Philippa Kaur

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

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

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

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

This paper describes the complexity of gravitational fractals in terms of global and local dimensions. They are presented in Table 1.Table 1 Global and local dimensions of gravitational fractals and attraction basins.Full size tableThe fractal in hexagonal CPT space, shown in Fig. 1, has a very rich structure, and therefore its characterization by means of fractal dimensions requires two approaches: (1) a global approach treating the fractal as a complex whole and (2) a local approach which allows us to determine the dimension of its fragments which are particularly interesting from a research perspective (see also Table 1). In the subsequent part of the paper, the results obtained are presented and interpreted according to the division in the table.Global dimension of boundaries of gravity attraction basinsTwo types of fractal dimensions have been thus far used in this analysis, i.e., the box and ruler dimensions. Figure 3 shows the distribution of the values of these dimensions determined for the boundaries of attraction as a function of space friction μ.Figure 3Comparison of the variability of the global ruler and box dimensions. Legend: The edge of all attraction basins is a function of the μ coefficient; 1–edges of all basins, 2–entire basins.Full size imageFigure 3 empirically confirms a fact known from chaos theory that whenever a fractal represents full chaos, the ruler dimension may be greater than 2 (Peitgen et al.33, 192–209), whereas the box dimension never exceeds this extreme value. Clearly, for a certain value of μ (in this case μ = 0.19), the numerical values of both types of dimensions are identical.In the bottom part of Fig. 3, line 1 illustrates the variability of the shapes of the attraction basins of individual cities depending on the value of μ, i.e., space resistance. The initially extremely complex shapes of the boundaries are smoothed to take the form of straight lines in the case of a large value of μ (μ = 0.52).In turn, line 2 illustrates not only the boundaries of the attraction basins, but also their internal structure. Clearly, the initially chaotic impacts of individual cities on the agent (μ = 0.005) are gradually smoothed out, so that in the final stage of the process they fully stabilize. This means that each city has a geometrically identical basin of attraction. Hence, if the agent is in the attraction basin of city 1 (purple color), it will always be attracted only by that city. This rule also applies to the other cities. It is obvious that the random process occurring at μ = 0.09 is then replaced by a strictly deterministic one. When chaos becomes complete order (Banaszak et al.15, the numerical values of both types of dimensions appear to stabilize at the level of 1.Global dimension of the boundary of each separate attraction basinFigure 1 also shows the geometric image of the attraction basins of individual cities. They were almost identical, and therefore also the fractal dimensions of the boundaries of these basins must match. The validity of this proposition is confirmed by Fig. 4. Six lines representing the distribution of the fractal dimension of the boundaries of the six basins coincide with almost full accuracy. Further analysis of Fig. 4 allows us to infer the conclusion that there is almost total chaos at the value db = 1.9021 (μ = 0.005). On the other hand, as space resistance increases to the value of μ = 0.22, there is a rapid decrease in the value of the fractal dimension of the boundary of each basin to the level of 1.2628; when μ = 0.34, then db = 1.2382. In that case, the value of the fractal dimension stabilizes, and at μ = 0.46, db = 1.2444 and finally for μ = 0.52, db reaches the value of 1.0412. The icons presented in Fig. 4 in lines 1 and 2 have slightly different structures than the icons in Fig. 3, due to different values of μ in certain cases.Figure 4The box dimension of the edges of the attraction basins depending on the μ coefficient (separately for each attractor). Legend: 1–boundaries of single attraction basins, 2–entire basins.Full size imageThe global dimension of the attraction basin of each city as an irregular geometric figureThe full symmetry of the basins of attraction of individual cities can be disturbed by the shape of the geometric figure on which the deterministic fractal is modeled. Such a situation occurs in the present case. Due to the fact that the fractal in Fig. 1 is formed on the surface of a square, the final basins of attraction of cities 1, 3, 4 and 6 are obviously larger than those of cities 2 and 5. Of course, these differences do not occur when considering the surface inside the hexagon.In Fig. 5, the line marked in black color represents the average value of the fractal dimension of the basins of attraction of individual cities, the value of which is (overline{{d }_{b}}=1.77). It can be seen that at very high values of the fractal dimension in the range (1.750, 1.775), there are db oscillations around this line. This is precisely the effect of modeling the fractal on the surface of the square, rather than the properties of this fractal. Therefore, (overline{{d }_{b}}=1.77) should be regarded as the global dimension of the basin of attraction (of each city) treated as an irregular figure.Figure 5Box dimension of the attraction basins as a geometric irregular figure in the gravitational fractal. Legend: 1-basins of the first city, 2-basins of the second city, 7-basins of all cities.Full size imageLocal dimensions of the boundary of the selected characteristic fragmentsFigure 6 presents fractal dimensions, with the Box and Ruler as functions of μ, and the boundaries of the attraction basins of individual cities occurring in all fragments A, …, E.Figure 6Distribution of the values of fractal dimensions of the boundaries of the attraction basins identified in selected fragments of a fractal; Legend: (A, D)-fragments marked in Fig. 1.Full size imageIt is evident that the structures of Fig. 6 (Box and Ruler) are almost identical. This means that, as has been stated earlier, when describing complex fractal objects, it does not really matter which type of dimension is used.Of interest here is the variability of the structure of both figures along with the increase in the value of the parameter μ. Fragments A, …, E (see Fig. 1) are characterized by high complexity, i.e. the intertwining attraction basins of the individual attractors (cities). This observation is confirmed by the numerical results of both fractal dimensions whose values are in the range (1.68–1.82). To illustrate the spatial complexity of these fragments, and thus their dimensions, by way of example, two fractal fragments are considered below: fragments A and D (see also Fig. 7).Figure 7Box dimension of the edge of each gravitation basin in A and D. Legend: The icons show the variability of the fragments A and D due to the share of the attraction basins of individual cities (3, 4 and 6).Full size imageFigure 6 offers important conclusions concerning the organization of social and economic life in the geographical area surrounding individual cities (attractors).

1.

Out of all the separated fragments, only in fragment A do we find the attraction basins of all the cities intertwined across the entire range of variation μ, i.e. (0.00–0.48). Hence, the graph of fractal dimension (db) (blue line) as a function of μ is continuous, and when the resistance of space is the greatest (μ = 0.48), the fractal dimension d = 1.00. This means that chaos has given way to total order, and fragment A has been symmetrically divided between cities 1 and 6. Hence, there are two colors left, namely red and purple.

2.

A similar situation occurs in the case of fragment D (yellow line), where the attraction basins of individual cities intertwine continuously within the range: 0.00 ≤ μ ≤ 0.46. Beyond the value of 0.46, the entire fragment D is filled with purple: the closest city 1 dominates it.

The research conducted here also confirms the conclusions presented in previous works by Banaszak et al.15,16 concerning the transformation of chaos into spatial order, which means the stabilization of permanent dominance, usually of one attractor (city). Thus, with regard to fragments A and D, in fragment A there is a constant dominance (in half of the area) of cities 1 and 6, from the limit value of μ = 0.24 onward. In the case of fragment D, beginning with the value of μ = 0.36, only city 1 dominates (purple). That is, in the final phase of establishing the order in spatial interactions in the arrangement of areas A and D, the role of the dominant attractor (city) is played by city 1 (purple).Due to the symmetry of Fig. 1, similar effects can be observed in other parts of this fractal, located symmetrically in relation to A, …, E (see Supplementary Material).Figures 1 and 6 confirm the findings, known in the theory of city development, that urban (and other) centers rise in the hierarchy (or their rank decreases), depending on the external and internal factors conditioning their development. In the model used in this study, the parameter μ represents external factors (space resistance). If μ values are low, all cities are attractive from the point of view of spatial interactions and create their own but symmetrical basins of attraction. When the resistance of space increases, one city becomes the dominant center, and its basin of attraction is a uniform compact isotropic surface.However, this is not a simple mechanism, since, as has been demonstrated by simulation experiments described in this paper, within a certain range of μ values, another city (attractor) may dominate the others during chaotic interactions. The dynamic history of urban development confirms this observation, for example, in relation to historical capitals of some countries that have lost their functions as administrative capitals.Local dimension of the boundary of each attraction basin in a selected fragment of a fractalFragments A, …, E (Fig. 1 and the Supplementary Material) consist of mutually intertwined basins of attraction (six cities) whose boundaries with complicated courses have a fractal dimension, e.g. a box dimension.Figure 7(fragment A) shows the distribution of db as a function of μ in this fragment. In the case of total internal chaos, the fractal dimension of the boundaries of the attraction basins of all cities is identical and amounts to 1.9152. A clear differentiation of db is noticeable from μ = 0.1 onward. It should also be noted that orange and blue, red and purple, yellow and green lines mutually coincide. The red–purple line tend towards db = 1 as μ increases. However, orange, blue, yellow and green lines reach a value of db = 0.The fractal dimension db = 1.0 is most closely represented by the blue line (city 2), then the red line (city 6) and the purple line (city 1). Since these lines almost coincide, and the red and purple lines are the last to reach the value db = 1, at μ = 0.48, fragment A is symmetrically covered in red and purple. Therefore, with very high spatial resistance, fragment A is dominated by two cities, namely by 1 and 6.In turn, Fig. 7(fragment D) illustrates the variability of the fractal dimension of boundaries of the attraction basins in this fragment. This dimension depends on the complexity of the mosaic patterns formed in this fragment, with varying μ values. When the values of μ are close to zero, all cities contribute to filling the space of fragment D. When μ = 0.18, city 1 (purple color) falls out of the competition for space, but only up to the value of μ = 0.24, when it starts to compete again with other cities. From the point of view of spatial interactions, in the final phase of this process (μ = 0.44), city 2 (blue) and city 6 (red) dominate to a small extent, because cities 3, 4 and 6, starting from μ = 0.3, do not play any role in fragment D.Figure 7 shows that the value μ = 0.3 is a characteristic point. It is a locus where all the curves representing the attraction basins of individual cities meet. As has already been stated, three of them lose their influence over the space of fragment D.Local dimensions of parts of the attraction basins treated as an irregular geometric figureIn each of the selected fragments A, …, E, some of the boundaries of the attraction basins of individual cities are distributed differently. They create certain holes in the form of irregularly colored mosaic patterns that have a certain fractal dimension. To present its variability, fragments A and D were used again. Figure 8 shows the distribution of db values depending on the value of μ.Figure 8Local dimensions of parts of the attraction basins treated as an irregular geometric figure in (A) and (D). Legend: The icons illustrate the variability of the shape of some of the attraction basins of individual cities in fragment (A) and (D) for cities 3, 4 and 6.Full size imageThe function has several characteristic points. Up to the value of μ = 0.04, attraction basins show a jumble in which no predominant color or shape can be identified. The fractal dimension is then: db = 1.7697. From this value onwards, where μ = 0.042, the interior of fragment A becomes increasingly ordered. With a value of μ = 0.125, the city’s attraction basins 3 and 4 begin to disappear in fragment A. The same happens to the city attraction basins 2 and 5 for the value of μ = 0.24.The final effect of the increase in space resistance (with μ = 0.50) leads to the filling of fragment A with two colors, i.e., purple and red. This means that cities 1 and 6, have won the competition for the space of fragment A. In this case, the fractal dimensions db equal 1.90.Figure 8 presents the variability of the fractal dimension and the effects of the competition for space between cities in fragment D. As is the case in fragment A and all others, i.e. B, C and E (see the Annex with Supplementary Material), the intertwined attraction basins are represented by the area consisting of an endless number of differently colored dots. Hence, up to the value of μ = 0.042, fragment D is dominated by pure spatial chaos that extends over its entire area. It is characterized by the fractal dimension db = 1.7697. This means that with an increase in the value of μ, for the emergence of an irregular shape of a geometric figure, chaos must be accompanied by an increase in the value of the fractal dimension. Its limiting value is number 2. Then, spatial dominance is usually gained by one city and the examined fragment is filled with one color (‘the winner takes it all’).This is precisely the situation in Fig. 8 where city 1 (purple color) has apparently won the competition. Since this color fills area D completely, we find the plausible result db = 2.0. More

Costanza, R. et al. Twenty years of ecosystem services: How far have we come and how far do we still need to go?. Ecosyst. Serv. 28, 1–16. https://doi.org/10.1016/j.ecoser.2017.09.008 (2017).Article 

Google Scholar 
Harwell, M. A. et al. Conceptual framework for assessing ecosystem health. Integr. Environ. Assess. Manag. 15, 544–564. https://doi.org/10.1002/ieam.4152 (2019).Article 

Google Scholar 
Halpern, B. S. et al. A global map of human impact on marine ecosystems. Science 319, 948–952. https://doi.org/10.1126/science.1149345 (2008).Article 
ADS 
CAS 

Google Scholar 
Roca, G. et al. Response of seagrass indicators to shifts in environmental stressors: A global review and management synthesis. Ecol. Ind. 63, 310–323. https://doi.org/10.1016/j.ecolind.2015.12.007 (2016).Article 

Google Scholar 
Westgate, M. J., Likens, G. E. & Lindenmayer, D. B. Adaptive management of biological systems: A review. Biol. Cons. 158, 128–139. https://doi.org/10.1016/j.biocon.2012.08.016 (2013).Article 

Google Scholar 
Logan, M. et al. Ecosystem health report cards: An overview of frameworks and analytical methodologies. Ecol. Indic. 113, 105834. https://doi.org/10.1016/j.ecolind.2019.105834 (2020).Article 

Google Scholar 
Dennison, W. C., Lookingbill, T. R., Carruthers, T. J., Hawkey, J. M. & Carter, S. L. An eye-opening approach to developing and communicating integrated environmental assessments. Front. Ecol. Environ. 5, 307–314. https://doi.org/10.1890/1540-9295(2007)5[307:AEATDA]2.0.CO;2 (2007).Article 

Google Scholar 
Harwell, M. A. et al. A framework for an ecosystem integrity report card: examples from south Florida show how an ecosystem report card links societal values and scientific information. Bioscience 49, 543–556. https://doi.org/10.2307/1313475 (1999).Article 

Google Scholar 
Collier, C. J. et al. An evidence-based approach for setting desired state in a complex Great Barrier Reef seagrass ecosystem: A case study from Cleveland Bay. Environ. Sustain. Indic. 7, 100042. https://doi.org/10.1016/j.indic.2020.100042 (2020).Article 

Google Scholar 
Coles, R. G. et al. Seagrass: Ecology, Uses and Threats (Nova Science Publishers, Inc., 2011).
Google Scholar 
Grech, A. et al. A comparison of threats, vulnerabilities and management approaches in global seagrass bioregions. Environ. Res. Lett. 7, 024006. https://doi.org/10.1088/1748-9326/7/2/024006 (2012).Article 
ADS 

Google Scholar 
Lambert, V. M. et al. Connecting targets for catchment sediment loads to ecological outcomes for seagrass using multiple lines of evidence. Mar. Pollut. Bull. https://doi.org/10.1016/j.marpolbul.2021.112494 (2021).Article 

Google Scholar 
Adams, M. P. et al. Predicting seagrass decline due to cumulative stressors. Environ. Model. Softw. 130, 104717. https://doi.org/10.1016/j.envsoft.2020.104717 (2020).Article 

Google Scholar 
Chartrand, K. M., Szabó, M., Sinutok, S., Rasheed, M. A. & Ralph, P. J. Living at the margins: The response of deep-water seagrasses to light and temperature renders them susceptible to acute impacts. Mar. Environ. Res. 136, 126–138. https://doi.org/10.1016/j.marenvres.2018.02.006 (2018).Article 
CAS 

Google Scholar 
Chartrand, K., Bryant, C., Carter, A., Ralph, P. & Rasheed, M. Light thresholds to prevent dredging impacts on the Great Barrier Reef seagrass, Zostera muelleri spp. capricorni. Front. Mar. Sci. 3, 17. https://doi.org/10.3389/fmars.2016.00106 (2016).Article 

Google Scholar 
Abal, E. & Dennison, W. Seagrass depth range and water quality in southern Moreton Bay, Queensland, Australia. Mar. Freshwater Res. 47, 763–771. https://doi.org/10.1071/MF9960763 (1996).Article 
CAS 

Google Scholar 
Dennison, W. et al. Assessing water quality with submersed aquatic vegetation: Habitat requirements as barometers of Chesapeake Bay health. Bioscience 43, 86–94. https://doi.org/10.2307/1311969 (1993).Article 

Google Scholar 
Carter, A. B., Collier, C., Coles, R., Lawrence, E. & Rasheed, M. A. Community-specific, “desired” states for seagrasses through cycles of loss and recovery. J. Environ. Manag. 314, 115059. https://doi.org/10.1016/j.jenvman.2022.115059 (2022).Article 

Google Scholar 
Kaldy, J. E., Brown, C. A. & Pacella, S. R. Carbon limitation in response to nutrient loading in an eelgrass mesocosm: Influence of water residence time. Mar. Ecol. Prog. Ser. 689, 1–17. https://doi.org/10.3354/meps14061 (2022).Article 
CAS 

Google Scholar 
Carter, A. B. et al. A spatial analysis of seagrass habitat and community diversity in the Great Barrier Reef World Heritage Area. Sci. Rep. https://doi.org/10.1038/s41598-021-01471-4 (2021).Article 

Google Scholar 
Kenworthy, W. J., Wyllie-Echeverria, S., Coles, R. G., Pergent, G. & Pergent-Martini, C. Seagrasses: Biology, Ecology and Conservation 595–623 (Springer, 2006).
Google Scholar 
Hayes, M. A. et al. The differential importance of deep and shallow seagrass to nekton assemblages of the great barrier reef. Diversity 12, 292. https://doi.org/10.3390/d12080292 (2020).Article 

Google Scholar 
Marsh, H., O’Shea, T. J. & Reynolds, J. E. III. Ecology and Conservation of the Sirenia: Dugongs and Manatees Vol. 18 (Cambridge University Press, 2011).Book 

Google Scholar 
Scott, A. L. et al. The role of herbivory in structuring tropical seagrass ecosystem service delivery. Front. Plant Sci. 9, 1–10. https://doi.org/10.3389/fpls.2018.00127 (2018).Article 

Google Scholar 
York, P. H., Macreadie, P. I. & Rasheed, M. A. Blue carbon stocks of Great Barrier Reef deep-water seagrasses. Biol. Lett. 14, 20180529. https://doi.org/10.1098/rsbl.2018.0529 (2018).Article 
CAS 

Google Scholar 
Unsworth, R. K., Collier, C. J., Waycott, M., Mckenzie, L. J. & Cullen-Unsworth, L. C. A framework for the resilience of seagrass ecosystems. Mar. Pollut. Bull. 100, 34–46. https://doi.org/10.1016/j.marpolbul.2015.08.016 (2015).Article 
CAS 

Google Scholar 
Madden, C. J., Rudnick, D. T., McDonald, A. A., Cunniff, K. M. & Fourqurean, J. W. Ecological indicators for assessing and communicating seagrass status and trends in Florida Bay. Ecol. Ind. 9, S68–S82. https://doi.org/10.1016/j.ecolind.2009.02.004 (2009).Article 
CAS 

Google Scholar 
York, P. et al. Dynamics of a deep-water seagrass population on the Great Barrier Reef: Annual occurrence and response to a major dredging program. Sci. Rep. 5, 13167. https://doi.org/10.1038/srep13167 (2015).Article 
ADS 
CAS 

Google Scholar 
Rasheed, M. A., McKenna, S. A., Carter, A. B. & Coles, R. G. Contrasting recovery of shallow and deep water seagrass communities following climate associated losses in tropical north Queensland, Australia. Mar. Pollut. Bull. 83, 491–499. https://doi.org/10.1016/j.marpolbul.2014.02.013 (2014).Article 
CAS 

Google Scholar 
Smith, T., Chartrand, K., Wells, J., Carter, A. & Rasheed, M. Seagrasses in Port Curtis and Rodds Bay 2019 Annual long-term monitoring and whole port survey. 71, https://www.tropwater.com/wp-content/uploads/2022/10/20-64-Annual-Seagrass-monitoring-in-Port-Curtis-and-Rodds-Bay-2019.pdf (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 20/64, James Cook University, Cairns, 2020).Ruaro, R., Gubiani, E. A., Hughes, R. M. & Mormul, R. P. Global trends and challenges in multimetric indices of biological condition. Ecol. Indic. 110, 105862. https://doi.org/10.1016/j.ecolind.2019.105862 (2020).Article 

Google Scholar 
Kilminster, K. et al. Unravelling complexity in seagrass systems for management: Australia as a microcosm. Sci. Total Environ. 534, 97–109. https://doi.org/10.1016/j.scitotenv.2015.04.061 (2015).Article 
ADS 
CAS 

Google Scholar 
Collier, C. J., Chartrand, K., Honchin, C., Fletcher, A. & Rasheed, M. Light thresholds for seagrasses of the GBR: a synthesis and guiding document. Including knowledge gaps and future priorities. 41, http://nesptropical.edu.au/wp-content/uploads/2016/05/NESP-TWQ-3.3-FINAL-REPORTa.pdf (Report to the National Environmental Science Programme, Cairns, 2016).Bryant, C., Jarvis, J. C., York, P. & Rasheed, M. Gladstone Healthy Harbour Partnership Pilot Report Card; ISP011: Seagrass., 74, https://researchonline.jcu.edu.au/44549/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 14/53, James Cook University, Cairns, 2014).McIntosh, E. J. et al. Designing report cards for aquatic health with a whole-of-system approach: Gladstone Harbour in the Great Barrier Reef. Ecol. Ind. 102, 623–632. https://doi.org/10.1016/j.ecolind.2019.03.012 (2019).Article 

Google Scholar 
Birch, W. & Birch, M. Succession and pattern of tropical intertidal seagrasses in Cockle Bay, Queensland, Australia: A decade of observations. Aquat. Bot. 19, 343–367. https://doi.org/10.1016/0304-3770(84)90048-2 (1984).Article 

Google Scholar 
Rasheed, M. A. Recovery and succession in a multi-species tropical seagrass meadow following experimental disturbance: The role of sexual and asexual reproduction. J. Exp. Mar. Biol. Ecol. 310, 13–45. https://doi.org/10.1016/j.jembe.2004.03.022 (2004).Article 

Google Scholar 
Christiaen, B., Lehrter, J., Goff, J. & Cebrian, J. Functional implications of changes in seagrass species composition in two shallow coastal lagoons. Mar. Ecol. Prog. Ser. 557, 11. https://doi.org/10.3354/meps11847 (2016).Article 

Google Scholar 
Hyndes, G. A., Kendrick, A. J., MacArthur, L. D. & Stewart, E. Differences in the species- and size-composition of fish assemblages in three distinct seagrass habitats with differing plant and meadow structure. Mar. Biol. 142, 1195–1206. https://doi.org/10.1007/s00227-003-1010-2 (2003).Article 

Google Scholar 
Ray, B. R., Johnson, M. W., Cammarata, K. & Smee, D. L. Changes in seagrass species composition in Northwestern Gulf of Mexico Estuaries: Effects on associated seagrass Fauna. PLoS ONE 9, e107751. https://doi.org/10.1371/journal.pone.0107751 (2014).Article 
ADS 
CAS 

Google Scholar 
Ondiviela, B. et al. The role of seagrasses in coastal protection in a changing climate. Coast. Eng. 87, 11. https://doi.org/10.1016/j.coastaleng.2013.11.005 (2014).Article 

Google Scholar 
Lavery, P. S., Mateo, M. -Á., Serrano, O. & Rozaimi, M. Variability in the carbon storage of seagrass habitats and its implications for global estimates of blue carbon ecosystem service. PLoS ONE 8, e73748. https://doi.org/10.1371/journal.pone.0073748 (2013).Article 
ADS 
CAS 

Google Scholar 
Coles, R. G. et al. The Great Barrier Reef World Heritage Area seagrasses: Managing this iconic Australian ecosystem resource for the future. Estuar. Coast. Shelf Sci. 153, A1–A12. https://doi.org/10.1016/j.ecss.2014.07.020 (2015).Article 
ADS 

Google Scholar 
Smith, T. M., Reason, C., McKenna, S. & Rasheed, M. A. Seagrasses in Port Curtis and Rodds Bay 2020. Annual long-term monitoring. 54, https://www.dropbox.com/s/f5yb6bjjpbvc1f2/21%2016%20Smith%20et%20al%202021%20Annual%20Seagrass%20monitoring%20in%20Port%20Curtis%20and%20Rodds%20Bay%202020_Final%20version.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/16, James Cook University, Cairns, 2021).Windle, J., Rolfe, J. & Pascoe, S. Assessing recreational benefits as an economic indicator for an industrial harbour report card. Ecol. Ind. 80, 224–231. https://doi.org/10.1016/j.ecolind.2017.05.036 (2017).Article 

Google Scholar 
Scott, A. & Rasheed, M. A. Port of Karumba long-term annual seagrass monitoring 2020. 28, https://www.dropbox.com/s/fwtys67ljssbp9t/21%2005%20Scott%20%26%20Rasheed%202021%20FINAL%202020%20Karumba%20Long-term%20seagrass%20monitoring%20report%20low%20res.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/05, James Cook University, Cairns, 2021).
Google Scholar 
Smith, T., Reason, C., McKenna, S. & Rasheed, M. Port of Weipa long‐term seagrass monitoring program, 2000 ‐ 2020. 49, https://www.dropbox.com/s/ghqy3bmn9p8jbsi/20%2058%20Smith%20et%20al%202020%20Port%20of%20Weipa%20Annual%20Long%20Term%20Seagrass%20Monitoring%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 20/58, James Cook University, Cairns, 2020).Reason, C. L., Smith, T. M. & Rasheed, M. A. Seagrass habitat of Cairns Harbour and Trinity Inlet: Cairns Shipping Development Program and Annual Monitoring Report 2020. 54, https://www.dropbox.com/s/m7xtrytjjip3a42/21%2009%20Final_Cairns%20Harbour%20Seagrass%20Monitoring%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/09, James Cook University, Cairns, 2021).Reason, C. L., York, P. H. & Rasheed, M. A. Seagrass habitat of Mourilyan Harbour: Annual monitoring report – 2020. 36, https://www.dropbox.com/s/kg3toxmlifh62tg/21%2010%20Mourilyan%20Harbour%20seagrass%20monitoring%20report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/10, James Cook University, Cairns, 2021).McKenna, S., Wilkinson, J., Chartrand, K. & Van De Wetering, C. Port of Townsville Seagrass Monitoring Program: 2020. 62, https://www.dropbox.com/s/n8nsx8ts93fgr36/21%2014%20Final%20POTL%20Annual%20Seagrass%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/14, James Cook University, Cairns, 2021).McKenna, S. A., van de Wetering, C., Wilkinson, J. & Rasheed, M. A. Port of Abbot Point long-term seagrass monitoring program: 2020. 35, https://www.dropbox.com/s/l5a5l7pkikcjrfb/21%2025%20McKenna%20et%20al%20Port%20of%20Abbot%20Point%20Long-term%20seagrass%20Monitoring%20report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/25, James Cook University, Cairns, 2021).York, P. H. & Rasheed, M. A. Annual Seagrass Monitoring in the Mackay-Hay Point Region – 2020. 42, https://www.dropbox.com/s/u45yezm3984lw1a/21%2020%20Hay%20Point%20and%20Mackay%20Seagrass%20Final%20Report%202020.pdf?dl=0 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/20, James Cook University, Cairns, 2021).van de Wetering, C., Carter, A. B. & Rasheed, M. A. Mackay-Whitsunday-Isaac Seagrass Monitoring 2017–2020: Marine Inshore South Zone. 30, https://researchonline.jcu.edu.au/70923/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/06, James Cook University, Cairns, 2021).Carter, A. B. et al. Torres Strait Seagrass 2021 Report Card. 76, https://researchonline.jcu.edu.au/70797/ (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 21/13, James Cook University, Cairns, 2021).Gladstone Ports Corporation. Port of Gladstone. https://www.gpcl.com.au/port-of-gladstone (2022).Sawynok, B., Venables, B. & Pinto, U. Incorporating a fish recruitment indicator into a health report card: A case study from Gladstone Harbour, Australia. Ecol. Indic. 115, 106329. https://doi.org/10.1016/j.ecolind.2020.106329 (2020).Article 

Google Scholar 
Pascoe, S. et al. Developing a social, cultural and economic report card for a regional industrial harbour. PLoS ONE 11, e0148271. https://doi.org/10.1371/journal.pone.0148271 (2016).Article 
CAS 

Google Scholar 
Chartrand, K. M., Bryant, C. V., Sozou, A., Ralph, P. J. & Rasheed, M. A. Final Report: Deep‐water seagrass dynamics ‐ Light requirements, seasonal change and mechanisms of recruitment. 67, https://www.dropbox.com/sh/mo8dcq1322qv5c3/AAAgu3lEnJsLgxdawXaOltu-a/2017?dl=0&preview=17+16+Final+Report+Deep-water+seagrass+dynamics.pdf&subfolder_nav_tracking=1 (Centre for Tropical Water & Aquatic Ecosystem Research (TropWATER) Publication 17/16, James Cook University, Cairns, 2017).Kirkman, H. Decline of seagrass in northern areas of Moreton Bay, Queensland. Aquat. Bot. 5, 63–76. https://doi.org/10.1016/0304-3770(78)90047-5 (1978).Article 

Google Scholar 
Mellors, J. E. An evaluation of a rapid visual technique for estimating seagrass biomass. Aquat. Bot. 42, 67–73. https://doi.org/10.1016/0304-3770(91)90106-F (1991).Article 

Google Scholar 
Emmer, I. et al. Methodology for tidal wetland and seagrass restoration VM0033, version 2.0. https://verra.org/wp-content/uploads/2018/03/VM0033-Methodology-for-Tidal-Wetland-and-Seagrass-Restoration-v2.0-30Sep21-1.pdf (2021). More

Guichard, F., Levin, S. A., Hastings, A. & Siegel, D. Toward a dynamic metacommunity approach to marine reserve theory. BioScience 54(11), 1003. https://doi.org/10.1641/0006-3568(2004)054[1003:tadmat]2.0.co;2 (2004).Article 

Google Scholar 
Wieters, E. A., Gaines, S. D., Navarrete, S. A., Blanchette, C. A. & Menge, B. A. Scales of dispersal and the biogeography of marine predator-prey interactions. Am. Nat. 171(3), 405–417. https://doi.org/10.1086/527492 (2008).Article 

Google Scholar 
Martínez-Moreno, J. et al. Global changes in oceanic mesoscale currents over the satellite altimetry record. Nat. Clim. Changehttps://doi.org/10.1038/s41558-021-01006-9 (2021).Article 

Google Scholar 
van Gennip, S. J. et al. Going with the flow: The role of ocean circulation in global marine ecosystems under a changing climate. Glob. Change Biol. 23(7), 2602–2617. https://doi.org/10.1111/gcb.13586 (2017).Article 
ADS 

Google Scholar 
O’Connor, M. I. et al. Temperature control of larval dispersal and the implications for marine ecology, evolution, and conservation. Proc. Natl. Acad. Sci. U.S.A. 104(4), 1266–1271. https://doi.org/10.1073/pnas.0603422104 (2007).Article 
ADS 
CAS 

Google Scholar 
Cowen, R. K. & Sponaugle, S. Larval dispersal and marine population connectivity. Ann. Rev. Mar. Sci. 1(1), 443–466. https://doi.org/10.1146/annurev.marine.010908.163757 (2009).Article 

Google Scholar 
Ospina-Alvarez, A., Parada, C. & Palomera, I. Vertical migration effects on the dispersion and recruitment of European anchovy larvae: From spawning to nursery areas. Ecol. Model. 231, 65–79. https://doi.org/10.1016/j.ecolmodel.2012.02.001 (2012).Article 

Google Scholar 
Selkoe, K. A. & Toonen, R. J. Marine connectivity: A new look at pelagic larval duration and genetic metrics of dispersal. Mar. Ecol. Prog. Ser. 436, 291–305. https://doi.org/10.3354/meps09238 (2011).Article 
ADS 

Google Scholar 
Siegel, D. A. et al. The stochastic nature of larval connectivity among nearshore marine populations. Proc. Natl. Acad. Sci. U.S.A. 105(26), 8974–8979. https://doi.org/10.1073/pnas.0802544105 (2008).Article 
ADS 

Google Scholar 
De Lestang, S. et al. What caused seven consecutive years of low puerulus settlement in the western rock lobster fishery of Western Australia?. ICES J. Mar. Sci. 72, i49–i58. https://doi.org/10.1093/icesjms/fsu177 (2015).Article 

Google Scholar 
Linnane, A. et al. Evidence of large-scale spatial declines in recruitment patterns of southern rock lobster Jasus edwardsii, across south-eastern Australia. Fish. Res. 105(3), 163–171. https://doi.org/10.1016/j.fishres.2010.04.001 (2010).Article 

Google Scholar 
Briones-Fourzán, P., Candela, J. & Lozano-Álvarez, E. Postlarval settlement of the spiny lobster Panulirus argus along the Caribbean coast of Mexico: Patterns, influence of physical factors, and possible sources of origin. Limnol. Oceanogr. 53(3), 970–985. https://doi.org/10.4319/lo.2008.53.3.0970 (2008).Article 
ADS 

Google Scholar 
Haury, L. R., McGowan, J. A. & Wiebe, P. H. Patterns and processes in the time-space scales of plankton distributions. In Spatial Pattern in Plankton Communities (ed. Steele, J. H.) 277–327 (Springer US, 1978). https://doi.org/10.1007/978-1-4899-2195-6_12.Cowen, R. K., Paris, C. B. & Srinivasan, A. Scaling of connectivity in marine populations. Science 311(5760), 522–527. https://doi.org/10.1126/science.1122039 (2006).Article 
ADS 
CAS 

Google Scholar 
Kavanaugh, M. T. et al. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. ICES J. Mar. Sci. 73(7), 1839–1850. https://doi.org/10.1093/icesjms/fsw086 (2016).Article 

Google Scholar 
Ospina-Alvarez, A., Weidberg, N., Aiken, C. M. & Navarrete, S. A. Larval transport in the upwelling ecosystem of central Chile: The effects of vertical migration, developmental time and coastal topography on recruitment. Prog. Oceanogr. 168, 82–99. https://doi.org/10.1016/j.pocean.2018.09.016 (2018) http://www.sciencedirect.com/science/article/pii/S0079661117300800.Article 
ADS 

Google Scholar 
Palumbi, S. Population genetics, demographic connectivity, and the design of marine reserves. Ecol. Appl. 13(1 Supplement), S146–S158 (2003).Article 

Google Scholar 
Barahona, M. et al. Environmental and demographic factors influence the spatial genetic structure of an intertidal barnacle in central-northern Chile. Mar. Ecol. Prog. Ser. 612, 151–165. https://doi.org/10.3354/meps12855 (2019) http://www.int-res.com/abstracts/meps/v612/p151-165/.Article 
ADS 

Google Scholar 
Spanier, E. et al. A concise review of lobster utilization by worldwide human populations from prehistory to the modern era. ICES J. Mar. Sci. 72(May), i7–i21. https://doi.org/10.1093/icesjms/fsv066 (2015).Article 

Google Scholar 
IUCN. Palinurus elephas: Goñi, R.: The IUCN Red List of Threatened Species 2014: e.T169975A1281221. Tech. Rep., International Union for Conservation of Nature (2013). http://www.iucnredlist.org/details/169975/0. Type: dataset.Canepa, A. et al. Pelagia noctiluca in the mediterranean sea (eds Pitt, K. A. & Lucas, C. H.) In Jellyfish Blooms, Vol. 9789400770 237–266 (Springer Netherlands, 2014). https://doi.org/10.1007/978-94-007-7015-7_11.Bosch-Belmar, M. et al. Jellyfish blooms perception in Mediterranean finfish aquaculture. Mar. Policy 76, 1–7. https://doi.org/10.1016/j.marpol.2016.11.005 (2017).Article 

Google Scholar 
Exceltur. Impactur baleares 2014. Tech. Rep., EXCELTUR – Govern de les Illes Balears, Madrid (2014).Vignudelli, S., Gasparini, G. P., Astraldi, M. & Schiano, M. E. A possible influence of the North Atlantic Oscillation on the circulation of the Western Mediterranean Sea. Geophys. Res. Lett. 26(5), 623–626. https://doi.org/10.1029/1999GL900038 (1999).Article 
ADS 

Google Scholar 
Somot, S. et al. Characterizing, modelling and understanding the climate variability of the deep water formation in the North-Western Mediterranean Sea. Clim. Dyn. 51(3), 1179–1210. https://doi.org/10.1007/s00382-016-3295-0 (2018).Article 

Google Scholar 
Díaz, D., Marí, M., Abelló, P. & Demestre, M. Settlement and juvenile habitat of the European spiny lobster Palinurus elephas (Crustacea: Decapoda: Palinuridae) in the Western Mediterranean Sea. Sci. Mar. 65(4), 347–356. https://doi.org/10.3989/scimar.2001.65n4347 (2001).Article 

Google Scholar 
Muñoz, A. et al. Exploration of the inter-annual variability and multi-scale environmental drivers of European spiny lobster, Palinurus elephas (Decapoda: Palinuridae) settlement in the NW Mediterranean. Mar. Ecol.https://doi.org/10.1111/maec.12654 (2021).Article 

Google Scholar 
Malej, A. & Malej, M. Population dynamics of the jellyfish Pelagia noctiluca (Forsskal, 1775) In Marine Eutrophication and Population Dynamics (eds Colombo, G., Ferrari, I., V., C. & R., R.) 215–219 (Olsen and Olsen, 1992).Ottmann, D. et al. Abundance of Pelagia noctiluca early life stages in the western Mediterranean Sea scales with surface chlorophyll. Mar. Ecol. Prog. Ser. 658, 75–88. https://doi.org/10.3354/meps13423 (2021).Article 
ADS 
CAS 

Google Scholar 
Benedetti-Cecchi, L. et al. Deterministic factors overwhelm stochastic environmental fluctuations as drivers of jellyfish outbreaks. PLoS One 10(10), 1–16. https://doi.org/10.1371/journal.pone.0141060 (2015).Article 
CAS 

Google Scholar 
Licandro, P. et al. A blooming jellyfish in the northeast Atlantic and Mediterranean. Biol. Lett. 6(5), 688–691. https://doi.org/10.1098/rsbl.2010.0150 (2010).Article 
CAS 

Google Scholar 
Goy, J., Morand, P. & Etienne, M. Long-term fluctuations of Pelagia noctiluca (Cnidaria, Scyphomedusa) in the western Mediterranean Sea. Prediction by climatic variables. Deep Sea Res. Part A Oceanogr. Res. Pap. 36(2), 269–279 (1989). https://doi.org/10.1016/0198-0149(89)90138-6 .Yahia, M. N. D. et al. Are the outbreaks timing of Pelagia noctiluca (Forsskal, 1775) getting more frequent in the Mediterranean basin?. ICES Cooper. Res. Rep. 300, 8–14 (2010).
Google Scholar 
Ferraris, M. et al. Distribution of Pelagia noctiluca (Cnidaria, Scyphozoa) in the Ligurian Sea (NW Mediterranean Sea). J. Plankton Res. 34(10), 874–885. https://doi.org/10.1093/plankt/fbs049 (2012).Article 

Google Scholar 
Millot, C. Circulation in the Western Mediterranean Sea. J. Mar. Syst. 20(1–4), 423–442. https://doi.org/10.1016/S0924-7963(98)00078-5 (1999).Article 

Google Scholar 
Galarza, J. A. et al. The influence of oceanographic fronts and early-life-history traits on connectivity among littoral fish species. Proc. Natl. Acad. Sci. 106(5), 1473–1478. https://doi.org/10.1073/pnas.0806804106 (2009).Article 
ADS 

Google Scholar 
Fernández de Puelles, M. L. & Molinero, J. C. Decadal changes in hydrographic and ecological time-series in the Balearic Sea (western Mediterranean), identifying links between climate and zooplankton. ICES J. Mar. Sci. 65(3), 311–317. https://doi.org/10.1093/icesjms/fsn017 (2008).Article 

Google Scholar 
Arsouze, T. et al. CIESM (ed.) Sensibility analysis of the Western Mediterranean Transition inferred by four companion simulations. (ed. CIESM) EGU General Assembly Conference Abstracts, Vol. 1 of EGU General Assembly Conference Abstracts, 13073 (2013).Amores, A., Jordà, G., Arsouze, T. & Le Sommer, J. Up to what extent can we characterize ocean eddies using present-day gridded altimetric products?. J. Geophys. Res. Oceans 123(10), 7220–7236. https://doi.org/10.1029/2018JC014140 (2018).Article 
ADS 

Google Scholar 
Waldman, R. et al. Impact of the mesoscale dynamics on ocean deep convection: The 2012–2013 case study in the northwestern mediterranean sea. J. Geophys. Res. Oceans 122(11), 8813–8840. https://doi.org/10.1002/2016JC012587 (2017).Article 
ADS 

Google Scholar 
Lett, C. et al. A Lagrangian tool for modelling ichthyoplankton dynamics. Environ. Model. Softw. 23(9), 1210–1214. https://doi.org/10.1016/j.envsoft.2008.02.005 (2008).Article 

Google Scholar 
Brickman, D. & Smith, P. C. Lagrangian stochastic modeling in coastal oceanography. J. Atmos. Ocean. Technol. 19(1), 83–99. https://doi.org/10.1175/1520-0426(2002)0192.0.CO;2 (2002).Article 
ADS 

Google Scholar 
Goñi, R. & Latrouite, D. Review of the biology, ecology and fisheries of Palinurus spp. species of European waters: Palinurus elephas (Fabricius, 1787) and Palinurus mauritanicus (Gruvel, 1911). Cahiers de Biol. Mar. 46(2), 127–142 (2005).
Google Scholar 
Bjornsson, H. & Venegas, S. A manual for EOF and SVD analyses of climatic data. Tech. Rep. CCGCR Report No. 97-1, McGill s Centre for Climate and Global Change Research (C2GCR) (1997).Herrmann, M., Somot, S., Sevault, F., Estournel, C. & Déqué, M. Modeling the deep convection in the northwestern mediterranean sea using an eddy-permitting and an eddy-resolving model: Case study of winter 1986–1987. J. Geophys. Res. Oceans 113(C4) (2008). https://doi.org/10.1029/2006JC003991.Hersbach, H. et al. ERA5 monthly averaged data on single levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10, 252–266 (2019). https://doi.org/10.24381/cds.f17050d7 .Bernard, P., Berline, L. & Gorsky, G. Long term (1981–2008) monitoring of the jellyfish Pelagia noctiluca (Cnidaria, Scyphozoa) on Mediterranean Coasts (Principality of Monaco and French Riviera). J. Oceanogr. Res. Data 4(1), 1–10 (2011).
Google Scholar 
Kough, A. S., Paris, C. B. & Butler, M. J. IV. Larval connectivity and the international management of fisheries. PLoS One 8(6), 1–12. https://doi.org/10.1371/journal.pone.0064970 (2013).Article 
CAS 

Google Scholar 
Sandvik, H. et al. Modelled drift patterns of fish larvae link coastal morphology to seabird colony distribution. Nat. Commun. 7(May), 1–8. https://doi.org/10.1038/ncomms11599 (2016).Article 
CAS 

Google Scholar 
Notarbartolo-Di-Sciara, G., Agardy, T., Hyrenbach, D., Scovazzi, T. & Van Klaveren, P. The Pelagos Sanctuary for Mediterranean marine mammals. Aquat. Conserv. Mar. Freshw. Ecosyst. 18(4), 367–391. https://doi.org/10.1002/aqc.855 (2008).Article 

Google Scholar 
Astraldi, M., Gasparini, G. P., Vetrano, a. & Vignudelli, S. Hydrographic characteristics and interannual variability of water masses in the central Mediterranean: A sensitivity test for long-term changes in the Mediterranean Sea. Deep Sea Res. Part I Oceanogr. Res. Pap. 49(4), 661–680 (2002). https://doi.org/10.1016/S0967-0637(01)00059-0 .Muffett, K. & Miglietta, M. P. Planktonic associations between medusae (classes Scyphozoa and Hydrozoa) and epifaunal crustaceans. PeerJ 9, e11281. https://doi.org/10.7717/peerj.11281 (2021) https://peerj.com/articles/11281.Article 

Google Scholar 
Stopar, K., Ramšak, A., Trontelj, P. & Malej, A. Lack of genetic structure in the jellyfish Pelagia noctiluca (Cnidaria: Scyphozoa: Semaeostomeae) across European seas. Mol. Phylogenet. Evol. 57(1), 417–428. https://doi.org/10.1016/j.ympev.2010.07.004 (2010).Article 
CAS 

Google Scholar 
Berline, L., Zakardjian, B., Molcard, A., Ourmières, Y. & Guihou, K. Modeling jellyfish Pelagia noctiluca transport and stranding in the Ligurian Sea. Mar. Pollut. Bull. 70(1–2), 90–99. https://doi.org/10.1016/j.marpolbul.2013.02.016 (2013).Article 
CAS 

Google Scholar 
Prieto, L., Macías, D., Peliz, A. & Ruiz, J. Portuguese Man-of-War (Physalia physalis) in the Mediterranean: A permanent invasion or a casual appearance? Sci. Rep. 5 (2015). https://doi.org/10.1038/srep11545.Houghton, J. D. R. et al. Identification of genetically and oceanographically distinct blooms of jellyfish. J. R. Soc. Interface 10(80), 20120920–20120920. https://doi.org/10.1098/rsif.2012.0920 (2013).Article 

Google Scholar 
Segura-García, I. et al. Reconstruction of larval origins based on genetic relatedness and biophysical modeling. Sci. Rep. 9(1), 1–9. https://doi.org/10.1038/s41598-019-43435-9 (2019).Article 
ADS 
CAS 

Google Scholar 
Elphie, H., Raquel, G., David, D. & Serge, P. Detecting immigrants in a highly genetically homogeneous spiny lobster population (Palinurus elephas) in the northwest Mediterranean Sea. Ecol. Evol. 2(10), 2387–2396. https://doi.org/10.1002/ece3.349 (2012).Article 

Google Scholar 
Babbucci, M. et al. Population structure, demographic history, and selective processes: Contrasting evidences from mitochondrial and nuclear markers in the European spiny lobster Palinurus elephas (Fabricius, 1787). Mol. Phylogenet. Evol. 56(3), 1040–1050. https://doi.org/10.1016/j.ympev.2010.05.014 (2010).Article 
CAS 

Google Scholar 
Cau, A. et al. European spiny lobster recovery from overfishing enhanced through active restocking in Fully Protected Areas. Sci. Rep. 9(1) (2019). https://doi.org/10.1038/s41598-019-49553-8 .Macias, D., Garcia-Gorriz, E. & Stips, A. Deep winter convection and phytoplankton dynamics in the NW Mediterranean Sea under present climate and future (Horizon 2030) scenarios. Sci. Rep. 8(1), 1–15. https://doi.org/10.1038/s41598-018-24965-0 (2018).Article 
CAS 

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