedere, N., Burlot, T., Coton, J. & le Roy, P. Nest acceptance, clutch, and oviposition traits are promising selection criteria to improve egg production in cage-free system. PLoS ONE 16, e0251037 (2021).CAS 

Google Scholar 
Thompson, M. J., Evans, J. C., Parsons, S. & Morand-Ferron, J. Urbanization and individual differences in exploration and plasticity. Behav. Ecol. 29, 1415–1425 (2018).
Google Scholar 
Stamps, J. & Groothuis, T. G. G. The development of animal personality: Relevance, concepts and perspectives. Biol. Rev. 85, 301–325 (2010).
Google Scholar 
Salinas-Melgoza, A., Salinas-Melgoza, V. & Wright, T. F. Behavioral plasticity of a threatened parrot in human-modified landscapes. Biol. Conserv. 159, 303–312 (2013).
Google Scholar 
Stamps, J. A., Briffa, M. & Biro, P. A. Unpredictable animals: Individual differences in intraindividual variability (IIV). Anim. Behav. 83, 1325–1334 (2012).
Google Scholar 
Hertel, A. G., Royauté, R., Zedrosser, A. & Mueller, T. Biologging reveals individual variation in behavioural predictability in the wild. J. Anim. Ecol. 90, 723–737 (2021).
Google Scholar 
Biro, P. A. & Adriaenssens, B. Predictability as a personality trait: Consistent differences in intraindividual behavioral variation. Am. Nat. 182, 621–629 (2013).
Google Scholar 
Henriksen, R. et al. Intra-individual behavioural variability: A trait under genetic control. Int. J. Mol. Sci. 21, 8069 (2020).CAS 

Google Scholar 
Rufener, C. et al. Finding hens in a haystack: Consistency of movement patterns within and across individual laying hens maintained in large groups. Sci. Rep. 8, (2018).Campbell, D. L. M., Karcher, D. M. & Siegford, J. M. Location tracking of individual laying hens housed in aviaries with different litter substrates. Appl. Anim. Behav. 184, 74–79 (2016).
Google Scholar 
Weeks, C. A. & Nicol, C. J. Behavioural needs, priorities and preferences of laying hens. Worlds Poult. Sci. J. 62, 296–307 (2006).
Google Scholar 
Hartcher, K. M. & Jones, B. The welfare of layer hens in cage and cage-free housing systems. Worlds Poult. Sci. J. 73, 767–782 (2017).
Google Scholar 
Zeltner, E. & Hirt, H. Effect of artificial structuring on the use of laying hen runs in a free-range system. Br. Poult. Sci. 44, 533–537 (2010).
Google Scholar 
Stratmann, A. et al. Modification of aviary design reduces incidence of falls, collisions and keel bone damage in laying hens. Appl. Anim. Behav. Sci. 165, 112–123 (2015).
Google Scholar 
Vandekerchove, D., Herdt, P., Laevens, H. & Pasmans, F. Colibacillosis in caged layer hens: Characteristics of the disease and the aetiological agent. Avian Pathol. 33, 117–125 (2004).CAS 

Google Scholar 
Montalcini, C. M., Voelkl, B., Gómez, Y., Gantner, M. & Toscano, M. J. Evaluation of an active LF tracking system and data processing methods for livestock precision farming in the poultry sector. Sensors 22, 659 (2022).ADS 

Google Scholar 
Revelle, W. Procedures for psychological, psychometric, and personality research. (2021).Kaiser, H. F. The application of electronic computers to factor analysis. Educ. Psychol. Meas. 20, 141–151 (1960).
Google Scholar 
Rufener, C., Baur, S., Stratmann, A. & Toscano, M. J. A reliable method to assess keel bone fractures in laying hens from radiographs using a tagged visual analogue scale. Front. Vet. Sci. 5, 124 (2018).
Google Scholar 
Tauson, R., Kjaer, J., Maria, G. A., Cepero, R. & Holm, K.-E. The creation of a common scoring system for the integument and health of laying hens: Applied scoring of integument and health in laying hens. Final report Health from the Laywell project. https://www.laywel.eu/web/pdf/deliverables%2031-33%20health.pdf (2005).Hertel, A. G. et al. A guide for studying among-individual behavioral variation from movement data in the wild. Mov. Ecol. 8, (2020).Nakagawa, S. & Schielzeth, H. Repeatability for Gaussian and non-Gaussian data: A practical guide for biologists. Biol. Rev. 85, 935–956 (2010).
Google Scholar 
Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).
Google Scholar 
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J Stat Softw 67, (2015).Cleasby, I. R., Nakagawa, S. & Schielzeth, H. Quantifying the predictability of behaviour: Statistical approaches for the study of between-individual variation in the within-individual variance. Methods Ecol. Evol. 6, 27–37 (2015).
Google Scholar 
Bürkner, P.-C. brms: An R package for bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).
Google Scholar 
Vehtari, A., Gelman, A. & Gabry, J. Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. Stat. Comput. 27, 1413–1432 (2017).MathSciNet 
MATH 

Google Scholar 
Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).MATH 

Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: The MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar 
Houslay, T. M. & Wilson, A. J. Avoiding the misuse of BLUP in behavioural ecology. Behav. Ecol. 28, 948–952 (2017).
Google Scholar 
Hertel, A. G., Niemelä, P. T., Dingemanse, N. J. & Mueller, T. Don’t poke the bear: Using tracking data to quantify behavioural syndromes in elusive wildlife. Anim. Behav. 147, 91–104 (2019).
Google Scholar 
Spiegel, O., Leu, S. T., Bull, C. M. & Sih, A. What’s your move? Movement as a link between personality and spatial dynamics in animal populations. Ecol. Lett. 20, 3–18 (2017).ADS 

Google Scholar 
Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).
Google Scholar 
Occhiuto, F., Vázquez-Diosdado, J. A., Carslake, C. & Kaler, J. Personality and predictability in farmed calves using movement and space-use behaviours quantified by ultra-wideband sensors. R. Soc. Open Sci. 9, (2022).Moinard, C. et al. Accuracy of laying hens in jumping upwards and downwards between perches in different light environments. Appl. Anim. Behav. Sci. 85, 77–92 (2004).
Google Scholar 
Baur, S., Rufener, C., Toscano, M. J. & Geissbühler, U. Radiographic evaluation of keel bone damage in laying hens—Morphologic and temporal observations in a longitudinal study. Front. Vet. Sci. 1, 129 (2020).
Google Scholar 
Cordiner, L. S. & Savory, C. J. Use of perches and nestboxes by laying hens in relation to social status, based on examination of consistency of ranking orders and frequency of interaction. Appl. Anim. Behav. Sci. 71, 305–317 (2001).
Google Scholar 
Rufener, C. & Makagon, M. M. Keel bone fractures in laying hens: A systematic review of prevalence across age, housing systems, and strains. J. Anim. Sci. 98, S36–S51 (2020).
Google Scholar 
Nasr, M. A. F., Nicol, C. J., Wilkins, L. & Murrell, J. C. The effects of two non-steroidal anti-inflammatory drugs on the mobility of laying hens with keel bone fractures. Vet. Anaesth. Analg. 42, 197–204 (2015).CAS 

Google Scholar 
Nasr, M., Murrell, J., Wilkins, L. J. & Nicol, C. J. The effect of keel fractures on egg-production parameters, mobility and behaviour in individual laying hens. Anim. Welf. 21, 127–135 (2012).CAS 

Google Scholar 
Koolhaas, J. M. & van Reenen, C. G. Animal behavior and well-being symposium: Interaction between coping style/personality, stress, and welfare: Relevance for domestic farm animals. J. Anim. Sci. 94, 2284–2296 (2016).CAS 

Google Scholar 
Coppens, C. M., de Boer, S. F. & Koolhaas, J. M. Coping styles and behavioural flexibility: Towards underlying mechanisms. Philos. Trans. R. Soc. B Biol. Sci. 365, 4021 (2010).
Google Scholar 
Koolhaas, J. M., de Boer, S. F., Coppens, C. M. & Buwalda, B. Neuroendocrinology of coping styles: Towards understanding the biology of individual variation. Front. Neuroendocrinol. 31, 307–321 (2010).CAS 

Google Scholar 
Finkemeier, M.-A., Langbein, J. & Puppe, B. Personality research in mammalian farm animals: Concepts, measures, and relationship to welfare. Front. Vet. Sci. 5, 131 (2018).
Google Scholar 
Martin, J. G. A., Pirotta, E., Petelle, M. B. & Blumstein, D. T. Genetic basis of between-individual and within-individual variance of docility. J. Evol. Biol. 30, 796–805 (2017).CAS 

Google Scholar 
Prentice, P. M., Houslay, T. M., Martin, J. G. A. & Wilson, A. J. Genetic variance for behavioural ‘predictability’ of stress response. J. Evol. Biol. 33, 642–652 (2020).
Google Scholar  More