Ecology

1.Stocker, T. F. et al. Climate Change 2013: The physical science basis. Contribution of Working Group I to the Fifth Assessment Report of IPCC the Intergovernmental Panel on Climate Change (Cambridge University Press, 2014).2.IPBES. Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES secretariat, 2019).3.Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).ADS 
Article 

Google Scholar 
4.Vellend, M. et al. Global meta-analysis reveals no net change in local-scale plant biodiversity over time. Proc. Natl Acad. Sci. 110, 19456–19459 (2013).ADS 
CAS 
Article 

Google Scholar 
5.DeMalach, N., Zaady, E. & Kadmon, R. Contrasting effects of water and nutrient additions on grassland communities: a global meta‐analysis. Glob. Ecol. Biogeogr. 26, 983–992 (2017).Article 

Google Scholar 
6.Gruner, D. S. et al. Effects of experimental warming on biodiversity depend on ecosystem type and local species composition. Oikos 126, 8–17 (2017).Article 

Google Scholar 
7.Komatsu, K. J. et al. Global change effects on plant communities are magnified by time and the number of global change factors imposed. Proc. Natl Acad. Sci. 116, 17867–17873 (2019).CAS 
Article 

Google Scholar 
8.Korell, L., Auge, H., Chase, J. M., Harpole, S. & Knight, T. M. We need more realistic climate change experiments for understanding ecosystems of the future. Glob. Change Biol. 26, 325–327 (2020).ADS 
Article 

Google Scholar 
9.Korell, L., Auge, H., Chase, J. M., Harpole, W. S. & Knight, T. M. Understanding plant communities of the future requires filling knowledge gaps. Glob. Change Biol. 26, 328–329 (2020).ADS 
Article 

Google Scholar 
10.Yue, K. et al. Changes in plant diversity and its relationship with productivity in response to nitrogen addition, warming and increased rainfall. Oikos 129, 939–952 (2020).11.Chase, J. M. & Knight, T. M. Scale‐dependent effect sizes of ecological drivers on biodiversity: why standardised sampling is not enough. Ecol. Lett. 16, 17–26 (2013).Article 

Google Scholar 
12.Chase, J. M. et al. Embracing scale‐dependence to achieve a deeper understanding of biodiversity and its change across communities. Ecol. Lett. 21, 1737–1751 (2018).Article 

Google Scholar 
13.Spake, R. et al. Implications of scale dependence for cross‐study syntheses of biodiversity differences. Ecol. Lett. 24, 374–390 (2020).Article 

Google Scholar 
14.Chesson, P. et al. Resource pulses, species interactions, and diversity maintenance in arid and semi-arid environments. Oecologia 141, 236–253 (2004).ADS 
Article 

Google Scholar 
15.Harrison, S., Spasojevic, M. J. & Li, D. Climate and plant community diversity in space and time. Proc. Natl Acad. Sci. 117, 4464–4470 (2020).CAS 
Article 

Google Scholar 
16.Wilcox, K. R. et al. Asymmetric responses of primary productivity to precipitation extremes: a synthesis of grassland precipitation manipulation experiments. Glob. Change Biol. 23, 4376–4385 (2017).Article 

Google Scholar 
17.Beier, C. et al. Precipitation manipulation experiments–challenges and recommendations for the future. Ecol. Lett. 15, 899–911 (2012).Article 

Google Scholar 
18.Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).ADS 
CAS 
Article 

Google Scholar 
19.Knapp, A. K. et al. Characterizing differences in precipitation regimes of extreme wet and dry years: implications for climate change experiments. Glob. Change Biol. 21, 2624–2633 (2015).ADS 
Article 

Google Scholar 
20.Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Ecol. Evol. Syst. 47, 215–237 (2016).Article 

Google Scholar 
21.Stuart-Haëntjens, E. et al. Mean annual precipitation predicts primary production resistance and resilience to extreme drought. Sci. Total Environ. 636, 360–366 (2018).ADS 
Article 

Google Scholar 
22.Adler, P. B. et al. Productivity is a poor predictor of plant species richness. Science 333, 1750–1753 (2011).ADS 
CAS 
Article 

Google Scholar 
23.Van der Plas, F. Biodiversity and ecosystem functioning in naturally assembled communities. Biol. Rev. 94, 1220–1245 (2019).PubMed 

Google Scholar 
24.Chase, J. M. & Leibold, M. A. Spatial scale dictates the productivity–biodiversity relationship. Nature 416, 427–430 (2002).ADS 
CAS 
Article 

Google Scholar 
25.Thompson, P. L., Isbell, F., Loreau, M., O’connor, M. I. & Gonzalez, A. The strength of the biodiversity–ecosystem function relationship depends on spatial scale. Proc. R. Soc. B 285, 20180038 (2018).Article 

Google Scholar 
26.Cleland, E. E. et al. Sensitivity of grassland plant community composition to spatial vs. temporal variation in precipitation. Ecology 94, 1687–1696 (2013).Article 

Google Scholar 
27.McCluney, K. E. et al. Shifting species interactions in terrestrial dryland ecosystems under altered water availability and climate change. Biol. Rev. 87, 563–582 (2012).Article 

Google Scholar 
28.Brooker, R. W. Plant–plant interactions and environmental change. New Phytol. 171, 271–284 (2006).Article 

Google Scholar 
29.Maron, J. L., Baer, K. C. & Angert, A. L. Disentangling the drivers of context‐dependent plant–animal interactions. J. Ecol. 102, 1485–1496 (2014).Article 

Google Scholar 
30.Harpole, W. S. et al. Addition of multiple limiting resources reduces grassland diversity. Nature 537, 93–96 (2016).ADS 
CAS 
Article 

Google Scholar 
31.Berdugo, M. et al. Global ecosystem thresholds driven by aridity. Science 367, 787–790 (2020).ADS 
CAS 
Article 

Google Scholar 
32.Huang, J., Yu, H., Guan, X., Wang, G. & Guo, R. Accelerated dryland expansion under climate change. Nat. Clim. Change 6, 166–171 (2016).ADS 
Article 

Google Scholar 
33.Safriel, U. et al. in Ecosystems and Human Well-being: Current State and Trends.: Findings of the Condition and Trends Working Group (Island Press, 2005).34.Hoover, D. L. et al. Traversing the wasteland: a framework for assessing ecological threats to drylands. BioScience 70, 35–47 (2020).Article 

Google Scholar 
35.Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).Article 

Google Scholar 
36.McGlinn, D. J. et al. Measurement of Biodiversity (MoB): a method to separate the scale‐dependent effects of species abundance distribution, density, and aggregation on diversity change. Methods Ecol. Evolution 10, 258–269 (2019).Article 

Google Scholar 
37.Chao, A. et al. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecol. Monogr. 84, 45–67 (2014).Article 

Google Scholar 
38.Hurlbert, S. H. The nonconcept of species diversity: a critique and alternative parameters. Ecology 52, 577–586 (1971).Article 

Google Scholar 
39.Jost, L. Entropy and diversity. Oikos 113, 363–375 (2006).Article 

Google Scholar 
40.Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evolution 7, 1451–1456 (2016).Article 

Google Scholar 
41.Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-6 (2019).42.Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta‐analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
43.Bates, D. et al. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw. 67, 1–48 (2015).44.Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17 (2020).45.Canty, A. & Ripley, B. boot: Bootstrap R (S-Plus) Functions. R package version 1.3–25 (2020).46.R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).47.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).48.Fox, J. et al. Visualizing Fit and Lack of Fit in Complex Regression Models with Predictor Effect Plots and Partial Residuals. J. Stat. Softw. 87, 1–27 (2018).49.Gelman, A.& Su, Y. arm: Data Analysis Using Regression and Multilevel/Hierarchical Models. R package version 1.11–2 (2020). More

1.Azam F, Malfatti F. Microbial structuring of marine ecosystems. Nat Rev Microbiol. 2007;5:782–91.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.Lechtenfeld OJ, Hertkorn N, Shen Y, Witt M, Benner R. Marine sequestration of carbon in bacterial metabolites. Nat Commun. 2015;6:6711.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
3.Buchan A, LeCleir GR, Gulvik CA, Gonzalez JM. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat Rev Microbiol. 2014;12:686–98.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.DeLong E (ed). Microbial Metagenomics, Metatranscriptomics, and Metaproteomics, 1st edn. San Diego, CA, USA: Academic Press; 2013.5.White RA III, Callister SJ, Moore RJ, Baker ES, Jansson JK. The past, present and future of microbiome analyses. Nat Protoc. 2016;11:2049.CAS 
Article 

Google Scholar 
6.Hanson CA, Fuhrman JA, Horner-Devine MC, Martiny JB. Beyond biogeographic patterns: processes shaping the microbial landscape. Nat Rev Microbiol. 2012;10:497–506.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Gilbert JA, Steele JA, Caporaso JG, Steinbruck L, Reeder J, Temperton B, et al. Defining seasonal marine microbial community dynamics. ISME J. 2012;6:298–308.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Needham DM, Fuhrman JA. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat Microbiol. 2016;1:16005.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Lindh MV, Sjostedt J, Andersson AF, Baltar F, Hugerth LW, Lundin D, et al. Disentangling seasonal bacterioplankton population dynamics by high-frequency sampling. Environ Microbiol. 2015;17:2459–76.PubMed 
Article 
PubMed Central 

Google Scholar 
10.Fuhrman JA, Cram JA, Needham DM. Marine microbial community dynamics and their ecological interpretation. Nat Rev Microbiol. 2015;13:133–46.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
11.Ruiz‐González C, Logares R, Sebastián M, Mestre M, Rodríguez‐Martínez R, Galí M, et al. Higher contribution of globally rare bacterial taxa reflects environmental transitions across the surface ocean. Mol Ecol. 2019;28:1930–45.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
12.Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Ocean plankton. Struct Funct Glob Ocean Microbiome Sci. 2015;348:1261359.
Google Scholar 
13.Ibarbalz FM, Henry N, Brandao MC, Martini S, Busseni G, Byrne H, et al. Global trends in marine plankton diversity across kingdoms of life. Cell. 2019;179:1084–97 e21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
14.Salazar G, Paoli L, Alberti A, Huerta-Cepas J, Ruscheweyh HJ, Cuenca M, et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell. 2019;179:1068–83 e21.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Boeuf D, Edwards BR, Eppley JM, Hu SK, Poff KE, Romano AE, et al. Biological composition and microbial dynamics of sinking particulate organic matter at abyssal depths in the oligotrophic open ocean. Proc Natl Acad Sci USA. 2019;116:11824–32.CAS 
PubMed 
PubMed Central 

Google Scholar 
16.Ghiglione JF, Galand PE, Pommier T, Pedros-Alio C, Maas EW, Bakker K, et al. Pole-to-pole biogeography of surface and deep marine bacterial communities. Proc Natl Acad Sci USA. 2012;109:17633–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Frias-Lopez J, Shi Y, Tyson GW, Coleman ML, Schuster SC, Chisholm SW, et al. Microbial community gene expression in ocean surface waters. Proc Natl Acad Sci USA. 2008;105:3805–10.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Acinas SG, Sánchez P, Salazar G, Cornejo-Castillo FM, Sebastián M, Logares R, et al. Metabolic architecture of the deep ocean microbiome. bioRxiv. 2019:635680. https://doi.org/10.1101/635680.19.Martin JH, Gordon RM, Fitzwater SE. Iron in Antarctic waters. Nature. 1990;345:156–8.CAS 
Article 

Google Scholar 
20.Church MJ, Hutchins DA, Ducklow HW. Limitation of bacterial growth by dissolved organic matter and iron in the Southern ocean. Appl Environ Microbiol. 2000;66:455–66.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Obernosterer I, Fourquez M, Blain S. Fe and C co-limitation of heterotrophic bacteria in the naturally fertilized region off the Kerguelen Islands. Biogeosciences. 2015;12:1983–92.Article 

Google Scholar 
22.Fourquez M, Obernosterer I, Blain S. A method for the use of the radiotracer 55Fe for microautoradiography and CARD-FISH of natural bacterial communities. FEMS Microbiol Lett. 2012;337:132–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Koedooder C, Gueneugues A, Van Geersdaële R, Vergé V, Bouget F-Y, Labreuche Y, et al. The role of the glyoxylate shunt in the acclimation to iron limitation in marine heterotrophic bacteria. Front Mar Sci. 2018;5:435.Article 

Google Scholar 
24.Blain S, Tagliabue A (eds). Iron Cycle in Oceans, 1st edn. London, UK: ISTE Ltd and John Wiley & Sons, Inc.; 2016.25.Dittmar T, Arnosti C. An inseparable liaison: marine microbes and nonliving organic matter. In: Gasol JM, Kirchman DL, editors. Microbial Ecology of the Oceans, 3rd edn. Hoboken NJ, USA: John Wiley and Sons, Inc.; 2018, pp 189–229.26.Blain S, Queguiner B, Armand L, Belviso S, Bombled B, Bopp L, et al. Effect of natural iron fertilization on carbon sequestration in the Southern Ocean. Nature. 2007;446:1070–4.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Lasbleiz M, Leblanc K, Armand LK, Christaki U, Georges C, Obernosterer I, et al. Composition of diatom communities and their contribution to plankton biomass in the naturally iron-fertilized region of Kerguelen in the Southern Ocean. FEMS Microbiol Ecol. 2016;92:fiw171.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
28.Obernosterer I, Catala P, Lebaron P, West NJ. Distinct bacterial groups contribute to carbon cycling during a naturally iron fertilized phytoplankton bloom in the Southern Ocean. Limnol Oceanogr. 2011;56:2391–401.CAS 
Article 

Google Scholar 
29.Blain S, Capparos J, Guéneuguès A, Obernosterer I, Oriol L. Distributions and stoichiometry of dissolved nitrogen and phosphorus in the iron-fertilized region near Kerguelen (Southern Ocean). Biogeosciences. 2015;12:623–35.Article 

Google Scholar 
30.d’Ovidio F, Della Penna A, Trull TW, Nencioli F, Pujol M-I, Rio M-H, et al. The biogeochemical structuring role of horizontal stirring: Lagrangian perspectives on iron delivery downstream of the Kerguelen Plateau. Biogeosciences. 2015;12:5567–81.Article 
CAS 

Google Scholar 
31.Landa M, Blain S, Christaki U, Monchy S, Obernosterer I. Shifts in bacterial community composition associated with increased carbon cycling in a mosaic of phytoplankton blooms. ISME J. 2016;10:39–50.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Landa M, Blain S, Harmand J, Monchy S, Rapaport A, Obernosterer I. Major changes in the composition of a Southern Ocean bacterial community in response to diatom-derived dissolved organic matter. FEMS Microbiol Ecol. 2018;94:8.Article 
CAS 

Google Scholar 
33.Fourquez M, Beier S, Jongmans E, Hunter R, Obernosterer I. Uptake of Leucine, chitin, and iron by prokaryotic groups during spring phytoplankton blooms induced by natural iron fertilization off Kerguelen Island (Southern Ocean). Front Mar Sci. 2016;3:256.Article 

Google Scholar 
34.Debeljak P, Toulza E, Beier S, Blain S, Obernosterer I. Microbial iron metabolism as revealed by gene expression profiles in contrasted Southern Ocean regimes. Environ Microbiol. 2019;21:2360–74.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Christaki U, Gueneugues A, Liu Y, Blain S, Catala P, Colombet J, et al. Seasonal microbial food web dynamics in contrasting Southern Ocean productivity regimes. Limnol Oceanogr. 2021;66:108–22.CAS 
Article 

Google Scholar 
36.Li D, Luo R, Liu CM, Leung CM, Ting HF, Sadakane K, et al. MEGAHIT v1.0: A fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods. 2016;102:3–11.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Uritskiy GV, DiRuggiero J, Taylor J. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018;6:158.PubMed 
PubMed Central 
Article 

Google Scholar 
38.Alneberg J, Bjarnason BS, de Bruijn I, Schirmer M, Quick J, Ijaz UZ, et al. Binning metagenomic contigs by coverage and composition. Nat Methods. 2014;11:1144–6.CAS 
PubMed 
Article 

Google Scholar 
39.Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
PubMed 
Article 

Google Scholar 
40.Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
PubMed Central 
Article 

Google Scholar 
41.Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Bendall ML, Stevens SL, Chan LK, Malfatti S, Schwientek P, Tremblay J, et al. Genome-wide selective sweeps and gene-specific sweeps in natural bacterial populations. ISME J. 2016;10:1589–601.PubMed 
PubMed Central 
Article 

Google Scholar 
43.Hyatt D, Chen GL, Locascio PF, Land ML, Larimer FW, Hauser LJ. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinforma. 2010;11:119.Article 
CAS 

Google Scholar 
44.Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. eggNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Huerta-Cepas J, Forslund K, Coelho LP, Szklarczyk D, Jensen LJ, von Mering C, et al. Fast genome-wide functional annotation through orthology assignment by eggNOG-Mapper. Mol Biol Evol. 2017;34:2115–22.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, et al. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–D32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
47.Eddy SR. Accelerated profile HMM searches. PLoS Comput Biol. 2011;7:e1002195.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Kanehisa M, Sato Y. Morishima K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J Mol Biol. 2016;428:726–31.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Aramaki T, Blanc-Mathieu R, Endo H, Ohkubo K, Kanehisa M, Goto S, et al. KofamKOALA: KEGG ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics. 2019;36:2251–2.PubMed Central 
Article 
CAS 

Google Scholar 
51.Saier MH Jr., Reddy VS, Tsu BV, Ahmed MS, Li C, Moreno-Hagelsieb G. The transporter classification database (TCDB): recent advances. Nucleic Acids Res. 2016;44:D372–9.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
53.Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 2009;37:D233–8.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Zhang H, Yohe T, Huang L, Entwistle S, Wu P, Yang Z, et al. dbCAN2: a meta server for automated carbohydrate-active enzyme annotation. Nucleic Acids Res. 2018;46:W95–W101.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
55.Rawlings ND, Barrett AJ, Thomas PD, Huang X, Bateman A, Finn RD. The MEROPS database of proteolytic enzymes, their substrates and inhibitors in 2017 and a comparison with peptidases in the PANTHER database. Nucleic Acids Res. 2018;46:D624–D32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Garber AI, Nealson KH, Okamoto A, McAllister SM, Chan CS, Barco RA, et al. FeGenie: a comprehensive tool for the identification of iron genes and iron gene neighborhoods in genome and metagenome assemblies. Front Microbiol. 2020;11:37.PubMed 
PubMed Central 
Article 

Google Scholar 
57.Dupont CL, Yang S, Palenik B, Bourne PE. Modern proteomes contain putative imprints of ancient shifts in trace metal geochemistry. Proc Natl Acad Sci USA. 2006;103:17822–7.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
58.Chaumeil PA, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.PubMed Central 

Google Scholar 
59.Emms DM, Kelly S. OrthoFinder: phylogenetic orthology inference for comparative genomics. Genome Biol. 2019;20:238.PubMed 
PubMed Central 
Article 

Google Scholar 
60.Katoh K, Standley DM. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol Biol Evol. 2013;30:772–80.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
61.Capella-Gutierrez S, Silla-Martinez JM, Gabaldon T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25:1972–3.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Nguyen LT, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 
PubMed 
Article 

Google Scholar 
63.Menzel P, Ng KL, Krogh A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat Commun. 2016;7:11257.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.CAS 
PubMed 
Article 

Google Scholar 
65.Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
66.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15:550.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
67.Rodriguez RL, Gunturu S, Tiedje JM, Cole JR, Konstantinidis KT. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. mSystems. 2018;3:3.Article 

Google Scholar 
68.Pearson WR An introduction to sequence similarity (“homology”) searching. Curr Protoc Bioinformatics. 2013;Chapter 3:Unit3 1.69.Jain C, Rodriguez RL, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Satinsky BM, Gifford SM, Crump BC, Moran MA. Use of internal standards for quantitative metatranscriptome and metagenome analysis. Methods Enzymol. 2013;531:237–50.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Louca S, Polz MF, Mazel F, Albright MBN, Huber JA, O’Connor MI, et al. Function and functional redundancy in microbial systems. Nat Ecol Evol. 2018;2:936–43.PubMed 
Article 
PubMed Central 

Google Scholar 
72.Kuhaudomlarp S, Patron NJ, Henrissat B, Rejzek M, Saalbach G, Field RA. Identification of Euglena gracilis beta-1,3-glucan phosphorylase and establishment of a new glycoside hydrolase (GH) family GH149. J Biol Chem. 2018;293:2865–76.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
73.Ho A, Di Lonardo DP, Bodelier PL. Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol. 2017;93:3.
Google Scholar 
74.Rodionov DA, Gelfand MS, Todd JD, Curson AR, Johnston AW. Computational reconstruction of iron- and manganese-responsive transcriptional networks in alpha-proteobacteria. PLoS Comput Biol. 2006;2:e163.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
75.Rincon-Enriquez G, Crete P, Barras F, Py B. Biogenesis of Fe/S proteins and pathogenicity: IscR plays a key role in allowing Erwinia chrysanthemi to adapt to hostile conditions. Mol Microbiol. 2008;67:1257–73.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
76.Py B, Barras F. Building Fe–S proteins: bacterial strategies. Nat Rev Microbiol. 2010;8:436–46.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
77.Zappa S, Bauer CE. Iron homeostasis in the Rhodobacter genus. Adv Bot Res. 2013;66:289–326.CAS 
Article 

Google Scholar 
78.Jaggavarapu S, O’Brian MR. Differential control of Bradyrhizobium japonicum iron stimulon genes through variable affinity of the iron response regulator (Irr) for target gene promoters and selective loss of activator function. Mol Microbiol. 2014;92:609–24.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
79.Grote J, Thrash JC, Huggett MJ, Landry ZC, Carini P, Giovannoni SJ, et al. Streamlining and core genome conservation among highly divergent members of the SAR11 clade. mBio. 2012;3:e00252–12.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
80.McAdams HH, Srinivasan B, Arkin AP. The evolution of genetic regulatory systems in bacteria. Nat Rev Genet. 2004;5:169–78.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
81.Fourquez M, Devez A, Schaumann A, Guéneuguès A, Jouenne T, Obernosterer I, et al. Effects of iron limitation on growth and carbon metabolism in oceanic and coastal heterotrophic bacteria. Limnol Oceanogr. 2014;59:349–60.CAS 
Article 

Google Scholar 
82.Wilson DN, Nierhaus KH. The weird and wonderful world of bacterial ribosome regulation. Crit Rev Biochem Mol Biol. 2007;42:187–219.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
83.Wei Y, Lee JM, Richmond C, Blattner FR, Rafalski JA, LaRossa RA. High-density microarray-mediated gene expression profiling of Escherichia coli. J Bacteriol. 2001;183:545–56.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
84.Hendrickson EL, Liu Y, Rosas-Sandoval G, Porat I, Soll D, Whitman WB, et al. Global responses of Methanococcus maripaludis to specific nutrient limitations and growth rate. J Bacteriol. 2008;190:2198–205.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
85.Gifford SM, Sharma S, Booth M, Moran MA. Expression patterns reveal niche diversification in a marine microbial assemblage. ISME J. 2013;7:281–98.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Sonnenburg ED, Zheng H, Joglekar P, Higginbottom SK, Firbank SJ, Bolam DN, et al. Specificity of polysaccharide use in intestinal bacteroides species determines diet-induced microbiota alterations. Cell. 2010;141:1241–52.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
87.Gregg KJ, Zandberg WF, Hehemann JH, Whitworth GE, Deng L, Vocadlo DJ, et al. Analysis of a new family of widely distributed metal-independent alpha-mannosidases provides unique insight into the processing of N-linked glycans. J Biol Chem. 2011;286:15586–96.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
88.Matulewicz M, Cerezo A. Water-soluble sulfated polysaccharides from the red seaweed Chaetangium fastigiatum. Analysis of the system and the structures of the α-D-(1→3)-linked mannans. Carbohydr Polym. 1987;7:121–32.CAS 
Article 

Google Scholar 
89.Kolender AA, Pujol CA, Damonte EB, Matulewicz MC, Cerezo AS. The system of sulfated α-(1→3)-linked D-mannans from the red seaweed Nothogenia fastigiata: structures, antiherpetic and anticoagulant properties. Carbohydr Res. 1997;304:53–60.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
90.Le Costaouëc T, Unamunzaga C, Mantecon L, Helbert WJAR. New structural insights into the cell-wall polysaccharide of the diatom Phaeodactylum tricornutum. Algal Res. 2017;26:172–9.Article 

Google Scholar 
91.Teeling H, Fuchs BM, Becher D, Klockow C, Gardebrecht A, Bennke CM, et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science. 2012;336:608–11.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
92.Teeling H, Fuchs BM, Bennke CM, Kruger K, Chafee M, Kappelmann L, et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. Elife. 2016;5:e11888.PubMed 
PubMed Central 
Article 

Google Scholar 
93.Chen J, Robb CS, Unfried F, Kappelmann L, Markert S, Song T, et al. Alpha‐and beta‐mannan utilization by marine Bacteroidetes. Environ Microbiol. 2018;20:4127–40.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
94.Biersmith A, Benner R. Carbohydrates in phytoplankton and freshly produced dissolved organic matter. Mar Chem. 1998;63:131–44.CAS 
Article 

Google Scholar 
95.Sichert A, Corzett CH, Schechter MS, Unfried F, Markert S, Becher D, et al. Verrucomicrobia use hundreds of enzymes to digest the algal polysaccharide fucoidan. Nat Microbiol. 2020;5:1026–39.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
96.Beja O, Aravind L, Koonin EV, Suzuki MT, Hadd A, Nguyen LP, et al. Bacterial rhodopsin: evidence for a new type of phototrophy in the sea. Science. 2000;289:1902–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
97.DeLong EF, Beja O. The light-driven proton pump proteorhodopsin enhances bacterial survival during tough times. PLoS Biol. 2010;8:e1000359.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
98.Olson DK, Yoshizawa S, Boeuf D, Iwasaki W, DeLong EF. Proteorhodopsin variability and distribution in the North Pacific Subtropical Gyre. ISME J. 2018;12:1047–60.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
99.Kim SY, Waschuk SA, Brown LS, Jung KH. Screening and characterization of proteorhodopsin color-tuning mutations in Escherichia coli with endogenous retinal synthesis. Biochim Biophys Acta. 2008;1777:504–13.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
100.von Lintig J, Vogt K. Filling the gap in vitamin A research. Molecular identification of an enzyme cleaving beta-carotene to retinal. J Biol Chem. 2000;275:11915–20.Article 

Google Scholar 
101.Korotkov KV, Sandkvist M, Hol WG. The type II secretion system: biogenesis, molecular architecture and mechanism. Nat Rev Microbiol. 2012;10:336–51.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

1.Potts, R. Hominin evolution in settings of strong environmental variability. Quat. Sci. Rev. 73, 1–13 (2013).Article 

Google Scholar 
2.Kingston, J. D. Shifting adaptive landscapes: progress and challenges in reconstructing early hominid environments. Am. J. Phys. Anthropol. 134, 20–58 (2007).Article 

Google Scholar 
3.Levin, N. E. Environment and climate of early human evolution. Annu. Rev. Earth Planet. Sci. 43, 405–429 (2015).CAS 
Article 

Google Scholar 
4.Campisano, C. J. et al. The Hominin sites and Paleolakes Drilling Project: high-resolution paleoclimate records from the East African Rift system and their implications for understanding the environmental context of hominin evolution. PaleoAnthropology 2017, 1–43 (2017).
Google Scholar 
5.Lupien, R. L. et al. Vegetation change in the Baringo Basin, East Africa across the onset of Northern Hemisphere glaciation 3.3–2.6 Ma. Palaeogeogr. Palaeoclimatol. Palaeoecol. 570, 109426 (2019).Article 

Google Scholar 
6.Yost, C. L. et al. Phytoliths, pollen, and microcharcoal from the Baringo Basin, Kenya reveal savanna dynamics during the Plio-Pleistocene transition. Palaeogeogr. Palaeoclimatol. Palaeoecol. 570, 109779 (2020).Article 

Google Scholar 
7.Reed, K. E. Paleoecological patterns at the Hadar hominin site, Afar regional state, Ethiopia. J. Hum. Evol. 54, 743–768 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Kovarovic, K., Su, D. F., Lintulaakso, K. in Methods in Paleoecology (eds Croft, D. A., Su. D. F. & Simpson, S. W.) 351–372 (Springer, 2018).9.Barr, W. A. in Methods in Paleoecology (eds Croft, D. A., Su. D. F. & Simpson, S. W.) 339–349 (Springer, 2018).10.Fortelius, M. et al. An ecometric analysis of the fossil mammal record of the Turkana Basin. Philos. Trans. R. Soc. Lond. B 371, 20150232 (2016).Article 

Google Scholar 
11.Polly, P. D. et al. History matters: ecometrics and integrative climate change biology. Proc. R. Soc. Lond. B 278, 1131–1140 (2011).
Google Scholar 
12.Wang, Y. & Cerling, T. E. A model of fossil tooth enamel and bone diagenesis: implications for stable isotope studies and paleoenvironment reconstruction. Palaeogeogr. Palaeoclimatol. Palaeoecol. 107, 281–289 (1994).Article 

Google Scholar 
13.Bocherens, H., Koch, P. L., Mariotti, A., Geraads, D. & Jaeger, J. J. Isotopic biogeochemistry (13C, 18O) of mammalian enamel from African Pleistocene hominid sites. Palaios 11, 306–318 (1996).Article 

Google Scholar 
14.Schoeninger, M. J., Reeser, H. & Hallin, K. Paleoenvironment of Australopithecus anamensis at Allia Bay, East Turkana, Kenya: evidence from mammalian herbivore enamel stable isotopes. J. Anthropol. Archaeol. 22, 200–207 (2003).Article 

Google Scholar 
15.Levin, N. E., Simpson, S. W., Quade, J., Cerling, T. E. & Frost, S. R. Herbivore enamel carbon isotopic composition and the environmental context of Ardipithecus at Gona, Ethiopia. The geology of early humans in the Horn of Africa. Geol. Soc. Am. Spec. Pap. 446, 215–234 (2008).
Google Scholar 
16.Levin, N. E., Haile-Selassie, Y., Frost, S. R. & Saylor, B. Z. Dietary change among hominins and cercopithecids in Ethiopia during the early Pliocene. Proc. Natl Acad. Sci. USA 112, 12304–12309 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
17.Kingston, J. D. in Paleontology and Geology of Laetoli: Human Evolution in Context (ed. Harrison, T.) 293–328 (Springer, 2011).18.Cerling, T. E. et al. Dietary changes of large herbivores in the Turkana Basin, Kenya from 4 to 1 Ma. Proc. Natl Acad. Sci. USA 112, 11467–11472 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.Wynn, J. G. et al. Dietary flexibility of Australopithecus afarensis in the face of paleoecological change during the middle Pliocene: faunal evidence from Hadar, Ethiopia. J. Hum. Evol. 99, 93–106 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Robinson, J. R., Rowan, J., Campisano, C. J., Wynn, J. G. & Reed, K. E. Late Pliocene environmental change during the transition from Australopithecus to Homo. Nat. Ecol. Evol. 1, 0159 (2017).Article 

Google Scholar 
21.Ambrose, S. H. & DeNiro, M. J. The isotopic ecology of East African mammals. Oecologia 69, 395–406 (1986).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Cerling, T. E. & Harris, J. M. Carbon isotope fractionation between diet and bioapatite in ungulate mammals and implications for ecological and paleoecological studies. Oecologia 120, 347–363 (1999).PubMed 
Article 
PubMed Central 

Google Scholar 
23.Sponheimer, M. et al. Diets of southern African Bovidae: stable isotope evidence. J. Mammal. 84, 471–479 (2003).Article 

Google Scholar 
24.Tieszen, L. L., Senyimba, M. M., Imbaba, S. K. & Troughton, J. H. The distribution of C3 and C4 grasses and carbon isotope discrimination along an altitudinal and moisture gradient in Kenya. Oecologia 37, 337–350 (1979).PubMed 
Article 
PubMed Central 

Google Scholar 
25.Tiezsen, L. L., Boutton, T., Tesdahl, K. & Slade, N. Fractionation and turnover of stable carbon isotopes in animal tissues: implications for the 13C analysis of diet. Oecologia 57, 32–37 (1983).Article 

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

Google Scholar 
27.Kingdon, J. et al. Mammals of Africa Vol. 1 (A&C Black, 2013).28.Kingston, J. D. & Harrison, T. Isotopic dietary reconstructions of Pliocene herbivores at Laetoli: implications for early hominin paleoecology. Palaeogeogr. Palaeoclimatol. Palaeoecol. 243, 272–306 (2007).Article 

Google Scholar 
29.Patterson, D. B. et al. Comparative isotopic evidence from East Turkana supports a dietary shift within the genus Homo. Nat. Ecol. Evol. 3, 1048–1056 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
30.Sponheimer, M. & Lee-Thorp, J. A. Using carbon isotope data of fossil bovid communities for palaeoenvironmental reconstruction: research articles: human origins research in South Africa. S. Afr. J. Sci. 99, 273–275 (2003).CAS 

Google Scholar 
31.Lee-Thorp, J. A., Sponheimer, M. & Luyt, J. Tracking changing environments using stable carbon isotopes in fossil tooth enamel: an example from the South African hominin sites. J. Hum. Evol. 53, 595–601 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Bedaso, Z., Wynn, J. G., Alemseged, Z. & Geraads, D. Paleoenvironmental reconstruction of the Asbole fauna (Busidima Formation, Afar, Ethiopia) using stable isotopes. Geobios 43, 165–177 (2010).Article 

Google Scholar 
33.Bedaso, Z. K., Wynn, J. G., Alemseged, Z. & Geraads, D. Dietary and paleoenvironmental reconstruction using stable isotopes of herbivore tooth enamel from middle Pliocene Dikika, Ethiopia: implication for Australopithecus afarensis habitat and food resources. J. Hum. Evol. 64, 21–38 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Leichliter, J. N. et al. Small mammal insectivore carbon isotopes as environmental proxies in a South African savanna ecosystem. Am. J. Phys. Anthropol. 159, 206–207 (2016).
Google Scholar 
35.Codron, J. et al. Landscape-scale feeding patterns of African elephant inferred from carbon isotope analysis of feces. Oecologia 165, 89–99 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Marston, C. G. et al. ‘Remote’ behavioural ecology: do megaherbivores consume vegetation in proportion to its presence in the landscape? PeerJ 8, e8622 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
37.Hernandez-Fernández, M. & Vrba, E. S. Plio-Pleistocene climatic change in the Turkana Basin (East Africa): evidence from large mammal faunas. J. Hum. Evol. 50, 595–626 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
38.Lintulaakso, K. & Kovarovic, K. Diet and locomotion, but not body size, differentiate mammal communities in worldwide tropical ecosystems. Palaeogeogr. Palaeoclimatol. Palaeoecol. 454, 20–29 (2016).Article 

Google Scholar 
39.Barr, W. A. Bovid locomotor functional trait distributions reflect land cover and annual precipitation in sub-Saharan Africa. Evol. Ecol. Res. 18, 253–269 (2017).
Google Scholar 
40.Eronen, J. T. et al. Precipitation and large herbivorous mammals I: estimates from present-day communities. Evol. Ecol. Res. 12, 217–233 (2010).
Google Scholar 
41.Eronen, J. T. et al. Precipitation and large herbivorous mammals II: application to fossil data. Evol. Ecol. Res. 12, 235–248 (2010).
Google Scholar 
42.Cerling, T. E. et al. Woody cover and hominin environments in the past 6 million years. Nature 476, 51–56 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.White, T. D. et al. Macrovertebrate paleontology and the Pliocene habitat of Ardipithecus ramidus. Science 326, 67–93 (2009).Article 
CAS 

Google Scholar 
44.Venter, Z. S., Cramer, M. D. & Hawkins, H. J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
45.Kohn, M. J. Carbon isotope compositions of terrestrial C3 plants as indicators of (paleo) ecology and (paleo) climate. Proc. Natl Acad. Sci. USA 107, 19691–19695 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Du, A., Robinson, J. R., Rowan, J., Lazagabaster, I. A. & Behrensmeyer, A. K. Stable carbon isotopes from paleosol carbonate and herbivore enamel document differing paleovegetation signals in the eastern African Plio-Pleistocene. Rev. Palaeobot. Palynol. 261, 41–52 (2019).Article 

Google Scholar 
47.Brown, F. H., McDougall, I. & Gathogo, P. N. in The Paleobiology of Australopithecus (eds Reed, K. E. et al.) 7–20 (Springer, 2013).48.McDougall, I. et al. New single crystal 40Ar/39Ar ages improve time scale for deposition of the Omo Group, Omo–Turkana Basin, East Africa. J. Geol. Soc. 169, 213–226 (2012).CAS 
Article 

Google Scholar 
49.Herries, A. I. et al. in The Paleobiology of Australopithecus (eds Reed, K. E., Fleagle, J. G. & Leakey, R. E.) 21–40 (Springer, 2013).50.Pickering, R. et al. U–Pb-dated flowstones restrict South African early hominin record to dry climate phases. Nature 565, 226–229 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Erena, M. G., Bekele, A. & Debella, H. J. Diet composition of forest inhabiting Cape buffalo (Syncerus caffer caffer) in western Ethiopia. Int. J. Ecol. Environ. Sci. 45, 165–178 (2019).
Google Scholar 
52.Pianka, E. R. in Theoretical Ecology. Principles and Applications (ed. May, R. M.) 114–141 (Blackwell Scientific, 1976).53.Schoener, T. W. The controversy over interspecific competition: despite spirited criticism, competition continues to occupy a major domain in ecological thought. Am. Sci. 70, 586–595 (1982).
Google Scholar 
54.Gordon, I. J. & Prins, H. H. T. in The Ecology of Browsing and Grazing (eds Gordon, I. J. & Prins, H. H. T.) 309–321 (Springer, 2008).55.O’Kane, C. A., Duffy, K. J., Page, B. R. & Macdonald, D. W. Effects of resource limitation on habitat usage by the browser guild in Hluhluwe-iMfolozi Park, South Africa. J. Trop. Ecol. 29, 39–47 (2013).Article 

Google Scholar 
56.Codron, J. et al. Taxonomic, anatomical, and spatio-temporal variations in the stable carbon and nitrogen isotopic compositions of plants from an African savanna. J. Archaeol. Sci. 32, 1757–1772 (2005).Article 

Google Scholar 
57.Codron, D., Codron, J., Lee-thorp, A. J., Sponheimer, M. & Brink, S. J. Dietary variation in impala Aepyceros melampus recorded by carbon isotope composition of feces. Acta Zool. Sin. 52, 1015–1025 (2006).CAS 

Google Scholar 
58.Uno, K. T. et al. High-resolution stable isotope profiles of modern elephant (Loxodonta africana) tusk dentin and tail hair from Kenya: implications for identifying seasonal variability in climate, ecology, and diet in ancient proboscideans. Palaeogeogr. Palaeoclimatol. Palaeoecol. 559, 109962 (2020).Article 

Google Scholar 
59.Uno, K. T., Polissar, P. J., Jackson, K. E. & deMenocal, P. B. Neogene biomarker record of vegetation change in eastern Africa. Proc. Natl Acad. Sci. USA 113, 6355–6363 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Owen-Smith, R. N. Megaherbivores: The Influence of Very Large Body Size on Ecology (Cambridge Univ. Press, 1988).61.Uno, K. T. et al. Forward and inverse methods for extracting climate and diet information from stable isotope profiles in proboscidean molars. Quat. Intern. 557, 92–109 (2020).Article 

Google Scholar 
62.White, F. The Vegetation of Africa: A Descriptive Memoir to Accompany the UNESCO/AETFAT/UNSO Vegetation Map of Africa (3 plates), 1:5,000,000 (UNESCO, 1983).63.Uno, K. T. et al. A Pleistocene palaeovegetation record from plant wax biomarkers from the Nachukui Formation, West Turkana, Kenya. Philos. Trans. R. Soc. Lond. B 371, 20150235 (2016).Article 
CAS 

Google Scholar 
64.Behrensmeyer, A. K., Kidwell, S. M. & Gastaldo, R. A. Taphonomy and paleobiology. Paleobiology 26, 103–147 (2000).Article 

Google Scholar 
65.Faith, J. T., Du, A. & Rowan, J. Addressing the effects of sampling on ecometric-based paleoenvironmental reconstructions. Palaeogeogr. Palaeoclimatol. Palaeoecol 528, 175–185 (2019).Article 

Google Scholar 
66.Shorrocks, B. & Bates, W. The Biology of African Savannahs (Oxford Univ. Press, 2015).67.Tieszen, L. L. Natural variations in the carbon isotope values of plants: implications for archaeology, ecology, and paleoecology. J. Archaeol. Sci. 18, 227–248 (1991).Article 

Google Scholar 
68.Cornwell, W. K. et al. Climate and soils together regulate photosynthetic carbon isotope discrimination within C3 plants worldwide. Glob. Ecol. Biogeogr. 27, 1056–1067 (2018).Article 

Google Scholar 
69.Luyt, J., Hare, V. J. & Sealy, J. The relationship of ungulate δ13C and environment in the temperate biome of southern Africa, and its palaeoclimatic application. Palaeogeogr. Palaeoclimatol. Palaeoecol. 514, 282–291 (2019).Article 

Google Scholar 
70.Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC and IUCN, 2020); www.protectedplanet.net71.ArcGIS Desktop Release 10 (Environmental Systems Research Institute, 2012).72.Ogutu, J. et al. Changing wildlife populations in Nairobi national park and adjoining Athi-Kaputiei plains: collapse of the migratory wildebeest. Open Conserv. Biol. J. 7, 11–26 (2013).Article 

Google Scholar 
73.Forest Atlas of the Democratic Republic of the Congo (Ministry of Environment and Sustainable Development of the Democratic Republic of the Congo and World Resources Institute, 2020); https://www.wri.org/resources/maps/forest-atlas-democratic-republic-congo74.R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.R-project.org/75.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016). More

1.Bardgett, R. D. Plant-soil interactions in a changing world. F1000 Biol. Rep. 3, 16 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Faure, D., Vereecke, D. & Leveau, J. H. Molecular communication in the rhizosphere. Plant Soil 321, 279–303 (2009).CAS 
Article 

Google Scholar 
3.de Zelicourt, A., Al-Yousif, M. & Hirt, H. Rhizosphere microbes as essential partners for plant stress tolerance. Mol. Plant 6, 242–245 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
4.Reynolds, H. L., Packer, A., Bever, J. D. & Clay, K. Grassroots ecology: plant–microbe–soil interactions as drivers of plant community structure and dynamics. Ecology 84, 2281–2291 (2003).Article 

Google Scholar 
5.Jones, P., Garcia, B., Furches, A., Tuskan, G. & Jacobson, D. Plant host-associated mechanisms for microbial selection. Front. Plant Sci. 10, 862 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
6.de Vries, F. T. et al. Changes in root‐exudate‐induced respiration reveal a novel mechanism through which drought affects ecosystem carbon cycling. N. Phytol. 224, 132–145 (2019).Article 
CAS 

Google Scholar 
7.Dodd, I. C., Zinovkina, N. Y., Safronova, V. I. & Belimov, A. A. Rhizobacterial mediation of plant hormone status. Ann. Appl. Biol. 157, 361–379 (2010).CAS 
Article 

Google Scholar 
8.Egamberdieva, D., Wirth, S. J., Alqarawi, A. A., Abd-Allah, E. F. & Hashem, A. Phytohormones and beneficial microbes: essential components for plants to balance stress and fitness. Front. Microbiol. 8, 2104 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Xu, L. & Coleman-Derr, D. Causes and consequences of a conserved bacterial root microbiome response to drought stress. Curr. Opin. Microbiol. 49, 1–6 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Naylor, D. & Coleman-Derr, D. Drought stress and root-associated bacterial communities. Front. Plant Sci. 8, 2223 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
11.Wittenmeyer, L. & Merbach, W. Plant responses to drought and phosphorus deficiency: contribution of phytohormones in root-related processes. J. Plant Nutr. Soil Sci. 168, 531–540 (2005).Article 
CAS 

Google Scholar 
12.Borghi, L., Kang, J., Ko, D., Lee, Y. & Martinoia, E. The role of ABCG-type ABC transporters in phytohormone transport. Biochem. Soc. Trans. 43, 924–930 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
13.Gargallo-Garriga, A. et al. Root exudate metabolomes change under drought and show limited capacity for recovery. Sci. Rep. 8, 1–15 (2018).CAS 
Article 

Google Scholar 
14.Hamer, U. & Marschner, B. Priming effects in different soil types induced by fructose, alanine, oxalic acid and catechol additions. Soil Biol. Biochem. 37, 445–454 (2005).CAS 
Article 

Google Scholar 
15.Mondini, C., Cayuela, M. L., Sanchez-Monedero, M. A., Roig, A. & Brookes, P. C. Soil microbial biomass activation by trace amounts of readily available substrate. Biol. Fertil. Soils 42, 542–549 (2006).Article 

Google Scholar 
16.Hu, L. et al. Root exudate metabolites drive plant-soil feedbacks on growth and defense by shaping the rhizosphere microbiota. Nat. Commun. 9, 1–13 (2018).Article 
CAS 

Google Scholar 
17.Fahad, S. et al. Potential role of phytohormones and plant growth-promoting rhizobacteria in abiotic stresses: consequences for changing environment. Environ. Sci. Pollut. Res. 22, 4907–4921 (2015).Article 

Google Scholar 
18.Speirs, J., Binney, A., Collins, M., Edwards, E. & Loveys, B. Expression of ABA synthesis and metabolism genes under different irrigation strategies and atmospheric VPDs is associated with stomatal conductance in grapevine (Vitis vinifera L. cv Cabernet Sauvignon). J. Exp. Bot. 64, 1907–1916 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
19.McAdam, S. A., Brodribb, T. J. & Ross, J. J. Shoot‐derived abscisic acid promotes root growth. Plant Cell Environ. 39, 652–659 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Ibort, P., Molina, S., Ruiz-Lozano, J. M. & Aroca, R. Molecular insights into the involvement of a never ripe receptor in the interaction between two beneficial soil bacteria and tomato plants under well-watered and drought conditions. Mol. Plant Microbe Interact. 31, 633–650 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
21.Timmusk, S. et al. Bacterial distribution in the rhizosphere of wild barley under contrasting microclimates. PLoS ONE 6, e17968 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
22.Ghosh, D., Gupta, A. & Mohapatra, S. Dynamics of endogenous hormone regulation in plants by phytohormone secreting rhizobacteria under water-stress. Symbiosis 77, 265–278 (2019).CAS 
Article 

Google Scholar 
23.Carvalhais, L. C., Dennis, P. G. & Schenk, P. M. Plant defence inducers rapidly influence the diversity of bacterial communities in a potting mix. Appl. Soil Ecol. 84, 1–5 (2014).Article 

Google Scholar 
24.Olds, C. L., Glennon, E. K. & Luckhart, S. Abscisic acid: new perspectives on an ancient universal stress signaling molecule. Microbes Infect. 20, 484–492 (2018).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Hartung, W., Sauter, A., Turner, N. C., Fillery, I. & Heilmeier, H. Abscisic acid in soils: what is its function and which factors and mechanisms influence its concentration? Plant Soil 184, 105–110 (1996).CAS 
Article 

Google Scholar 
26.Belimov, A. A. et al. Abscisic acid metabolizing rhizobacteria decrease ABA concentrations in planta and alter plant growth. Plant Physiol. Biochem. 74, 84–91 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Glick, B. R., Penrose, D. M. & Li, J. P. A model for the lowering of plant ethylene concentrations by plant growth-promoting rhizobacteria. J. Theor. Biol. 190, 63–68 (1998).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
28.Kazan, K. Diverse roles of jasmonates and ethylene in abiotic stress tolerance. Trends Plant Sci. 20, 219–229 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
29.de Ollas, C. & Dodd, I. C. Physiological impacts of ABA–JA interactions under water-limitation. Plant Mol. Biol. 91, 641–650 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
30.Carvalhais, L. C. et al. Linking jasmonic acid signaling, root exudates, and rhizosphere microbiomes. Mol. Plant Microbe Interact. 28, 1049–1058 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
31.Ngumbi, E. & Kloepper, J. Bacterial-mediated drought tolerance: current and future prospects. Appl. Soil Ecol. 105, 109–125 (2016).Article 

Google Scholar 
32.Vurukonda, S. S. K. P., Vardharajula, S., Shrivastava, M. & SkZ, A. Enhancement of drought stress tolerance in crops by plant growth promoting rhizobacteria. Microbiol. Res. 184, 13–24 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
33.Kudoyarova, G. et al. Phytohormone mediation of interactions between plants and non-symbiotic growth promoting bacteria under edaphic stresses. Front. Plant Sci. 10, 1368 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
34.Wallenstein, M. D. & Hall, E. K. A trait-based framework for predicting when and where microbial adaptation to climate change will affect ecosystem functioning. Biogeochemistry 109, 35–47 (2012).Article 

Google Scholar 
35.Martiny, J. B. et al. Microbial legacies alter decomposition in response to simulated global change. ISME J. 11, 490–499 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Grime, J. P. et al. The response of two contrasting limestone grasslands to simulated climate change. Science 289, 762–765 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
37.Fridley, J. D., Lynn, J. S., Grime, J. P. & Askew, A. P. Longer growing seasons shift grassland vegetation towards more-productive species. Nat. Clim. Change 6, 865–868 (2016).Article 

Google Scholar 
38.Sayer, E. J. et al. Links between soil microbial communities and plant traits in a species‐rich grassland under long‐term climate change. Ecol. Evol. 7, 855–862 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Trinder, S., Askew, A. P. & Whitlock, R. Climate‐driven evolutionary change in reproductive and early‐acting life‐history traits in the perennial grass Festuca ovina. J. Ecol. 108, 1398–1410 (2020).CAS 
Article 

Google Scholar 
40.Fridley, J. D., Grime, J. P., Askew, A. P., Moser, B. & Stevens, C. J. Soil heterogeneity buffers community response to climate change in species‐rich grassland. Glob. Change Biol. 17, 2002–2011 (2011).Article 

Google Scholar 
41.Schimel, J., Balser, T. C. & Wallenstein, M. Microbial stress‐response physiology and its implications for ecosystem function. Ecology 88, 1386–1394 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Kuzyakov, Y., Friedel, J. K. & Stahr, K. Review of mechanisms and quantification of priming effects. Soil Biol. Biochem. 32, 1485–1498 (2000).CAS 
Article 

Google Scholar 
43.Keiluweit, M. et al. Mineral protection of soil carbon counteracted by root exudates. Nat. Clim. Change 5, 588–595 (2015).CAS 
Article 

Google Scholar 
44.Chanclud, E. & Morel, J. B. Plant hormones: a fungal point of view. Mol. Plant Pathol. 17, 1289–1297 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
45.Sembdner, G. A. P. B. & Parthier, B. The biochemistry and the physiological and molecular actions of jasmonates. Annu. Rev. Plant Biol. 44, 569–589 (1993).CAS 
Article 

Google Scholar 
46.Eng, F. et al. Jasmonic acid biosynthesis by fungi: derivatives, first evidence on biochemical pathways and culture conditions for production. PeerJ 9, e10873 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Fuchslueger, L. et al. Drought history affects grassland plant and microbial carbon turnover during and after a subsequent drought event. J. Ecol. 104, 1453–1465 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
48.Schimel, J. P. Life in dry soils: effects of drought on soil microbial communities and processes. Annu. Rev. Ecol. Evol. Syst. 49, 409–432 (2018).Article 

Google Scholar 
49.Waring, B. G., Averill, C. & Hawkes, C. V. Differences in fungal and bacterial physiology alter soil carbon and nitrogen cycling: insights from meta-analysis and theoretical models. Ecol. Lett. 16, 887–894 (2013).PubMed 
Article 
PubMed Central 

Google Scholar 
50.Staddon, P. L. et al. Mycorrhizal fungal abundance is affected by long‐term climatic manipulations in the field. Glob. Change Biol. 9, 186–194 (2003).Article 

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

Google Scholar 
52.Belimov, A. A. et al. Rhizosphere bacteria containing ACC deaminase increase yield of plants grown in drying soil via both local and systemic hormone signalling. N. Phytol. 181, 413–423 (2009).CAS 
Article 

Google Scholar 
53.Lennon, J. T. & Jones, S. E. Microbial seed banks: the ecological and evolutionary implications of dormancy. Nat. Rev. Microbiol. 9, 119–130 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Chodak, M., Gołębiewski, M., Morawska-Płoskonka, J., Kuduk, K. & Niklińska, M. Soil chemical properties affect the reaction of forest soil bacteria to drought and rewetting stress. Ann. Microbiol. 65, 1627–1637 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Kakumanu, M. L., Ma, L. & Williams, M. A. Drought-induced soil microbial amino acid and polysaccharide change and their implications for C-N cycles in a climate change world. Sci. Rep. 9, 1–12 (2019).CAS 

Google Scholar 
56.Puertolas, J., Alcobendas, R., Alarcón, J. J. & Dodd, I. C. Long‐distance abscisic acid signalling under different vertical soil moisture gradients depends on bulk root water potential and average soil water content in the root zone. Plant Cell Environ. 36, 1465–1475 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
57.Axtell, C. A. & Beattie, G. A. Construction and characterization of a proU-gfp transcriptional fusion that measures water availability in a microbial habitat. Appl. Environ. Microbiol. 68, 4604–4612 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
58.Wesener, F. & Tietjen, B. Primed to be strong, primed to be fast: modeling benefits of microbial stress responses. FEMS Microbiol. Ecol. 95, 114 (2019).Article 
CAS 

Google Scholar 
59.Andrade‐Linares, D. R., Lehmann, A. & Rillig, M. C. Microbial stress priming: a meta‐analysis. Environ. Microbiol. 18, 1277–1288 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
60.Grime, J. P. et al. Long-term resistance to simulated climate change in an infertile grassland. Proc. Natl Acad. Sci. USA 105, 10028–10032 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
61.Giannetta, B., Plaza, C., Zaccone, C., Vischetti, C. & Rovira, P. Ecosystem type effects on the stabilization of organic matter in soils: combining size fractionation with sequential chemical extractions. Geoderma 353, 423–434 (2019).CAS 
Article 

Google Scholar 
62.Campbell, C. D., Chapman, S. J., Cameron, C. M., Davidson, M. S. & Potts, J. M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
63.Tworkoski, T., Wisniewski, M. & Artlip, T. Application of BABA and s-ABA for drought resistance in apple. J. Appl. Hortic. 13, 95–90 (2011).Article 

Google Scholar 
64.Rohwer, C. L. & Erwin, J. E. Horticultural applications of jasmonates: a review. J. Hortic. Sci. Biotechnol. 83, 283–304 (2008).CAS 
Article 

Google Scholar 
65.Creamer, R. E. et al. An inter-laboratory comparison of multi-enzyme and multiple substrate-induced respiration assays to assess method consistency in soil monitoring. Biol. Fertil. Soils 45, 623–633 (2009).CAS 
Article 

Google Scholar 
66.Stott, D. E. Recommended Soil Health Indicators and Associated Laboratory Procedures. Soil Health Technical Note No. 450-03. (U.S. Department of Agriculture, Natural Resources Conservation Service, 2019).67.Buyer, J. S. & Sasser, M. High throughput phospholipid fatty acid analysis of soils. Appl. Soil Ecol. 61, 127–130 (2012).Article 

Google Scholar 
68.Bardgett, R. D. & McAlister, E. The measurement of soil fungal: bacterial biomass ratios as an indicator of ecosystem self-regulation in temperate meadow grasslands. Biol. Fertil. Soils 29, 282–290 (1999).Article 

Google Scholar 
69.Bardgett, R. D., Hobbs, P. J. & Frostegård, Å. Changes in soil fungal: bacterial biomass ratios following reductions in the intensity of management of an upland grassland. Biol. Fertil. Soils 22, 261–264 (1996).Article 

Google Scholar 
70.Zhu, Z. et al. Fate of rice shoot and root residues, rhizodeposits, and microbial assimilated carbon in paddy soil-part 2: turnover and microbial utilization. Plant Soil 416, 243–257 (2017).CAS 
Article 

Google Scholar 
71.R Core Team. R: A Language and Environment for Statistical Computing, https://www.R-project.org/ (R Foundation for Statistical Computing, 2019).72.Bates, D. M., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2014).
Google Scholar 
73.Cohen, J. The effect size index: d. Stat. Power Anal. Behav. Sci. 2, 284–288 (1988).
Google Scholar 
74.Anderson, T. H. & Domsch, A. K. The metabolic quotient for CO2 (qCO2) as a specific activity parameter to assess the effects of environmental conditions, such as pH, on the microbial biomass of forest soils. Soil Biol. Biochem. 25, 393–395 (1993).Article 

Google Scholar 
75.Pinheiro, J.C., Bates, D.M. Mixed-Effects Models in S and S-PLUS (Springer, 2000).76.Kuznetsova, A., Brockhoff, P. B. & Christensen, R. H. B. lmerTest package: tests in linear mixed effects models. J. Stat. Softw. 82, 13 (2017).Article 

Google Scholar 
77.Sayer, E. J. et al. Data from: Adaptation to chronic drought modifies soil microbial community responses to phytohormones. figshare https://doi.org/10.6084/m9.figshare.14130065 (2021). More

1.Medic, G., Wille, M. & Hemels, M. Short- and long-term health consequences of sleep disruption. Nat. Sci. Sleep 9, 151–161 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
2.Randazzo, A. C., Muehlbach, M. J., Schweitzer, P. K. & Walsh, J. K. Cognitive function following acute sleep restriction in children ages 10–14. Sleep 21, 861–868 (1998).CAS 
PubMed 
PubMed Central 

Google Scholar 
3.Stickgold, R. Sleep-dependent memory consolidation. Nature 437, 1272–1278 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Tononi, G. & Cirelli, C. Sleep and the price of plasticity: From synaptic and cellular homeostasis to memory consolidation and integration. Neuron 81, 12–34 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Marshall, L. & Born, J. The contribution of sleep to hippocampus-dependent memory consolidation. Trends Cogn. Sci. 11, 442–450 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Smith, C. Sleep states and memory processes in humans: Procedural versus declarative memory systems. Sleep Med. Rev. 5, 491–506 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
7.Johnston, T. D. In Selective Costs and Benefits in the Evolution of Learning. in Advances in the Study of Behavior (eds. Rosenblatt, J. S. et al.) 12, 65–106 (Academic Press, 1982).8.Hendricks, J. C. et al. Rest in Drosophila is a sleep-like state. Neuron 25, 129–138 (2000).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Campbell, S. S. & Tobler, I. Animal sleep: A review of sleep duration across phylogeny. Neurosci. Biobehav. Rev. 8, 269–300 (1984).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Shaw, P. J. Correlates of sleep and waking in Drosophila melanogaster. Science (80-). 287, 1834–1837 (2000).ADS 
CAS 
Article 

Google Scholar 
11.Hamblen, M. et al. Germ-line transformation involving DNA from the period locus in Drosophila melanogaster: Overlapping genomic fragments that restore circadian and ultradian rhythmicity to per 0 and per—mutants. J. Neurogenet. 3, 249–291 (1986).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Kirszenblat, L. & van Swinderen, B. Sleep in Drosophila. In Handbook of Sleep Research, Vol. 30 (ed. Dringenberg, H. C.) 333–347 (Elsevier, 2019).13.Ly, S., Pack, A. I. & Naidoo, N. The neurobiological basis of sleep: Insights from Drosophila. Neurosci. Biobehav. Rev. 87, 67–86 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
14.Helfrich-Förster, C. Sleep in insects. Annu. Rev. Entomol. 63, 69–86 (2018).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
15.Le Glou, E., Seugnet, L., Shaw, P. J., Preat, T. & Goguel, V. Circadian modulation of consolidated memory retrieval following sleep deprivation in Drosophila. Sleep 35, 1377–1384 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
16.Li, X., Yu, F. & Guo, A. Sleep deprivation specifically impairs short-term olfactory memory in Drosophila. Sleep 32, 1417–1424 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
17.Rihel, J. & Bendor, D. Flies sleep on it, or Fuhgeddaboudit!. Cell 161, 1498–1500 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Geissmann, Q., Beckwith, E. J. & Gilestro, G. F. Most sleep does not serve a vital function: Evidence from Drosophila melanogaster. Sci. Adv. 5, eaau8253 (2019).Article 
CAS 

Google Scholar 
19.Tougeron, K. & Abram, P. K. An ecological perspective on sleep disruption. Am. Nat. 190, E55–E66 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
20.Aulsebrook, A. E., Jones, T. M., Rattenborg, N. C., Roth, T. C. & Lesku, J. A. Sleep ecophysiology: Integrating neuroscience and ecology. Trends Ecol. Evol. 31, 590–599 (2016).PubMed 
Article 
PubMed Central 

Google Scholar 
21.Markow, T. A. Host use and host shifts in Drosophila. Curr. Opin. Insect Sci. 31, 139–145 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Badel, L., Ohta, K., Tsuchimoto, Y. & Kazama, H. Decoding of context-dependent olfactory behavior in Drosophila. Neuron 91, 155–167 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Knaden, M., Strutz, A., Ahsan, J., Sachse, S. & Hansson, B. S. Spatial representation of odorant valence in an insect brain. Cell Rep. 1, 392–399 (2012).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Hopkins, A. A discussion of C.G. Hewitt’s paper on ‘Insect Behavior’. J. Econ. Entomol. 10, 92–93 (1917).
Google Scholar 
25.Davis, J. M. & Stamps, J. A. The effect of natal experience on habitat preferences. Trends Ecol. Evol. 19, 411–416 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
26.Barron, A. B. The life and death of Hopkins’ host selection principle. J. Insect Behav. 14, 725–737 (2001).Article 

Google Scholar 
27.van Emden, H. F. et al. Plant chemistry and aphid parasitoids (Hymenoptera: Braconidae): Imprinting and memory. Eur. J. Entomol. 105, 477–483 (2008).Article 

Google Scholar 
28.Liu, S. S., Li, Y. H., Liu, Y. Q. & Zalucki, M. P. Experience-induced preference for oviposition repellents derived from a non-host plant by a specialist herbivore. Ecol. Lett. 8, 722–729 (2005).Article 

Google Scholar 
29.Hamilton, C. E., Beresford, D. V. & Sutcliffe, J. F. Effects of natal habitat odour, reinforced by adult experience, on choice of oviposition site in the mosquito Aedes aegypti. Med. Vet. Entomol. 25, 428–435 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Turlings, T. C. L., Wackers, F. L., Vet, L. E. M., Lewis, W. J. & Tumlinson, J. H. Learning of Host-Finding Cues by Hymenopterous parasitoids. In Insect Learning (eds. Papaj, D. R. & Lewis, W. J.) 51–78 (Springer US, 1993). https://doi.org/10.1007/978-1-4615-2814-2_331.Jaenike, J. Induction of host preference in Drosophila melanogaster. Oecologia 58, 320–325 (1983).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
32.Takemoto, H., Powell, W., Pickett, J., Kainoh, Y. & Takabayashi, J. Two-step learning involved in acquiring olfactory preferences for plant volatiles by parasitic wasps. Anim. Behav. 83, 1491–1496 (2012).Article 

Google Scholar 
33.Andretic, R. & Shaw, P. J. Essentials of sleep recordings in Drosophila: Moving beyond sleep time. Methods Enzymol. 393, 759–772 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
34.Garbe, D. S. et al. Context-specific comparison of sleep acquisition systems in Drosophila. Biol. Open 4, 1558–1568 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
35.Faraway, J. J. Extending the Linear Model with R (CRC Press, 2016). https://doi.org/10.1201/b21296.Book 
MATH 

Google Scholar 
36.Ho, K. S. & Sehgal, A. Drosophila melanogaster: An insect model for fundamental studies of sleep. Methods Enzymol. 393, 1834–1837 (2005).
Google Scholar 
37.Greenspan, R. J., Tononi, G., Cirelli, C. & Shaw, P. J. Sleep and the fruit fly. Trends Neurosci. 24, 142–145 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Killgore, W. D. S. Sleep deprivation and behavioral risk-taking. In Modulation of Sleep by Obesity, Diabetes, Age, and Diet 279–287 (Elsevier, 2015). https://doi.org/10.1016/B978-0-12-420168-2.00030-2.39.Revadi, S. et al. Olfactory responses of Drosophila suzukii females to host plant volatiles. Physiol. Entomol. 40, 54–64 (2015).CAS 
Article 

Google Scholar 
40.Cirelli, C. & Tononi, G. Is sleep essential?. PLoS Biol. 6, 1605–1611 (2008).CAS 
Article 

Google Scholar 
41.Bateson, M., Desire, S., Gartside, S. E. & Wright, G. A. Agitated honeybees exhibit pessimistic cognitive biases. Curr. Biol. 21, 1070–1073 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Wilkin, M. M., Waters, P., McCormick, C. M. & Menard, J. L. Intermittent physical stress during early- and mid-adolescence differentially alters rats’ anxiety- and depression-like behaviors in adulthood. Behav. Neurosci. 126, 344–360 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
43.Chaumet, G. et al. Confinement and sleep deprivation effects on propensity to take risks. Aviat. Space. Environ. Med. 80, 73–80 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
44.Killgore, W. D. S. Effects of sleep deprivation and morningness-eveningness traits on risk-taking. Psychol. Rep. 100, 613–626 (2007).PubMed 
Article 
PubMed Central 

Google Scholar 
45.Killgore, W. D. S. et al. Restoration of risk-propensity during sleep deprivation: Caffeine, dextroamphetamine, and modafinil. Aviat. Space. Environ. Med. 79, 867–874 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Tversky, A. & Kahneman, D. Judgment under uncertainty: Heuristics and biases. Science (80-). 185, 1124–1131 (1974).ADS 
CAS 
Article 

Google Scholar 
47.Spieth, H. T. Courtship behavior in Drosophila. Annu. Rev. Entomol. 19, 385–405 (1974).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
48.Bartelt, R. J., Schaner, A. M. & Jackson, L. L. cis-Vaccenyl acetate as an aggregation pheromone in Drosophila melanogaster. J. Chem. Ecol. 11, 1747–1756 (1985).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Cazalé-Debat, L., Houot, B., Farine, J. P., Everaerts, C. & Ferveur, J. F. Flying Drosophila show sex-specific attraction to fly-labelled food. Sci. Rep. 9, 1–13 (2019).Article 
CAS 

Google Scholar 
50.Malek, H. L. & Long, T. A. F. On the use of private versus social information in oviposition site choice decisions by Drosophila melanogaster females. Behav. Ecol. 31, 739–749 (2020).Article 

Google Scholar 
51.Inoue, I. et al. Impaired locomotor activity and exploratory behavior in mice lacking histamine H1 receptors. Proc. Natl. Acad. Sci. U. S. A. 93, 13316–13320 (1996).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
52.Daffner, K. R., Mesulam, M.-M., Cohen, L. G. & Scinto, L. F. M. Mechanisms underlying diminished novelty-seeking behavior in patients with probable Alzheimer’s disease. Neuropsychiatry Neuropsychol. Behav. Neurol. 12, 58–66 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Lee, A. C. H., Rahman, S., Hodges, J. R., Sahakian, B. J. & Graham, K. S. Associative and recognition memory for novel objects in dementia: Implications for diagnosis. Eur. J. Neurosci. 18, 1660–1670 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
54.Ju, Y.-E.S., Lucey, B. P. & Holtzman, D. M. Sleep and Alzheimer disease pathology—A bidirectional relationship. Nat. Rev. Neurol. 10, 115–119 (2014).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
55.Tabuchi, M. et al. Sleep interacts with aβ to modulate intrinsic neuronal excitability. Curr. Biol. 25, 702–712 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Dissel, S. et al. Enhanced sleep reverses memory deficits and underlying pathology in drosophila models of Alzheimer’s disease. Neurobiol. Sleep Circadian Rhythm. 2, 15–26 (2017).Article 

Google Scholar 
57.Takano-Shimizu-Kouno, T. KYOTO Stock Center—Department of Drosophila Genomics and Genetic Resources (Kyoto Institute of Technology, 2015).58.Shaw, P. J., Tortoni, G., Greenspan, R. J. & Robinson, D. F. Stress response genes protect against lethal effects of sleep deprivation in Drosophila. Nature 417, 287–291 (2002).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
59.https://www.arduino.cc/. Accessed 6 Jan 202160.https://processing.org/. Accessed 6 Jan 2021 More

1.Yadav, G. S. et al. Energy budget and carbon footprint in a no-till and mulch based rice–mustard cropping system. J. Clean. Prod. 191, 144–157 (2018).Article 

Google Scholar 
2.Fleming-Muñoz, D. A., Preston, K. & Arratia-Solar, A. Value and impact of publicly funded climate change agricultural mitigation research: Insights from New Zealand. J. Clean. Prod. 248, 119249 (2020).Article 

Google Scholar 
3.IPCC. Climate Change 2014: Mitigation of Climate Change (Cambridge University Press, 2014).
Google Scholar 
4.Wang, Z. B. et al. Lowering carbon footprint of winter wheat by improving management practices in North China Plain. J. Clean. Prod. 112, 149–157 (2016).CAS 
Article 

Google Scholar 
5.Grassini, P. & Cassman, K. G. High-yield maize with large net energy yield and small global warming intensity. Proc. Natl. Acad. Sci. 109, 1074–1079 (2012).ADS 
CAS 
PubMed 
Article 

Google Scholar 
6.Gao, B. et al. Chinese cropping systems are a net source of greenhouse gases despite soil carbon sequestration. Glob. Change Biol. 24, 5590–5606 (2018).Article 

Google Scholar 
7.Xue, J. F. et al. Carbon footprint of dryland winter wheat under film mulching during summer-fallow season and sowing method on the Loess Plateau. Ecol. Indic. 95, 12–20 (2018).CAS 
Article 

Google Scholar 
8.Yuan, S., Peng, S. B., Wang, D. & Man, J. G. Evaluation of the energy budget and energy use efficiency in wheat production under various crop management practices in China. Energy 160, 184–191 (2018).Article 

Google Scholar 
9.Qi, J. Y. et al. Response of carbon footprint of spring maize production to cultivation patterns in the Loess Plateau, China. J. Clean. Prod. 187, 525–536 (2018).CAS 
Article 

Google Scholar 
10.Lu, X. L. & Liao, Y. C. Effect of tillage practices on net carbon flux and economic parameters from farmland on the Loess Plateau in China. J. Clean. Prod. 162, 1617–1624 (2017).CAS 
Article 

Google Scholar 
11.Tan, Y. C., Wu, D., Bol, R., Wu, W. L. & Meng, F. Q. Conservation farming practices in winter wheat–summer maize cropping reduce GHG emissions and maintain high yields. Agric. Ecosyst. Environ. 272, 266–275 (2019).CAS 
Article 

Google Scholar 
12.Lal, R. Carbon emission from farm operations. Environ. Int. 30, 981–990 (2004).CAS 
PubMed 
Article 

Google Scholar 
13.Wang, X. L. et al. Emergy analysis of grain production systems on large-scale farms in the North China Plain based on LCA. Agric. Syst. 128, 66–78 (2014).Article 

Google Scholar 
14.Chen, X. Z. et al. Environmental impact assessment of water-saving irrigation systems across 60 irrigation construction projects in northern China. J. Clean. Prod. 245, 118883 (2020).Article 

Google Scholar 
15.Racette, K., Zurweller, B., Tillman, B. & Rowland, D. Transgenerational stress memory of water deficit in peanut production. Field Crop. Res. 248, 107712 (2020).Article 

Google Scholar 
16.Xie, J. H. et al. Subsoiling increases grain yield, water use efficiency, and economic return of maize under a fully mulched ridge-furrow system in a semiarid environment in China. Soil. Till. Res. 199, 104584 (2020).Article 

Google Scholar 
17.Li, R., Hou, X. Q., Jia, Z. K. & Han, Q. F. Soil environment and maize productivity in semi-humid regions prone to drought of Weibei Highland are improved by ridge-and-furrow tillage with mulching. Soil. Till. Res. 196, 104476 (2020).Article 

Google Scholar 
18.Zhang, X. D. et al. Optimizing fertilization under ridge-furrow rainfall harvesting system to improve foxtail millet yield and water use in a semiarid region, China. Agric. Water Manag. 227, 105852 (2020).Article 

Google Scholar 
19.Nishimura, S., Komada, M., Takebe, M., Yonemura, S. & Kato, N. Nitrous oxide evolved from soil covered with plastic mulch film in horticultural field. Biol. Fertil. Soils 48, 787–795 (2012).CAS 
Article 

Google Scholar 
20.Xiong, L., Liang, C., Ma, B., Shah, F. & Wu, W. Carbon footprint and yield performance assessment under plastic film mulching for winter wheat production. J. Clean. Prod. 270, 122468 (2020).CAS 
Article 

Google Scholar 
21.Zhang, F., Zhang, W. J., Qi, J. G. & Li, F. M. A regional evaluation of plastic film mulching for improving crop yields on the Loess Plateau of China. Agric. Forest Meteorol. 248, 458–468 (2018).ADS 
Article 

Google Scholar 
22.Peng, X. Y., Wu, X. H., Wu, F. Q., Wang, X. Q. & Tong, X. G. Life cycle assessment of winter wheat-summer maize rotation system in Guanzhong region of shaanxi province. J. Agro-Environ. Sci. 34, 809–816 (2015).CAS 

Google Scholar 
23.Li, C. J. et al. Ridge-furrow with plastic film mulching practice improves maize productivity and resource use efficiency under the wheat-maize double-cropping system in dry semi-humid areas. Field Crop. Res. 203, 201–211 (2017).Article 

Google Scholar 
24.Tang, J. J., Folmer, H. & Xue, J. H. Technical and allocative efficiency of irrigation water use in the Guanzhong Plain. China. Food Policy 50, 43–52 (2015).Article 

Google Scholar 
25.Liu, Y., Zhang, X. L., Xi, L. Y., Liao, Y. C. & Han, J. Ridge-furrow planting promotes wheat grain yield and water productivity in the irrigated sub-humid region of China. Agric. Water Manag. 231, 105935 (2020).Article 

Google Scholar 
26.Li, Y. Z. et al. Combined ditch buried straw return technology in a ridge–furrow plastic film mulch system: Implications for crop yield and soil organic matter dynamics. Soil. Till. Res. 199, 104596 (2020).Article 

Google Scholar 
27.Wart, J. V., Kersebaum, K. C., Peng, S. B., Maribeth, M. & Cassman, K. G. Estimating crop yield potential at regional to national scales. Field Crops Res. 143, 34–43 (2013).Article 

Google Scholar 
28.Hu, Y. J. et al. Exploring optimal soil mulching for the wheat-maize cropping system in sub-humid drought-prone regions in China. Agric. Water Manag. 219, 59–71 (2019).Article 

Google Scholar 
29.Cui, J. X. et al. Integrated assessment of economic and environmental consequences of shifting cropping system from wheat-maize to monocropped maize in the North China Plain. J. Clean. Prod. 193, 524–532 (2018).Article 

Google Scholar 
30.Yin, W. et al. Wheat-maize intercropping with reduced tillage and straw retention: A step towards enhancing economic and environmental benefits in arid areas. Front. Plant Sci. 9, 1328 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
31.Zheng, J. F. et al. Biochar compound fertilizer increases nitrogen productivity and economic benefits but decreases carbon emission of maize production. Agric. Ecosyst. Environ. 241, 70–78 (2017).CAS 
Article 

Google Scholar 
32.Liang, L. et al. A multi-indicator assessment of peri-urban agricultural production in Beijing, China. Ecol. Indic. 97, 350–362 (2019).CAS 
Article 

Google Scholar 
33.Moitzi, G., Neugschwandtner, R. W., Kaul, H. P. & Wagentristl, H. Energy efficiency of winter wheat in a long-term tillage experiment under Pannonian climate conditions. Eur. J. Agron. 103, 24–31 (2019).Article 

Google Scholar 
34.Nasseri, A. Energy use and economic analysis for wheat production by conservation tillage along with sprinkler irrigation. Sci. Total Environ. 648, 450–459 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
35.Sahabi, H., Feizi, H. & Karbasi, A. Is saffron more energy and economic efficient than wheat in crop rotation systems in northeast Iran?. Sustain. Prod. Consum. 5, 29–35 (2016).Article 

Google Scholar 
36.Mondani, F., Aleagha, S., Khoramivafa, M. & Ghobadi, R. Evaluation of greenhouse gases emission based on energy consumption in wheat agroecosystems. Energy Rep. 3, 37–45 (2017).Article 

Google Scholar 
37.Bertocco, M., Basso, B., Sartori, L. & Martin, E. C. Evaluating energy efficiency of site-specific tillage in maize in NE Italy. Bioresour. Technol. 99, 6957–6965 (2008).CAS 
PubMed 
Article 

Google Scholar 
38.Amaducci, S., Colauzzi, M., Battini, F., Fracasso, A. & Perego, A. Effect of irrigation and nitrogen fertilization on the production of biogas from maize and sorghum in a water limited environment. Eur. J. Agron. 76, 54–65 (2016).ADS 
CAS 
Article 

Google Scholar 
39.Qiu, G. Y., Zhang, X., Yu, X. & Zou, Z. The increasing effects in energy and GHG emission caused by groundwater level declines in North China’s main food production plain. Agr. Water Manag. 203, 138–150 (2018).Article 

Google Scholar 
40.Arvidsson, J. Energy use efficiency in different tillage systems for winter wheat on a clay and silt loam in Sweden. Eur. J. Agron. 33, 250–256 (2010).Article 

Google Scholar 
41.Singh, R. J. et al. Energy budgeting and emergy synthesis of rainfed maize–wheat rotation system with different soil amendment applications. Ecol. Indic. 61, 753–765 (2016).CAS 
Article 

Google Scholar 
42.Zhang, Y. et al. Effects of different fertilizer strategies on soil water utilization and maize yield in the ridge and furrow rainfall harvesting system in semiarid regions of China. Agric. Water Manag. 208, 414–421 (2018).Article 

Google Scholar 
43.Cheng, K. et al. Carbon footprint of China’s crop production–An estimation using agro-statistics data over 1993–2007. Agr. Ecosyst. Environ. 142, 231–237 (2011).Article 

Google Scholar 
44.Hillier, J. et al. The carbon footprints of food crop production. Int. J. Agric. Sustain. 7, 107–118 (2009).Article 

Google Scholar 
45.Su, B., Su, Z. & Shangguan, Z. Trade-off analyses of plant biomass and soil moisture relations on the Loess Plateau. CATENA 197, 104946 (2020).Article 

Google Scholar 
46.Prata, J. C. et al. Solutions and integrated strategies for the control and mitigation of plastic and microplastic pollution. Int. J. Environ. Res. Public Health 16, 2411 (2019).PubMed Central 
Article 
PubMed 

Google Scholar 
47.Sardon, H. & Dove, A. P. Plastics recycling with a difference. Science 360, 380–381 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
48.Qin, W., Hu, C. & Oenema, O. Soil mulching significantly enhances yields and water and nitrogen use efficiencies of maize and wheat: A metaeanalysis. Sci. Rep. 5, 16210 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
49.Sun, M. et al. Maize and rice double cropping benefits carbon footprint and soil carbon budget in paddy field. Field Crops Res. 243, 107620 (2019).Article 

Google Scholar 
50.Choudhary, M. et al. Energy budgeting and carbon footprint of pearl millet e mustard cropping system under conventional and conservation agriculture in rainfed semi-arid agro-ecosystem. Energy 141, 1052–1058 (2017).Article 

Google Scholar 
51.Bai, J. et al. Straw returning and one-time application of a mixture of controlled release and solid granular urea to reduce carbon footprint of plastic film mulching spring maize. J. Clean. Prod. 280, 124478 (2021).CAS 
Article 

Google Scholar 
52.Li, C. J. et al. Towards the highly effective use of precipitation by ridge-furrow with plastic film mulching instead of relying on irrigation resources in a dry semi-humid area. Field Crops Res. 188, 62–73 (2016).ADS 
Article 

Google Scholar 
53.Reisinger, A., Ledgard, S. F. & Falconer, S. J. Sensitivity of the carbon footprint of New Zealand milk to greenhouse gas metrics. Ecol. Indic. 81, 74–82 (2017).CAS 
Article 

Google Scholar 
54.Chen, X. et al. Carbon footprint of a typical pomelo production region in China basedon farm survey data. J. Clean. Prod. 277, 124041 (2020).CAS 
Article 

Google Scholar 
55.Pratibha, G. et al. Impact of conservation agriculture practices on energy use efficiency and global warming potential in rainfed pigeonpea–castor systems. Eur. J. Agron. 66, 30–40 (2015).Article 

Google Scholar 
56.Wang, C., Li, X., Gong, T. & Zhang, H. Life cycle assessment of wheat-maize rotation system emphasizing high crop yield and high resource use efficiency in Quzhou County. J. Clean. Prod. 68, 56–63 (2014).CAS 
Article 

Google Scholar 
57.Li, S. et al. Effect of straw management on carbon sequestration and grain production in a maize–wheat cropping system in Anthrosol of the Guanzhong Plain. Soil Till. Res. 157, 43–51 (2016).Article 

Google Scholar 
58.Kong, A. Y. Y., Six, J., Bryant, D. C., Denison, R. F. & van Kessel, C. The relationship between carbon input, aggregation, and soil organic carbon stabilization in sustainable cropping systems. Soil Sci. Soc. Am. J. 69, 1078–1085 (2005).ADS 
CAS 
Article 

Google Scholar 
59.Zhu, Y. C. et al. Large-scale farming operations are win-win for grain production, soil carbon storage and mitigation of greenhouse gases. J. Clean. Prod. 172, 2143–2152 (2018).Article 

Google Scholar 
60.Wang, Z. B. et al. Comparison of greenhouse gas emissions of chemical fertilizer types in China’s crop production. J. Clean. Prod. 141, 1267–1274 (2017).CAS 
Article 

Google Scholar  More

1.Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
2.de Groot, R. et al. Global estimates of the value of ecosystems and their services in monetary units. Ecosyst. Serv. 1, 50–61 (2012).Article 

Google Scholar 
3.Exton, D. A. et al. Artisanal fish fences pose broad and unexpected threats to the tropical coastal seascape. Nat. Commun. 10, 2100 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Pandolfi, J. M. et al. Global trajectories of the long-term decline of coral reef ecosystems. Science (80-) 301, 955–958 (2003).ADS 
CAS 
Article 

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

Google Scholar 
6.Roff, G. & Mumby, P. J. Global disparity in the resilience of coral reefs. Trends Ecol. Evol. 27, 404–413 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
7.Jackson, J.B.C., Donovan, M.K., Cramer, K.L. and Lam, V.V. Status and trends of Caribbean coral reefs. Global Coral Reef Monitoring
Network, IUCN, Gland, Switzerland, pp.1970-2012. (2014).8.Alvarez-Filip, L., Dulvy, N. K., Gill, J. A., Côté, I. M. & Watkinson, A. R. Flattening of Caribbean coral reefs: region-wide declines in architectural complexity. Proc. R. Soc. B Biol. Sci. 276, 3019–3025 (2009).Article 

Google Scholar 
9.Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

Google Scholar 
10.Sasaki, T., Furukawa, T., Iwasaki, Y., Seto, M. & Mori, A. S. Perspectives for ecosystem management based on ecosystem resilience and ecological thresholds against multiple and stochastic disturbances. Ecol. Ind. 57, 395–408 (2015).Article 

Google Scholar 
11.McCann, K. S. The diversity-stability debate. Nature 405, 228–233 (2000).CAS 
PubMed 
Article 

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

Google Scholar 
13.Mumby, P. J., Hastings, A. & Edwards, H. J. Thresholds and the resilience of Caribbean coral reefs. Nature 450, 98–101 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
14.Hughes, T. P., Graham, N. A. J., Jackson, J. B. C., Mumby, P. J. & Steneck, R. S. Rising to the challenge of sustaining coral reef resilience. Trends Ecol. Evol. 25, 633–642 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
15.Solandt, J. L. & Campbell, A. C. Macroalgal feeding characteristics of the sea urchin Diadema antillarum Philippi at Discovery Bay, Jamaica. Caribb. J. Sci. 37, 227–238 (2001).
Google Scholar 
16.Chiappone, M., Rutten, L. M., Miller, S. L. & Swanson, D. W. Recent trends (1999–2011) in population density and size of the echinoid Diadema antillarum in the Florida Keys. Florida Sci. 76, 23–35 (2013).
Google Scholar 
17.Lessios, H. A. The great Diadema antillarum die-off: 30 years later. Annu. Rev. Mar. Sci. 8, 267–283 (2016).ADS 
CAS 
Article 

Google Scholar 
18.Bruno, J. F., Sweatman, H., Precht, W. F., Selig, E. R. & Schutte, V. G. W. Assessing evidence of phase shifts from coral to macroalgal dominance on coral reefs. Ecol. Soc. Am. 90, 1478–1484 (2009).
Google Scholar 
19Miller, M. W., Szmant, A. M. & Precht, W. F. Lessons learned from experimental key-species restoration. In Coral Reef Restoration Handbook, 219–234 (ed. Precht, W. F.) (Taylor & Francis, 2006).
Google Scholar 
20.Mumby, P. J., Hedley, J. D., Zychaluk, K., Harborne, A. R. & Blackwell, P. G. Revisiting the catastrophic die-off of the urchin Diadema antillarum on Caribbean coral reefs: fresh insights on resilience from a simulation model. Ecol. Modell. 196, 131–148 (2006).Article 

Google Scholar 
21.Myhre, S. & Acevedo-Gutiérrez, A. Recovery of sea urchin Diadema antillarum populations is correlated to increased coral and reduced macroalgal cover. Mar. Ecol. Prog. Ser. 329, 205–210 (2007).ADS 
Article 

Google Scholar 
22.Carpenter, R. C. Predator and population density control of homing behavior in the Caribbean echinoid Diadema antillarum. Mar. Biol. 82, 101–108 (1984).Article 

Google Scholar 
23.Edmunds, P. J. & Carpenter, R. C. Recovery of Diadema antillarum reduces macroalgal cover and increases abundance of juvenile corals on a Caribbean reef. Proc. Natl. Acad. Sci. U. S. A. 98, 5067–5071 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Wanders, J. B. W. The role of benthic algae in the shallow reef of Curaçao (Netherlands Antilles) III: the significance of grazing. Aquat. Bot. 3, 357–390 (1977).Article 

Google Scholar 
25.Bak, R. P. M., Carpay, M. J. E. & de Ruyter van Steveninck, E. D. Densities of the sea urchin Diadema antillarum before and after mass mortalities on the coral reefs of Curacao. Mar. Ecol. Prog. Ser. 17, 105–108 (1984).ADS 
Article 

Google Scholar 
26.Levitan, D. R. Algal-urchin biomass responses following mass mortality of Diadema antillarum Philippi at Saint John, U.S. Virgin Islands. J. Exp. Mar. Biol. Ecol. 119, 167–178 (1988).Article 

Google Scholar 
27.Chiappone, M., Rutten, L., Swanson, D. & Miller, S. Population status of the urchin Diadema antillarum in the Florida Keys 25 years after the Caribbean mass mortality. In Proceedings of 11th International Coral Reef Symposium 706–710 (2008).28.Bodmer, M. D. V., Rogers, A., Speight, M. R., Lubbock, N. & Exton, D. A. Using an isolated population boom to explore barriers to recovery in the keystone Caribbean coral reef herbivore Diadema antillarum. Coral Reefs 34, 1011–1021 (2015).ADS 
Article 

Google Scholar 
29.Lessios, H. A., Robertson, D. R. & Cubit, J. D. Spread of Diadema mass mortality through the Caribbean. Science (80-) 226, 335–337 (1984).ADS 
CAS 
Article 

Google Scholar 
30.Liddell, W. D. & Ohlhorst, S. L. Changes in benthic community composition following the mass mortality of Diadema at Jamaica. J. Exp. Mar. Biol. Ecol. 95, 271–278 (1986).Article 

Google Scholar 
31.Betchel, J. D., Gayle, P. & Kaufman, L. The return of Diadema antillarum to Discovery Bay: patterns of distribution and abundance. In Proceedings of 10th International Coral Reef Symposium 367–375 (2006).32.Robertson, D. R. Increases in surgeonfish populations after mass mortality of the sea urchin Diadema antillarum in Panamá indicate food limitation. Mar. Biol. 111, 437–444 (1991).Article 

Google Scholar 
33.Lessios, H. A. Diadema antillarum populations in Panama 20 years following mass mortality. Coral Reefs 24, 125–127 (2005).Article 

Google Scholar 
34.Hunte, W. & Younglao, D. Recruitment and population recovery of Diadema antillarum (Echinodermata; Echinoidea) in Barbados. Mar. Ecol. Prog. Ser. 45, 109–119 (1988).ADS 
Article 

Google Scholar 
35.Noriega, N., Pauls, S. M. & del Mónaco, C. Abundancia de Diadema antillarum (Echinodermata: Echinoidea) en las costas de Venezuela. Rev. Biol. Trop. 54, 793–802 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Debrot, A. O. & Nagelkerken, I. Recovery of the long-spined sea urchin Diadema antillarum in Curacao (Netherlands Antilles) linked to lagoonal and wave sheltered shallow rocky habitats. Bull. Mar. Sci. 72, 415–424 (2006).
Google Scholar 
37.Vermeij, M. J. A., Debrot, A. O., van der Hal, N., Bakker, J. & Bak, R. P. M. Increased recruitment rates indicate recovering populations of the sea urchin Diadema antillarum on Curaçao. Bull. Mar. Sci. 86, 719–725 (2010).
Google Scholar 
38.Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science (80-) 321, 560–563 (2008).ADS 
CAS 
Article 

Google Scholar 
39.Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science (80-) 301, 958–960 (2003).ADS 
CAS 
Article 

Google Scholar 
40.Pennington, J. T. The ecology of fertilization of echinoid eggs: the consequences of sperm dilution, adult aggregation, and synchronous spawning. Biol. Bull. 169, 417–430 (1985).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Levitan, D. R. Influence of body size and population density on fertilization success and reproductive output in a free-spawning invertebrate. Biol. Bull. 181, 261–268 (1991).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Levitan, D. R., Edmunds, P. J. & Levitan, K. E. What makes a species common? No evidence of density-dependent recruitment or mortality of the sea urchin Diadema antillarum after the 1983–1984 mass mortality. Oecologia 175, 117–128 (2014).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
43.Lacey, E. A., Fourqurean, J. W. & Collado-Vides, L. Increased algal dominance despite presence of Diadema antillarum populations on a Caribbean coral reef. Bull. Mar. Sci. 89, 603–620 (2013).Article 

Google Scholar 
44.Dumas, P., Kulbicki, M., Chifflet, S., Fichez, R. & Ferraris, J. Environmental factors influencing urchin spatial distributions on disturbed coral reefs (New Caledonia, South Pacific). J. Exp. Mar. Biol. Ecol. 344, 88–100 (2007).Article 

Google Scholar 
45.Rogers, A. & Lorenzen, K. Does slow and variable recovery of Diadema antillarum on Caribbean fore-reefs reflect density-dependent habitat selection? Front. Mar. Sci. 3, 63 (2016).Article 

Google Scholar 
46.Alvarado, J. J., Cortés, J., Guzman, H. & Reyes-Bonilla, H. Density, size, and biomass of Diadema mexicanum (Echinoidea) in Eastern Tropical Pacific coral reefs. Aquat. Biol. 24, 151–161 (2016).Article 

Google Scholar 
47.Ogden, J. C. & Carpenter, R. C. Long-spined black sea urchin. Biol. Rep. 82, 1–17 (1987).
Google Scholar 
48.Bodmer, M. D. V. et al. Interacting effects of temperature, habitat and phenotype on predator avoidance behaviour in Diadema antillarum: implications for restorative conservation. Mar. Ecol. Prog. Ser. 566, 105–115 (2017).ADS 
Article 

Google Scholar 
49.Andradi-Brown, D. A., Gress, E., Wright, G., Exton, D. A. & Rogers, A. D. Reef fish community biomass and trophic structure changes across shallow to upper-mesophotic reefs in the mesoamerican barrier reef, Caribbean. PLoS ONE 11, 1–19 (2016).
Google Scholar 
50.Rodríguez-Barreras, R., Pérez, M. E., Mercado-Molina, A. E. & Sabat, A. M. Arrested recovery of Diadema antillarum population: survival or recruitment limitation? Estuar. Coast. Shelf Sci. 163, 167–174 (2015).ADS 
Article 

Google Scholar 
51.Risk, M. J. Fish diversity on a coral reef in the Virgin Islands. Atoll Res. Bull. 153, 1–4 (1972).Article 

Google Scholar 
52.Figueira, W. et al. Accuracy and precision of habitat structural complexity metrics derived from underwater photogrammetry. Remote Sens. 7, 16883–16900 (2015).ADS 
Article 

Google Scholar 
53.Leon, J. X., Roelfsema, C. M., Saunders, M. I. & Phinn, S. R. Measuring coral reef terrain roughness using ‘Structure-from-Motion’ close-range photogrammetry. Geomorphology 242, 21–28 (2015).ADS 
Article 

Google Scholar 
54.Storlazzi, C. D., Dartnell, P., Hatcher, G. A. & Gibbs, A. E. End of the chain? Rugosity and fine-scale bathymetry from existing underwater digital imagery using structure-from-motion (SfM) technology. Coral Reefs 35, 889–894 (2016).ADS 
Article 

Google Scholar 
55.Young, G. C., Dey, S., Rogers, A. D. & Exton, D. A. Cost and time-effective method for multiscale measures of rugosity, fractal dimension, and vector dispersion from coral reef 3D models. PLoS ONE 12, e0175341 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
56.Zawada, D. G. & Brock, J. C. A multiscale analysis of coral reef topographic complexity using lidar-derived bathymetry. J. Coast. Res. 2009, 6–16 (2009).Article 

Google Scholar 
57.Randall, J. E., Schroeder, R. E. & Starck, W. A. Notes on the biology of the echinoid Diadema antillarum. Caribb. J. Sci. 4, 421–433 (1964).
Google Scholar 
58.Hunt, C. L. et al. Aggregating behaviour in invasive Caribbean lionfish is driven by habitat complexity. Sci. Rep. 9, 783 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
59.Millott, N. & Yoshida, M. The shadow reaction of Diadema antillarum Philippi I: the spine response and its relation to the stimulus. J. Exp. Biol. 37, 363–375 (1960).Article 

Google Scholar 
60.Millott, N. & Yoshida, M. The shadow reaction of Diadema antillarum Philippi II: inhibition by light. J. Exp. Biol. 37, 376–389 (1960).Article 

Google Scholar 
61.Raible, F. et al. Opsins and clusters of sensory G-protein-coupled receptors in the sea urchin genome. Dev. Biol. 300, 461–475 (2006).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
62.Ullrich-Lüter, E. M., D’Aniello, S. & Arnone, M. I. C-opsin expressing photoreceptors in echinoderms. Integr. Comp. Biol. 53, 27–38 (2013).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
63.Yoshida, M. On the light response of the chromatophore of the sea-urchin, Diadema setosum (Leske). J. Exp. Biol. 33, 119–123 (1956).Article 

Google Scholar 
64.JPL MUR MEaSUREs. GHRSST Level 4 MUR global foundation sea surface temperature analysis. Version 4.1 PO.DAAC, CA, USA. Dataset accessed 23 Jan 2021 at https://doi.org/10.5067/GHGMR-4FJ04 (2015).65.Pickering, H., Whitmarsh, D. & Jensen, A. Artificial reefs as a tool to aid rehabilitation of coastal ecosystems: investigating the potential. Mar. Pollut. Bull. 37, 505–514 (1999).Article 

Google Scholar 
66.Fitzhardinge, R. C. & Bailey-Brock, J. H. Colonization of artificial reef materials by corals and other sessile organisms. Bull. Mar. Sci. 44, 567–579 (1989).
Google Scholar 
67.R Core Team. R: A Language and Environment for Statistical Computing. Vienna. https://www.r-project.org/. (2016).68.RStudio Team. RStudio: Integrated Development for R (2015).69.Dinno, A. conover.test: Conover-Iman test of multiple comparisons using rank sums. R Package Version 1.1.5. (2017).70.Scheibling, R. E. & Robinson, M. C. Settlement behaviour and early post-settlement predation of the sea urchin Strongylocentrotus droebachiensis. J. Exp. Mar. Biol. Ecol. 365, 59–66 (2008).Article 

Google Scholar 
71.Kintzing, M. D. & Butler, M. J. The influence of shelter, conspecifics, and threat of predation on the behavior of the long-spined sea urchin (Diadema antillarum). J. Shellfish Res. 33, 781–785 (2014).Article 

Google Scholar 
72.Clemente, S., Hernández, J. C., Toledo, K. & Brito, A. Predation upon Diadema aff. antillarum in barren grounds in the Canary Islands. Sci. Mar. 71, 745–754 (2007).Article 

Google Scholar 
73.Jennings, L. B. & Hunt, H. L. Settlement, recruitment and potential predators and competitors of juvenile echinoderms in the rocky subtidal zone. Mar. Biol. 157, 307–316 (2010).Article 

Google Scholar 
74.Rodríguez-Barreras, R. Demographic implications of predatory wrasses on low-density Diadema antillarum populations. Mar. Biol. Res. 14, 383–391 (2018).Article 

Google Scholar 
75.Delgado, G. A. & Sharp, W. C. Does artificial shelter have a place in Diadema antillarum restoration in the Florida Keys? Tests of habitat manipulation and sheltering behavior. Glob. Ecol. Conserv. 26, e01502 (2021).Article 

Google Scholar 
76.Sammarco, P. W. & Williams, A. H. Damselfish territoriality: influence on Diadema antillarum distribution and implications for coral community structure. Mar. Ecol. Prog. Ser. 8, 53–59 (1982).ADS 
Article 

Google Scholar 
77.Nedimyer, K. & Moe, M. A. 2003. Techniques development for the reestablishment of the long-spined sea urchin, Diadema antillarum, on two small patch reefs in the upper Florida Keys. 2002–2003 Sanctuary Science Report: An Ecosystem Report Card After Five Years of Marine Zoning.78.Idjadi, J., Haring, R. & Precht, W. Recovery of the sea urchin Diadema antillarum promotes scleractinian coral growth and survivorship on shallow Jamaican reefs. Mar. Ecol. Prog. Ser. 403, 91–100 (2010).ADS 
Article 

Google Scholar 
79.Macia, S., Robinson, M. P. & Nalevanko, A. Experimental dispersal of recovering Diadema antillarum increases grazing intensity and reduces macroalgal abundance on a coral reef. Mar. Ecol. Prog. Ser. 348, 173–182 (2007).ADS 
Article 

Google Scholar  More

1.EU. Communication from the Commission. Ports: an engine for growth (2013).2.EU. Directive (EU) 2019/883 of the European Parliament and of the Council of 17 April 2019. 2019(March), 116–142 (2019).3.Chao, M. & Rodríguez, M. New trends in port managing: towards the e-port. J. Marit. Res. 3(2), 35–42 (2006).
Google Scholar 
4.Paiano, A., Crovella, T. & Lagioia, G. Managing sustainable practices in cruise tourism: the assessment of carbon footprint and waste of water and beverage packaging. Tour. Manag. 77(October 2019), 104016. https://doi.org/10.1016/j.tourman.2019.104016 (2020).Article 

Google Scholar 
5.Kovačić, M. & Silveira, L. Nautical tourism in Croatia and in Portugal in the late 2010’s: issues and perspectives. Pomorstvo 32(2), 281–289. https://doi.org/10.31217/p.32.2.13 (2018).Article 

Google Scholar 
6.Pérez Labajos, C. & Blanco Rojo, B. Leisure ports planning. J. Marit. Res. 3(2), 67–82 (2006).
Google Scholar 
7.BOE. Real Decreto Legislativo 2/2011, de 5 de septiembre, por el que se aprueba el Texto Refundido de la Ley de Puertos del Estado y de la Marina Mercante. Span. Off. Bull. 255, 11. https://www.boe.es/buscar/pdf/2011/BOE-A-2011-16467-consolidado.pdf (2011).8.Gómez, A. G., Valdor, P. F., Ondiviela, B., Díaz, J. L. & Juanes, J. A. Mapping the environmental risk assessment of marinas on water quality: the Atlas of the Spanish coast. Mar. Pollut. Bull. 139(January), 355–365. https://doi.org/10.1016/j.marpolbul.2019.01.008 (2019).CAS 
Article 
PubMed 

Google Scholar 
9.Sofiev, M. et al. Cleaner fuels for ships provide public health benefits with climate tradeoffs. Nat. Commun. 9(1), 1–12. https://doi.org/10.1038/s41467-017-02774-9 (2018).CAS 
Article 

Google Scholar 
10.Chen, C., Saikawa, E., Comer, B., Mao, X. & Rutherford, D. Ship emission impacts on air quality and human health in the Pearl River Delta (PRD) Region, China, in 2015, with projections to 2030. GeoHealth 3(9), 284–306. https://doi.org/10.1029/2019GH000183 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
11.Mateos, M. R. Los puertos deportivos como infraestructuras de soporte de las actividades náuticas de recreo en Andalucía. Mar. Infrastruct. Supports Naut. Recreat. Act. Andal. 54, 335–360 (2010).
Google Scholar 
12.Nursey-Bray, M. et al. Vulnerabilities and adaptation of ports to climate change. J. Environ. Plan. Manag. 56(7), 1021–1045. https://doi.org/10.1080/09640568.2012.716363 (2013).Article 

Google Scholar 
13.Antequera, P. D., Jaime, D. & Abel, L. Tourism, transport and climate change: the carbon footprint of international air traffic on Islands. Sustainability 13(4), 1795. https://doi.org/10.3390/su13041795 (2021).CAS 
Article 

Google Scholar 
14.Hadjikakou, M., Chenoweth, J. & Miller, G. Estimating the direct and indirect water use of tourism in the eastern Mediterranean. J. Environ. Manag. 114, 548–556. https://doi.org/10.1016/j.jenvman.2012.11.002 (2013).Article 

Google Scholar 
15.Annis, G. M. et al. Designing coastal conservation to deliver ecosystem and human well-being benefits. PLoS ONE 12(2), 1–21. https://doi.org/10.1371/journal.pone.0172458 (2017).CAS 
Article 

Google Scholar 
16.Kizielewicz, J. & Lukovic, T. The phenomenon of the marina development to support the European model of economic development. TransNav Int. J. Mar. Navig. Saf. Sea Transp. 7(3), 461–466. https://doi.org/10.12716/1001.07.03.19 (2013).Article 

Google Scholar 
17.Ridolfi, E., Pujol, D. S., Ippolito, A., Saradakou, E. & Salvati, L. An urban political ecology approach to local development in fast-growing, tourism-specialized coastal cities. Tourismos 12(1), 171–204 (2017).
Google Scholar 
18.Sevinç, F. & Güzel, T. Sustainable Yacht tourism practices. Manag. Mark. XV(1), 61–76 (2017).
Google Scholar 
19.Lam-González, Y. E., León, C. J. & González-Hernández, M. M. Determinants of the European Yachtsmen´s satisfaction with the ports of call of the Canary Islands (Spain). Études Caribéennes https://doi.org/10.4000/etudescaribeennes.10584 (2017).Article 

Google Scholar 
20.Novales, A., Martínez Martín, M. I., Castro Núñez, R. B., Cazcarro Castellano, I. & Santero Sánchez, R. El impacto económico de la Náutica de Recreo 99 (Universidad Complutense de Madrid, 2018).
Google Scholar 
21.Cámara de Comercio e Industria de Marsella. Náutica de recreo en el Mediterráneo 114 (Etinet, 2011).
Google Scholar 
22.Mensa, J. A., Vasallo, P. & Fabiano, M. JMarinas: a simple tool for the environmentally sound management of small marinas. J. Environ. Manag. 92, 67–77 (2011).CAS 
Article 

Google Scholar 
23.Benton, T. G. From castaways to throwaways: marine litter in the Pitcairn Islands. Biol. J. Lin. Soc. 56, 415–422 (1995).Article 

Google Scholar 
24.Chainho, P. et al. Non-indigenous species in Portuguese coastal areas, coastal lagoons, estuaries and islands. Estuar. Coast. Shelf Sci. 167, 199–211. https://doi.org/10.1016/j.ecss.2015.06.019 (2015).ADS 
Article 

Google Scholar 
25.Styhre, L., Winnes, H., Black, J., Lee, J. & Le-Griffin, H. Greenhouse gas emissions from ships in ports: case studies in four continents. Transp. Res. Part D Transp. Environ. 54, 212–224. https://doi.org/10.1016/j.trd.2017.04.033 (2017).Article 

Google Scholar 
26.Yang, Y. C. Operating strategies of CO2 reduction for a container terminal based on carbon footprint perspective. J. Clean. Prod. 141, 472–480. https://doi.org/10.1016/j.jclepro.2016.09.132 (2017).CAS 
Article 

Google Scholar 
27.Giunta, M., Bressi, S. & D’Angelo, G. Life cycle cost assessment of bitumen stabilised ballast: a novel maintenance strategy for railway track-bed. Constr. Build. Mater. 172, 751–759. https://doi.org/10.1016/j.conbuildmat.2018.04.020 (2018).Article 

Google Scholar 
28.Hickmann, T. Voluntary global business initiatives and the international climate negotiations: a case study of the Greenhouse Gas Protocol. J. Clean. Prod. 169, 94–104. https://doi.org/10.1016/j.jclepro.2017.06.183 (2017).Article 

Google Scholar 
29.Garcia, R. & Freire, F. Carbon footprint of particleboard: a comparison between ISO/TS 14067, GHG protocol, PAS 2050 and climate declaration. J. Clean. Prod. 66, 199–209. https://doi.org/10.1016/j.jclepro.2013.11.073 (2014).CAS 
Article 

Google Scholar 
30.Ingrid, M.-M., Pablo, C.-M., Jose, V.-C. & Miguel Ángel, P.-G. Economic impact of a port on the hinterland: application to Santander’s port. Int. J. Shipp. Transp. Logist. 4, 235–249 (2012).Article 

Google Scholar 
31.Abdul-azeez, I. A. Development of carbon dioxide emission assessment tool towards promoting sustainability in UTM Malaysia. Open J. Energy Effic. https://doi.org/10.4236/ojee.2018.72004 (2018).Article 

Google Scholar 
32.Jeswani, H. K. & Azapagic, A. Water footprint: methodologies and a case study for assessing the impacts of water use. J. Clean. Prod. 19(12), 1288–1299. https://doi.org/10.1016/j.jclepro.2011.04.003 (2011).Article 

Google Scholar 
33.Zhuo, La., Mekonnen, M. M. & Hoekstra, A. Y. Consumptive water footprint and virtual water trade scenarios for China: with a focus on crop production, consumption and trade. Environ. Int. 94, 211–223 (2016).Article 

Google Scholar 
34.Arto, I., Andreoni, V. & Rueda-Cantuche, J. M. Global use of water resources: a multiregional analysis of water use, water footprint and water trade balance. Water Resour. Econ. 15, 1–14. https://doi.org/10.1016/j.wre.2016.04.002 (2016).Article 

Google Scholar 
35.Zhi, Y., Yang, Z., Yin, X., Hamilton, P. B. & Zhang, L. Using gray water footprint to verify economic sectors’ consumption of assimilative capacity in a river basin: model and a case study in the Haihe River Basin, China. J. Clean. Prod. 92, 267–273. https://doi.org/10.1016/j.jclepro.2014.12.058 (2015).Article 

Google Scholar 
36.Norén, A., Karlfeldt Fedje, K., Strömvall, A. M., Rauch, S. & Andersson-Sköld, Y. Integrated assessment of management strategies for metal-contaminated dredged sediments: what are the best approaches for ports, marinas and waterways?. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.135510 (2020).Article 
PubMed 

Google Scholar 
37.Kenworthy, J. M., Rolland, G., Samadi, S. & Lejeusne, C. Local variation within marinas: effects of pollutants and implications for invasive species. Mar. Pollut. Bull. 133(March), 96–106. https://doi.org/10.1016/j.marpolbul.2018.05.001 (2018).CAS 
Article 
PubMed 

Google Scholar 
38.Veettil, A. V. & Mishra, A. K. Water security assessment using blue and green water footprint concepts. J. Hydrol. 542, 589–602. https://doi.org/10.1016/j.jhydrol.2016.09.032 (2016).ADS 
Article 

Google Scholar 
39.Gu, Y., Li, Y., Wang, H. & Li, F. Gray water footprint: taking quality, quantity, and time effect into consideration. Water Resour. Manag. 28(11), 3871–3874. https://doi.org/10.1007/s11269-014-0695-y (2014).Article 

Google Scholar 
40.Duvat, V. K. E. et al. Trajectories of exposure and vulnerability of small islands to climate change. Rev. Clim. Change https://doi.org/10.1002/wcc.478 (2017).Article 

Google Scholar 
41.Millán, M. M. Extreme hydrometeorological events and climate change predictions in Europe. J. Hydrol. 518(PB), 206–224. https://doi.org/10.1016/j.jhydrol.2013.12.041 (2014).ADS 
CAS 
Article 

Google Scholar 
42.Smith, J. B. et al. Assessing dangerous climate change through an update of the Intergovernmental Panel on Climate Change (IPCC) “‘reasons for concern’”. Proc. Natl. Acad. Sci. U.S.A. 106(11), 4133–4137. https://doi.org/10.1073/pnas.0812355106 (2009).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
43.IPCC. Climate change 2014: impacts, adaptation and vulnerability (2014).44.Ciscar, J. C. et al. Physical and economic consequences of climate change in Europe. Proc. Natl. Acad. Sci. U.S.A. 108(7), 2678–2683. https://doi.org/10.1073/pnas.1011612108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Melo, N., Santos, B. F. & Leandro, J. A prototype tool for dynamic pluvial-flood emergency planning. Urban Water J. 12(1), 79–88. https://doi.org/10.1080/1573062X.2014.975725 (2015).Article 

Google Scholar 
46.Lazrus, H. Sea change: Island communities and climate change. Annu. Rev. Anthropol. 41, 285–301. https://doi.org/10.1146/annurev-anthro-092611-145730 (2012).Article 

Google Scholar 
47.Reid, S., Johnston, N. & Patiar, A. Coastal resorts setting the pace: an evaluation of sustainable hotel practices. J. Hosp. Tour. Manag. 33, 11–22. https://doi.org/10.1016/j.jhtm.2017.07.001 (2017).Article 

Google Scholar 
48.Vargas-Amelin, E. & Pindado, P. The challenge of climate change in Spain: water resources, agriculture and land. J. Hydrol. 518(PB), 243–249. https://doi.org/10.1016/j.jhydrol.2013.11.035 (2014).ADS 
Article 

Google Scholar 
49.Fagerberg, J., Laestadius, S. & Martin, B. R. The triple challenge for Europe: the economy, climate change, and governance. Innov. Econ. Dev. Policy Sel. Essays 59(3), 384–410. https://doi.org/10.1080/05775132.2016.1171668 (2018).Article 

Google Scholar 
50.UNCTAD. Maritime transport in small island developing states. Rev. Marit. Transp. https://doi.org/10.1017/CBO9781107415324.004 (2014).Article 

Google Scholar 
51.Hinkey, L. M., Zaidi, B. R., Volson, B. & Rodriguez, N. J. Identifying sources and distributions of sediment contaminants at two US Virgin Islands marinas. Mar. Pollut. Bull. 50, 1244–1250. https://doi.org/10.1016/j.marpolbul.2005.04.035 (2005).CAS 
Article 
PubMed 

Google Scholar 
52.Marín, J. C. et al. Properties of particulate pollution in the port city of Valparaiso, Chile. Atmos. Environ. 171, 301–316. https://doi.org/10.1016/j.atmosenv.2017.09.044 (2017).ADS 
CAS 
Article 

Google Scholar 
53.Tóvar-Sánchez, A., Sánchez-Quiles, D. & Rodríguez-Romero, A. Massive coastal tourism influx to the Mediterranean Sea: the environmental risk of sunscreens. Sci. Total Environ. 656, 316–321 (2019).ADS 
Article 

Google Scholar 
54.Uche-Soria, M. & Rodríguez-Monroy, C. Solutions to marine pollution in Canary Islands’ ports: alternatives and optimization of energy management. Resources https://doi.org/10.3390/resources8020059 (2019).Article 

Google Scholar 
55.Bosch, N. E., Gonçalves, J. M. S., Tuya, F. & Erzini, K. Marinas as habitats for nearshore fish assemblages: comparative analysis of underwater visual census, baited cameras and fish traps. Sci. Mar. 81(2), 159. https://doi.org/10.3989/scimar.04540.20a (2017).Article 

Google Scholar 
56.Di Franco, A. et al. Do small marinas drive habitat specific impacts? A case study from Mediterranean Sea. Mar. Pollut. Bull. 62, 926–933. https://doi.org/10.1016/j.marpolbul.2011.02.053 (2011).CAS 
Article 
PubMed 

Google Scholar 
57.Pasetto, M. & Partl, M. N. in Lecture Notes in Civil Engineering Proceedings of the 5th International Symposium on Asphalt Pavements & Environment (APE). http://www.springer.com/series/15087 (2020)58.Praticò, F. G., Giunta, M., Mistretta, M. & Gulotta, T. M. Energy and environmental life cycle assessment of sustainable pavement materials and technologies for urban roads. Sustainability (Switzerland) https://doi.org/10.3390/su12020704 (2020).Article 

Google Scholar 
59.Hertwich, E. G. & Wood, R. The growing importance of scope 3 greenhouse gas emissions from industry. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/aae19a (2018).Article 

Google Scholar 
60.Di Vaio, A., Varriale, L. & Alvino, F. Key performance indicators for developing environmentally sustainable and energy efficient ports: evidence from Italy. Energy Policy 122(July), 229–240. https://doi.org/10.1016/j.enpol.2018.07.046 (2018).Article 

Google Scholar 
61.Corrigan, S., Kay, A., Ryan, M., Brazil, B. & Ward, M. E. Human factors & safety culture: challenges & opportunities for the port environment. Saf. Sci. 125, 14. https://doi.org/10.1016/j.ssci.2018.02.030 (2020).Article 

Google Scholar 
62.Mali, M., Dell’Anna, M. M., Mastrorilli, P., Damiani, L. & Piccinni, A. F. Assessment and source identification of pollution risk for touristic ports: heavy metals and polycyclic aromatic hydrocarbons in sediments of 4 marinas of the Apulia region (Italy). Mar. Pollut. Bull. 114(2), 768–777. https://doi.org/10.1016/j.marpolbul.2016.10.063 (2017).CAS 
Article 
PubMed 

Google Scholar 
63.Cutroneo, L., Reboa, A., Besio, G., Borgogno, F., Canesi, L., Canuto, S., Dara, M., Enrile, F., Forioso, I., Greco, G., Lenoble, V., Malatesta, A., Mounier, S., Petrillo, M., Rovetta, R., Stocchino, A., Tesan, J., Vagge, G., & Capello, M. Correction to: Microplastics in seawater: sampling strategies, laboratory methodologies, and identification techniques applied to port environment (Environmental Science and Pollution Research, (2020), 27, 9, (8938–8952), https://doi.org/10.1007/s11356-020-07783-8). Environ. Sci. Pollut. Res. 27(16), 20571. https://doi.org/https://doi.org/10.1007/s11356-020-08704-5 (2020)64.Kotowska, I. & Kubowicz, D. The role of ports in reduction of road transport pollution in port cities. Transp. Res. Procedia 39, 212–220. https://doi.org/10.1016/j.trpro.2019.06.023 (2019).Article 

Google Scholar 
65.Coronado Mondragon, A. E., Lalwani, C. S., Coronado Mondragon, E. S., Coronado Mondragon, C. E. & Pawar, K. S. Intelligent transport systems in multimodal logistics: a case of role and contribution through wireless vehicular networks in a sea port location. Int. J. Prod. Econ. 137, 165–175. https://doi.org/10.1016/j.ijpe.2011.11.006 (2012).Article 

Google Scholar 
66.Caballini, C., Rebecchi, I. & Sacone, S. Combining multiple trips in a port environment for empty movements minimization. Transp. Res. Procedia 10, 694–703. https://doi.org/10.1016/j.trpro.2015.09.023 (2015).Article 

Google Scholar 
67.Sifakis, N. & Tsoutsos, T. Planning zero-emissions ports through the nearly zero energy port concept. J. Clean. Prod. 286, 20. https://doi.org/10.1016/j.jclepro.2020.125448 (2021).Article 

Google Scholar 
68.Karimpour, R., Ballini, F. & Ölcer, A. I. Circular economy approach to facilitate the transition of the port cities into self-sustainable energy ports: a case study in Copenhagen-Malmö Port (CMP). WMU J. Marit. Aff. 18(2), 225–247. https://doi.org/10.1007/s13437-019-00170-2 (2019).Article 

Google Scholar 
69.Babrowski, S., Heinrichs, H., Jochem, P. & Fichtner, W. Load shift potential of electric vehicles in Europe. J. Power Sources 255, 283–293. https://doi.org/10.1016/j.jpowsour.2014.01.019 (2014).ADS 
CAS 
Article 

Google Scholar 
70.Azarkamand, S., Ferré, G. & Darbra, R. M. Calculating the carbon footprint in ports by using a standardized tool. Sci. Total Environ. 734, 139407. https://doi.org/10.1016/j.scitotenv.2020.139407 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
71.Carballo-Penela, A., Mateo-Mantecón, I., Doménech, J. L. & Coto-Millán, P. From the motorways of the sea to the green corridors’ carbon footprint: the case of a port in Spain. J. Environ. Plan. Manag. 55(6), 765–782. https://doi.org/10.1080/09640568.2011.627422 (2012).Article 

Google Scholar 
72.Paska, J. & Surma, T. Electricity generation from renewable energy sources in Poland. Renew. Energy 71, 286–294 (2014).Article 

Google Scholar 
73.Trujillo-Baute, E., del Río, P. & Mir-Artigues, P. Analysing the impact of renewable energy regulation on retail electricity prices. Energy Policy 114, 153–164 (2018).Article 

Google Scholar 
74.Ruiz-Romero, S., Colmenar-Santos, A., Gil-Ortego, R. & Molina-Bonilla, A. Distributed generation: the definitive boost for renewable energy in Spain. Renew. Energy 53, 354–364 (2013).Article 

Google Scholar 
75.Burgos-Payán, M., Roldán-Fernández, J. M., Trigo-García, Á. L., Bermúdez-Ríos, J. M. & Riquelme-Santos, J. M. Costs and benefits of the renewable production of electricity in Spain. Energy Policy 56, 259–270 (2013).Article 

Google Scholar 
76.Taliotis, C. et al. Renewable energy technology integration for the island of Cyprus: a cost-optimization approach. Energy 137(2017), 31–41. https://doi.org/10.1016/j.energy.2017.07.015 (2017).Article 

Google Scholar 
77.Deyà-Tortella, B., Garcia, C., Nilsson, W. & Tirado, D. The effect of the water tariff structures on the water consumption in Mallorcan hotels. Water Resour. Res. 52(8), 6386–6403. https://doi.org/10.1002/2016WR018621 (2016).ADS 
Article 

Google Scholar 
78.Liu, J. et al. A global and spatially explicit assessment of climate change impacts on crop production and consumptive water use. PLoS ONE https://doi.org/10.1371/journal.pone.0057750 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
79.Hof, A. & Schmitt, T. Urban and tourist land use patterns and water consumption: evidence from Mallorca, Balearic Islands. Land Use Policy 28, 792–804 (2011).Article 

Google Scholar 
80.Urban water consumption in the Balearic islands. The water portal: http://www.caib.es/sites/aigua/es/consumo_agua/81.García, C., Mestre-Runge, C., Morán-Tejeda, E., Lorenzo-Lacruz, J., Tirado, D. (2020). Impact of Cruise Activity on Freshwater Use in the Port of Palma (Mallorca, Spain): Water 12, 1088.82.Yves Tramblay, Aristeidis Koutroulis, Luis Samaniego, Sergio Vicente-Serrano, Florence Volaire, et al. Challenges for drought assessment in the Mediterranean region under future climate scenarios. EarthScience Reviews, Elsevier, 2020, 210, pp.103348. https://doi.org/10.1016/j.earscirev.2020.103348f More