Ecology

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Martin-Roberts, E. et al. Carbon capture and storage at the end of a lost decade. One Earth 4, 1569–1584 (2021).Article 
ADS 

Google Scholar 
Liu, Z. et al. Challenges and opportunities for carbon neutrality in China. Nat. Rev. Earth Environ. 3, 141–155 (2022).Article 
ADS 

Google Scholar 
Wang, F. et al. Technologies and perspectives for achieving carbon neutrality. Innovation 2, 100180 (2021).CAS 

Google Scholar 
Third National Communication of Climate Change in the People’s Republic of China (Ministry of Ecology and Environment of the People’s Republic of China, 2018).Chen, X. et al. Producing more grain with lower environmental costs. Nature 514, 486–489 (2014).Article 
ADS 
CAS 

Google Scholar 
Cui, Z. et al. Pursuing sustainable productivity with millions of smallholder farmers. Nature 555, 363–366 (2018).Article 
ADS 
CAS 

Google Scholar 
Liu, B. et al. Promoting potato as staple food can reduce the carbon–land–water impacts of crops in China. Nat. Food 2, 570–577 (2021).Article 

Google Scholar 
Jiang, Y. et al. Water management to mitigate the global warming potential of rice systems: a global meta-analysis. Field Crops Res. 234, 47–54 (2019).Article 

Google Scholar 
Shang, Z. et al. Can cropland management practices lower net greenhouse emissions without compromising yield? Glob. Change Biol. 27, 4657–4670 (2021).Article 
CAS 

Google Scholar 
Xia, L. et al. Can knowledge-based N management produce more staple grain with lower greenhouse gas emission and reactive nitrogen pollution? A meta-analysis. Glob. Change Biol. 23, 1917–1925 (2016).Article 
ADS 

Google Scholar 
Ju, X. et al. Reducing environmental risk by improving N management in intensive Chinese agricultural systems. Proc. Natl Acad. Sci. USA 106, 3041–3046 (2009).Article 
ADS 
CAS 

Google Scholar 
Wang, B. et al. Four pathways towards carbon neutrality by controlling net greenhouse gas emissions in Chinese cropland. Resour. Conserv. Recycl. 186, 106576 (2022).Article 
CAS 

Google Scholar 
Xia, L. et al. Trade-offs between soil carbon sequestration and reactive nitrogen losses under straw return in global agroecosystems. Glob. Change Biol. 12, 5919–5932 (2018).Article 

Google Scholar 
Zhao, Y. et al. Economics- and policy-driven organic carbon input enhancement dominates soil organic carbon accumulation in Chinese croplands. Proc. Natl Acad. Sci. USA 115, 4045–4050 (2018).Article 
ADS 
CAS 

Google Scholar 
Yan, X., Akiyama, H., Yagi, K. & Akimoto, H. Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change guidelines. Glob. Biogeochemical Cycles 23, GB2002 (2009).Jiang, Y. et al. Acclimation of methane emissions from rice paddy fields to straw addition. Sci. Adv. 5, eaau9038 (2019).Article 
ADS 

Google Scholar 
Chen, Z. et al. Microbial process-oriented understanding of stimulation of soil N2O emission following the input of organic materials. Environ. Pollut. 284, 117176 (2021).Article 
CAS 

Google Scholar 
Lugato, E., Leip, A. & Jones, A. Mitigation potential of soil carbon management overestimated by neglecting N2O emissions. Nat. Clim. Change 8, 219–223 (2018).Article 
ADS 
CAS 

Google Scholar 
Xia, L., Wang, S. & Yan, X. Effects of long-term straw incorporation on the net global warming potential and the net economic benefit in a rice-wheat cropping system in China. Agric. Ecosyst. Environ. 197, 118–127 (2014).Article 

Google Scholar 
Xia, L., Ti, C., Li, B., Xia, Y. & Yan, X. Greenhouse gas emissions and reactive nitrogen releases during the life-cycles of staple food production in China and their mitigation potential. Sci. Total Environ. 556, 116–125 (2016).Article 
ADS 
CAS 

Google Scholar 
Yang, Y. et al. Restoring abandoned farmland to mitigate climate change on a full Earth. One Earth 3, 176–186 (2020).Article 
ADS 

Google Scholar 
Lehmann, J. et al. Biochar in climate change mitigation. Nat. Geosci. 14, 883–892 (2021).Article 
ADS 
CAS 

Google Scholar 
Woolf, D., Amonette, J. E., Street-Perrott, F. A., Lehmann, J. & Joseph, S. Sustainable biochar to mitigate global climate change. Nat. Commun. 1, 56 (2010).Article 
ADS 

Google Scholar 
Jeffery, S., Verheijen, F. G., Kammann, C. & Abalos, D. Biochar effects on methane emissions from soils: a meta-analysis. Soil Biol. Biochem. 101, 251–258 (2016).Article 
CAS 

Google Scholar 
Schmidt, H. P. et al. Biochar in agriculture – a systematic review of 26 global meta-analyses. GCB Bioenergy 13, 1708–1730 (2021).Article 
CAS 

Google Scholar 
Cayuela, M. L. et al. Biochar and denitrification in soils: when, how much and why does biochar reduce N2O emissions? Sci. Rep. 3, 1732 (2013).Article 

Google Scholar 
He, Y. et al. Effects of biochar application on soil greenhouse gas fluxes: a meta-analysis. GCB Bioenergy 9, 743–755 (2017).Article 
CAS 

Google Scholar 
Liu, Q. et al. Biochar application as a tool to decrease soil nitrogen losses (NH3 volatilization, N2O emissions, and N leaching) from croplands: options and mitigation strength in a global perspective. Glob. Change Biol. 25, 2077–2093 (2019).Article 
ADS 

Google Scholar 
He, X. et al. Effects of pyrolysis temperature on the physicochemical properties of gas and biochar obtained from pyrolysis of crop residues. Energy 143, 746–756 (2018).Article 
CAS 

Google Scholar 
Yang, Q. et al. Prospective contributions of biomass pyrolysis to China’s 2050 carbon reduction and renewable energy goals. Nat. Commun. 12, 1698 (2021).Article 
ADS 
CAS 

Google Scholar 
Smith, P. et al. Biophysical and economic limits to negative CO2 emissions. Nat. Clim. Change 6, 42–50 (2016).Article 
ADS 
CAS 

Google Scholar 
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).Ritchie, H., Roser, M. & Rosado, P. CO2 and Greenhouse Gas Emissions (Our World in Data, 2020); https://ourworldindata.org/co2-and-other-greenhouse-gas-emissionsLiu, Y. et al. A quantitative review of the effects of biochar application on rice yield and nitrogen use efficiency in paddy fields: a meta-analysis. Sci. Total Environ. 830, 154792 (2022).Article 
ADS 
CAS 

Google Scholar 
Cassman, K. G. & Grassini, P. A global perspective on sustainable intensification research. Nat. Sustain. 3, 262–268 (2020).Article 

Google Scholar 
Gu, B. et al. Abating ammonia is more cost-effective than nitrogen oxides for mitigating PM2.5 air pollution. Science 374, 758–762 (2021).Article 
ADS 
CAS 

Google Scholar 
Yang, Y., Reilly, E. C., Jungers, J. M., Chen, J. & Smith, T. M. Climate benefits of increasing plant diversity in perennial bioenergy crops. One Earth 1, 434–445 (2019).Article 
ADS 

Google Scholar 
Weller, S. et al. Methane and nitrous oxide emissions from rice and maize production in diversified rice cropping systems. Nutr. Cycling Agroecosyst. 101, 37–53 (2015).Article 
CAS 

Google Scholar 
Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8, 325–332 (2018).Article 
ADS 
CAS 

Google Scholar 
Gu, B., Zhang, X., Bai, X., Fu, B. & Chen, D. Four steps to food security for swelling cities. Nature 566, 31–33 (2019).Article 
ADS 
CAS 

Google Scholar 
Zhang, X. et al. Managing nitrogen for sustainable development. Nature 528, 51–59 (2015).Article 
ADS 
CAS 

Google Scholar 
Gu, B., Ju, X., Chang, J., Ge, Y. & Vitousek, P. M. Integrated reactive nitrogen budgets and future trends in China. Proc. Natl Acad. Sci. USA 112, 8792–8797 (2015).Article 
ADS 
CAS 

Google Scholar 
Galloway, J. N. et al. Transformation of the nitrogen cycle: recent trends, questions, and potential solutions. Science 320, 889–892 (2008).Article 
ADS 
CAS 

Google Scholar 
Lee, X. J., Ong, H. C., Gan, Y. Y., Chen, W. H. & Mahlia, T. M. I. State of art review on conventional and advanced pyrolysis of macroalgae and microalgae for biochar, bio-oil and bio-syngas production. Energy Convers. Manag. 210, 112707 (2020).Article 
CAS 

Google Scholar 
Nevzorova, T. & Kutcherov, V. Barriers to the wider implementation of biogas as a source of energy: a state-of-the-art review. Energy Strategy Rev. 26, 100414 (2019).Article 

Google Scholar 
Xia, S. et al. Pyrolysis behavior and economics analysis of the biomass pyrolytic polygeneration of forest farming waste. Bioresource Technol. 270, 189–197 (2018).Article 
CAS 

Google Scholar 
Liu, Z., Niu, W., Chu, H., Zhou, T. & Niu, Z. Effect of the carbonization temperature on the properties of biochar produced from the pyrolysis of crop residues. BioResources 13, 3429–3446 (2018).Article 
CAS 

Google Scholar 
Hengeveld, E. J., Bekkering, J., van Gemert, W. J. T. & Broekhuis, A. A. Biogas infrastructures from farm to regional scale, prospects of biogas transport grids. Biomass Bioenergy 86, 43–52 (2016).Article 

Google Scholar 
Ansari, S. H. et al. Incorporation of solar-thermal energy into a gasification process to co-produce bio-fertilizer and power. Environ. Pollut. 266, 115103 (2020).Article 
CAS 

Google Scholar 
Yang, S. I., Wu, M. S. & Hsu, T. C. Spray combustion characteristics of kerosene/bio-oil part I: experimental study. Energy 119, 26–36 (2017).Article 
CAS 

Google Scholar 
Xia, L. et al. Elevated CO2 negates O3 impacts on terrestrial carbon and nitrogen cycles. One Earth 4, 1752–1763 (2022).Article 
ADS 

Google Scholar 
Gu, B. et al. Atmospheric reactive nitrogen in China: sources, recent trends, and damage costs. Environ. Sci. Technol. 46, 9420–9427 (2012).Article 
ADS 
CAS 

Google Scholar 
Xia, L. et al. Greenhouse gas emissions and reactive nitrogen releases from rice production with simultaneous incorporation of wheat straw and nitrogen fertilizer. Biogeosciences 13, 4569–4579 (2016).Article 
ADS 
CAS 

Google Scholar  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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