Philippa Kaur

Seneviratne, S. I. et al. in Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 1513–1766 (Cambridge Univ. Press, 2021).
Google Scholar 
Hesketh, A. V. & Harley, C. D. G. Glob. Change Biol. https://doi.org/10.1111/gcb.16390 (2022).Article 

Google Scholar 
Jørgensen, L. B., Ørsted, M., Malte, H., Wang, T. & Overgaard, J. Nature https://doi.org/10.1038/s41586-022-05334-4 (2022).Article 

Google Scholar 
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Nature Clim. Change 2, 686–690 (2012).Article 

Google Scholar 
Cossins, A. R. & Bowler, K. Temperature Biology of Animals (Chapman & Hall, 1987).
Google Scholar 
Dell, A. I., Pawar, S. & Savage, V. M. Proc. Natl Acad. Sci. USA 108, 10591–10596 (2011).Article 
PubMed 

Google Scholar 
Dillon, M. E. et al. Integr. Comp. Biol. 56, 14–30 (2016).Article 
PubMed 

Google Scholar 
Stillman, J. H. Physiology 34, 86–100 (2019).Article 
PubMed 

Google Scholar 
MacMillan, H. A. J. Exp. Biol. 222, jeb191593 (2019).Article 
PubMed 

Google Scholar 
Kingsolver, J. G. & Umbanhowar, J. J. Exp. Biol. 221, jeb167858 (2018).Article 
PubMed 

Google Scholar  More

Steen AD, Carini ACP, Lloyd KG, Thrash JC, Deangelis KM, Fierer N. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 2019;13:3126–30.PubMed 
PubMed Central 

Google Scholar 
Lloyd KG, Steen AD, Ladau J, Yin J. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems. 2018;3:e00055–18.CAS 
PubMed 
PubMed Central 

Google Scholar 
Marcy Y, Ouverney C, Bik EM, Lo T, Ivanova N, Garcia H, et al. Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc Natl Acad Sci USA. 2007;104:11889–94.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pascoal F, Costa R, Magalhães C. The microbial rare biosphere: current concepts, methods and ecological principles. FEMS Microbiol Ecol. 2021;97:fiaa227.CAS 
PubMed 

Google Scholar 
Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA. 2006;103:12115–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gareev KG, Grouzdev DS, Kharitonskii PV, Kosterov A, Koziaeva VV, Sergienko ES, et al. Magnetotactic bacteria and magnetosomes: basic properties and applications. Magnetochemistry. 2021;7:86.CAS 

Google Scholar 
Lefevre CT, Bazylinski DA. Ecology, diversity, and evolution of magnetotactic bacteria. Microbiol Mol Biol Rev. 2013;77:497–526.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin W, Pan Y, Bazylinsky DA. Diversity and ecology of and biomineralization by magnetotactic bacteria. Environ Microbiol Rep. 2017;9:345–56.CAS 
PubMed 

Google Scholar 
Uebe R, Schüler D. Magnetosome biogenesis in magnetotactic bacteria. Nat Rev Microbiol. 2016;14:621–37.CAS 
PubMed 

Google Scholar 
Lefèvre CT, Frankel RB, Bazylinski DA. Magnetotaxis in prokaryotes. eLS. 2011. https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2.Goswami P, He K, Li J, Pan Y, Roberts AP, Lin W. Magnetotactic bacteria and magnetofossils: ecology, evolution and environmental implications. npj Biofilms Microbiomes. 2022;8:43.PubMed 
PubMed Central 

Google Scholar 
Flies CB, Jonkers HM, De Beer D, Bosselmann K, Böttcher ME, Schüler D. Diversity and vertical distribution of magnetotactic bacteria along chemical gradients in freshwater microcosms. FEMS Microbiol Ecol. 2005;52:185–95.CAS 
PubMed 

Google Scholar 
Wolfe RS, Thauer RK, Pfennig N. A’capillary racetrack’ method for isolation of magnetotactic bacteria. FEMS Microbiol Ecol. 1987;45:31–5.
Google Scholar 
Jogler C, Lin W, Meyerdierks A, Kube M, Katzmann E, Flies C, et al. Toward cloning of the magnetotactic metagenome: identification of magnetosome island gene clusters in uncultivated magnetotactic bacteria from different aquatic sediments. Appl Environ Microbiol. 2009;75:3972–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin W, Zhang W, Paterson GA, Zhu Q, Zhao X. Expanding magnetic organelle biogenesis in the domain Bacteria. Microbiome. 2020;8:152.CAS 
PubMed 
PubMed Central 

Google Scholar 
Geissinger O, Herlemann DPR, Mo E, Maier UG, Brune A. The ultramicrobacterium “Elusimicrobium minutum” gen. nov., sp. nov., the first cultivated representative of the Termite Group 1 phylum. Appl Environ Microbiol. 2009;75:2831–40.CAS 
PubMed 
PubMed Central 

Google Scholar 
Wakako I-O, Brune A. Cospeciation of termite gut flagellates and their bacterial endosymbionts: Trichonympha species and ‘Candidatus Endomicrobium trichonymphae’. Mol Ecol. 2009;18:332–42.
Google Scholar 
Zheng H, Dietrich C, Radek R, Brune A. Endomicrobium proavitum, the first isolate of Endomicrobia class. nov. (phylum Elusimicrobia) – an ultramicrobacterium with an unusual cell cycle that fixes nitrogen with a Group IV nitrogenase. Environ Ecol Stat. 2016;18:191–204.CAS 

Google Scholar 
Méheust R, Castelle CJ, Carnevali PBM, Chen L, Amano Y, Hug LA, et al. Groundwater Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have a novel nitrogenase paralog. ISME J. 2020;14:2907–22.PubMed 
PubMed Central 

Google Scholar 
Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.CAS 
PubMed 
PubMed Central 

Google Scholar 
Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
PubMed 

Google Scholar 
Zhang L, Gong X, Wang L, Guo K, Cao S, Zhou Y. Science of the total environment metagenomic insights into the effect of thermal hydrolysis pre-treatment on microbial community of an anaerobic digestion system. Sci Total Environ. 2021;791:148096.CAS 
PubMed 

Google Scholar 
Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
PubMed 

Google Scholar 
Uzun M, Alekseeva L, Krutkina M, Koziaeva V, Grouzdev D. Unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases. Sci Data. 2020;7:252.PubMed 
PubMed Central 

Google Scholar 
Tully BJ, Wheat CG, Glazer BT, Huber JA. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME J. 2018;12:1–16.CAS 
PubMed 

Google Scholar 
Kirillova NP, Sileva TM, Ul’yanova TY, Rozov SY, Il’yashenko MA, Makarov MI. Digital soil map of Chashnikovo training and experimental soil ecological center, Moscow State University. Mosc Univ Soil Sci Bull. 2015;70:58–65.
Google Scholar 
Koziaeva VV, Alekseeva LM, Uzun MM, Leão P, Sukhacheva MV, Patutina EO, et al. Biodiversity of magnetotactic bacteria in the freshwater lake Beloe Bordukovskoe, Russia. Microbiology. 2020;89:348–58.CAS 

Google Scholar 
Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
PubMed 

Google Scholar 
Edgar RC, Flyvbjerg H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics. 2015;31:3476–82.CAS 
PubMed 

Google Scholar 
Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv 2016. https://doi.org/10.1101/081257.Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Fierer N, Jackson JA, Vilgalys R, Jackson RB. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl Environ Microbiol. 2005;71:4117–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
PubMed 
PubMed Central 

Google Scholar 
Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. MetaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
PubMed 
PubMed Central 

Google Scholar 
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 

Google Scholar 
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 

Google Scholar 
Lin HH, Liao YC. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep. 2016;6:12–9.
Google Scholar 
Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
PubMed 
PubMed Central 

Google Scholar 
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.CAS 
PubMed 
PubMed Central 

Google Scholar 
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 

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

Google Scholar 
Tatusova T, Dicuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.CAS 
PubMed 
PubMed Central 

Google Scholar 
Ji R, Zhang W, Pan Y, Lin W. MagCluster: a tool for identification, annotation, and visualization of magnetosome gene clusters. Microbiol Resour Announc. 2022;11:e01031–21.CAS 
PubMed Central 

Google Scholar 
Wu S, Zhu Z, Fu L, Niu B, Li W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics. 2011;12:444.PubMed 
PubMed Central 

Google Scholar 
Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.CAS 
PubMed 

Google Scholar 
Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL. 0003 3527 8101, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.CAS 
PubMed 
PubMed Central 

Google Scholar 
Jain C, Rodriguez-R LM, 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 

Google Scholar 
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 

Google Scholar 
Kalyaanamoorthy S, Minh BQ, Wong TKF, Haeseler AVon, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
PubMed 
PubMed Central 

Google Scholar 
Hoang DT, Chernomor O, Von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
PubMed 

Google Scholar 
Letunic I, Bork P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.CAS 
PubMed 
PubMed Central 

Google Scholar 
Coleman GA, Davín AA, Mahendrarajah TA, Szánthó LL, Spang A, Hugenholtz P, et al. A rooted phylogeny resolves early bacterial evolution. Science. 2021;372:eabe0511.CAS 
PubMed 

Google Scholar 
Parks DH. https://github.com/dparks1134/CompareM.Dombrowski N, Lee JH, Williams TA, Offre P, Spang A. Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett. 2019;366:fnz008.CAS 
PubMed Central 

Google Scholar 
Lin W, Zhang W, Zhao X, Roberts AP, Paterson GA, Bazylinski DA, et al. Genomic expansion of magnetotactic bacteria reveals an early common origin of magnetotaxis with lineage-specific evolution. ISME J. 2018;12:1508–19.CAS 
PubMed 
PubMed Central 

Google Scholar 
Urakawa H, Garcia JC, Nielsen JL, Le VQ, Kozlowski JA, Stein LY, et al. Nitrosospira lacus sp. nov., a psychrotolerant, ammonia-oxidizing bacterium from sandy lake sediment. Int J Syst Evol Microbiol. 2015;65:242–50.CAS 
PubMed 

Google Scholar 
Kalyuzhnaya MG, De Marco P, Bowerman S, Pacheco CC, Lara JC, Lidstrom ME, et al. Methyloversatilis universalis gen. nov., sp. nov., a novel taxon within the Betaproteobacteria represented by three methylotrophic isolates. Int J Syst Evol Microbiol. 2006;56:2517–22.CAS 
PubMed 

Google Scholar 
Bazylinski DA, Frankel RB, Konhauser KO. Modes of biomineralization of magnetite by microbes. Geomicrobiol J. 2007;24:465–75.CAS 

Google Scholar 
Uzun M, Koziaeva V, Dziuba M, Leão P, Krutkina M, Grouzdev D. Detection of interphylum transfers of the magnetosome gene cluster in magnetotactic bacteria. Front Microbiol. 2022;13:945734.PubMed 
PubMed Central 

Google Scholar 
Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996.CAS 
PubMed 

Google Scholar 
Murphy CL, Biggerstaff J, Eichhorn A, Ewing E, Shahan R, Soriano D, et al. Genomic characterization of three novel Desulfobacterota classes expand the metabolic and phylogenetic diversity of the phylum. Environ Microbiol. 2021;23:4326–43.CAS 
PubMed 

Google Scholar 
Konstantinidis KT, Rosselló-Móra R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
PubMed Central 

Google Scholar 
Denise R, Abby SS, Rocha EPC. Diversification of the type IV filament superfamily into machines for adhesion, protein secretion, DNA uptake, and motility. PLoS Biol. 2019;17:e3000390.PubMed 
PubMed Central 

Google Scholar 
Hennell James R, Deme JC, Kjӕr A, Alcock F, Silale A, Lauber F, et al. Structure and mechanism of the proton-driven motor that powers type 9 secretion and gliding motility. Nat Microbiol. 2021;6:221–33.CAS 
PubMed 

Google Scholar 
Nolan LM, Whitchurch CB, Barquist L, Katrib M, Boinett CJ, Mayho M, et al. A global genomic approach uncovers novel components for twitching motility-mediated biofilm expansion in Pseudomonas aeruginosa. Micro Genomics. 2018;4:e000229.
Google Scholar 
Uzun M, Koziaeva V, Dziuba M, Alekseeva L, Grouzdev D. Mam protein trees. 2022. https://doi.org/10.6084/m9.figshare.c.6045158.v1.Arnoux P, Siponen MI, Lefèvre CT, Ginet N, Pignol D. Structure and evolution of the magnetochrome domains: no longer alone. Front Microbiol. 2014;5:117.PubMed 
PubMed Central 

Google Scholar 
Katzmann E, Scheffel A, Gruska M, Plitzko JM, Schüler D. Loss of the actin-like protein MamK has pleiotropic effects on magnetosome formation and chain assembly in Magnetospirillum gryphiswaldense. Mol Microbiol. 2010;77:208–24.CAS 
PubMed 

Google Scholar 
Wagner-Döbler I, Bennasar A, Vancanneyt M, Strömpl C, Brümmer I, Eichner C, et al. Microcosm enrichment of biphenyl-degrading microbial communities from soils and sediments. Appl Environ Microbiol. 1998;64:3014–22.PubMed 
PubMed Central 

Google Scholar 
Ibekwe AM, Papiernik SK, Gan J, Yates SR, Crowley DE, Yang CH. Microcosm enrichment of 1,3-dichloropropene-degrading soil microbial communities in a compost-amended soil. J Appl Microbiol. 2001;91:668–76.CAS 
PubMed 

Google Scholar 
Yakimov MM, Denaro R, Genovese M, Cappello S, D’Auria G, Chernikova TN, et al. Natural microbial diversity in superficial sediments of Milazzo Harbor (Sicily) and community successions during microcosm enrichment with various hydrocarbons. Environ Microbiol. 2005;7:1426–41.CAS 
PubMed 

Google Scholar 
Tringe SG, Von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, et al. Comparative metagenomics of microbial communities. Science. 2005;308:554–7.CAS 
PubMed 

Google Scholar 
Lefèvre CT, Trubitsyn D, Abreu F, Kolinko S, Jogler C, de Almeida LGP, et al. Comparative genomic analysis of magnetotactic bacteria from the Deltaproteobacteria provides new insights into magnetite and greigite magnetosome genes required for magnetotaxis. Environ Microbiol. 2013;15:2712–35.PubMed 

Google Scholar 
Wadhwa N, Berg HC. Bacterial motility: machinery and mechanisms. Nat Rev Microbiol. 2022;20:161–73.CAS 
PubMed 

Google Scholar 
Zhu K, Pan H, Li J, Yu-Zhang K, Zhang SD, Zhang WY, et al. Isolation and characterization of a marine magnetotactic spirillum axenic culture QH-2 from an intertidal zone of the China Sea. Res Microbiol. 2010;161:276–83.CAS 
PubMed 

Google Scholar 
Kaimer C, Zusman DR. Regulation of cell reversal frequency in Myxococcus xanthus requires the balanced activity of CheY-like domains in FrzE and FrzZ. Mol Microbiol. 2016;100:379–95.CAS 
PubMed 

Google Scholar 
Kühn MJ, Talà L, Inclan YF, Patino R, Pierrat X, Vos I, et al. Mechanotaxis directs Pseudomonas aeruginosa twitching motility. Proc Natl Acad Sci USA. 2021;118:e2101759118.PubMed 
PubMed Central 

Google Scholar  More

World Urbanization Prospects: The 2018 Revision (UN Department of Economic and Social Affairs, 2018).Global Vector Control Response 2017–2030 (World Health Organization & UNICEF, 2017).Gubler, D. J. Dengue, urbanization and globalization: the unholy trinity of the 21st century. Trop. Med. Health 39, S3–S11 (2011).Article 

Google Scholar 
Brady, O. J. et al. Refining the global spatial limits of dengue virus transmission by evidence-based consensus. PLoS Negl. Trop. Dis. 6, e1760 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Kraemer, M. U. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Brown, J. E. et al. Worldwide patterns of genetic differentiation imply multiple ‘domestications’ of Aedes aegypti, a major vector of human diseases. Proc. R. Soc. B Biol. Sci. 278, 2446–2454 (2011).Article 

Google Scholar 
Padmanabha, H., Durham, D., Correa, F., Diuk-Wasser, M. & Galvani, A. The interactive roles of Aedes aegypti super-production and human density in dengue transmission. PLoS Negl. Trop. Dis. 6, e1799 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
Stewart-Ibarra, A. M. et al. Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010. BMC Infect. Dis. 14, 610 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Cavany, S. M. et al. Optimizing the deployment of ultra-low volume and targeted indoor residual spraying for dengue outbreak response. PLoS Comput. Biol. 16, e1007743 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Stefopoulou, Α et al. Reducing Aedes albopictus breeding sites through education: a study in urban area. PLoS ONE 13, e0202451 (2018).Article 

Google Scholar 
Lindsay, S. W., Wilson, A., Golding, N., Scott, T. W. & Takken, W.Improving the built environment in urban areas to control Aedes aegypti-borne diseases. Bull. World Health Organ. 95, 607–608 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Echaubard, P. et al. Fostering social innovation and building adaptive capacity for dengue control in Cambodia: a case study. Infect. Dis. Poverty 9, 126 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Vazquez-Prokopec, G. M., Lenhart, A. & Manrique-Saide, P. Housing improvement: a novel paradigm for urban vector-borne disease control? Trans. R. Soc. Trop. Med. Hyg. 110, 567–569 (2016).Article 
PubMed 

Google Scholar 
Malone, R. W. et al. Zika virus: medical countermeasure development challenges. PLoS Negl. Trop. Dis. 10, e0004530 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Murdock, C. C., Evans, M. V., McClanahan, T. D., Miazgowicz, K. L. & Tesla, B. Fine-scale variation in microclimate across an urban landscape shapes variation in mosquito population dynamics and the potential of Aedes albopictus to transmit arboviral disease. PLoS Negl. Trop. Dis. 11, e0005640 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Bradley, C. A. & Altizer, S. Urbanization and the ecology of wildlife diseases. Trends Ecol. Evol. 22, 95–102 (2007).Article 
PubMed 

Google Scholar 
McDonald, R. I., Kareiva, P. & Forman, R. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).Article 

Google Scholar 
Ferraguti, M. et al. Effects of landscape anthropization on mosquito community composition and abundance. Sci. Rep. 6, 29002 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Juliano, S. A., Westby, K. M. & Ower, G. D. Know your enemy: effects of a predator on native and invasive container mosquitoes. J. Med. Entomol. 56, 320–328 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Mahendra, A. & Seto, K. C. Upward and Outward Growth: Managing Urban Expansion for More Equitable Cities in the Global South (World Resources Institute, 2019).Moretto, L. et al. Challenges of water and sanitation service co-production in the global South. Environ. Urban. 30, 425–443 (2018).Article 

Google Scholar 
Seto, K. C., Sánchez-Rodríguez, R. & Fragkias, M. The new geography of contemporary urbanization and the environment. Annu. Rev. Environ. Resour. 35, 167–194 (2010).Article 

Google Scholar 
Estallo, E. L. et al. A decade of arbovirus emergence in the temperate southern cone of South America: dengue, Aedes aegypti and climate dynamics in Córdoba, Argentina. Heliyon 6, e04858 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Kaufman, M. G. & Fonseca, D. M.Invasion biology of Aedes japonicus japonicus (Diptera: Culicidae). Annu. Rev. Entomol. 59, 31–49 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kache, P. A. et al. Environmental determinants of Aedes albopictus abundance at a northern limit of its range in the United States. Am. J. Trop. Med. Hyg. 102, 436–447 (2020).Article 
PubMed 

Google Scholar 
Eskew, E. A. & Olival, K. J. De-urbanization and zoonotic disease risk. EcoHealth 15, 707–712 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Biehler, D. et al. in The Palgrave Handbook of Critical Physical Geography 295–318 (Springer, 2018).Stoddard, S. T. et al. The role of human movement in the transmission of vector-borne pathogens. PLoS Negl. Trop. Dis. 3, e481 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and Chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wong, P. P.-Y., Lai, P.-C., Low, C.-T., Chen, S. & Hart, M. The impact of environmental and human factors on urban heat and microclimate variability. Build. Environ. 95, 199–208 (2016).Article 

Google Scholar 
Rey, J. R. & O’Connell, S. M. Oviposition by Aedes aegypti and Aedes albopictus: influence of congeners and of oviposition site characteristics. J. Vector Ecol. 39, 190–196 (2014).Article 
PubMed 

Google Scholar 
Leisnham, P. T. & Juliano, S. Spatial and temporal patterns of coexistence between competing Aedes mosquitoes in urban Florida. Oecologia 160, 343–352 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Paploski, I. A. D. et al. Storm drains as larval development and adult resting sites for Aedes aegypti and Aedes albopictus in Salvador, Brazil. Parasit. Vectors 9, 419 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Zainon, N., Rahim, F. A. M., Roslan, D. & Abd Samat, A. H. Prevention of Aedes breeding habitats for urban high-rise building in Malaysia. Plan. Malay. 14, 115–128 (2016).
Google Scholar 
Kenneson, A. et al. Social-ecological factors and preventive actions decrease the risk of dengue infection at the household-level: results from a prospective dengue surveillance study in Machala, Ecuador. PLoS Negl. Trop. Dis. 11, e0006150 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Harrington, L. C. et al. Dispersal of the dengue vector Aedes aegypti within and between rural communities. Am. J. Trop. Med. Hyg. 72, 209–220 (2005).Article 
PubMed 

Google Scholar 
Vavassori, L., Saddler, A. & Müller, P. Active dispersal of Aedes albopictus: a mark–release–recapture study using self-marking units. Parasit. Vectors 12, 583 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Ren, H., Wu, W., Li, T. & Yang, Z. Urban villages as transfer stations for dengue fever epidemic: a case study in the Guangzhou, China. PLoS Negl. Trop. Dis. 13, e0007350 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Charron, D. F. in Ecohealth Research in Practice 255–271 (Springer, 2012).Lippi, C. A. et al. Exploring the utility of social–ecological and entomological risk factors for dengue infection as surveillance indicators in the dengue hyper-endemic city of Machala, Ecuador. PLoS Negl. Trop. Dis. 15, e0009257 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Wijayanti, S. P. et al. The importance of socio-economic versus environmental risk factors for reported dengue cases in Java, Indonesia. PLoS Negl. Trop. Dis. 10, e0004964 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Zellweger, R. M. et al. Socioeconomic and environmental determinants of dengue transmission in an urban setting: an ecological study in Nouméa, New Caledonia. PLoS Negl. Trop. Dis. 11, e0005471 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Ryan, S. J. et al. Socio-ecological factors associated with dengue risk and Aedes aegypti presence in the Galápagos Islands, Ecuador. Int. J. Environ. Res. Public Health 16, 682 (2019).Article 
PubMed Central 

Google Scholar 
Roiz, D. et al. Integrated Aedes management for the control of Aedes-borne diseases. PLoS Negl. Trop. Dis. 12, e0006845 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Sanchez, L. et al. Aedes aegypti larval indices and risk for dengue epidemics. Emerg. Infect. Dis. 12, 800–806 (2006).Article 
PubMed 
PubMed Central 

Google Scholar 
Cromwell, E. A. et al. The relationship between entomological indicators of Aedes aegypti abundance and dengue virus infection. PLoS Negl. Trop. Dis. 11, e0005429 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Honório, N. A. et al. Spatial evaluation and modeling of dengue seroprevalence and vector density in Rio de Janeiro, Brazil. PLoS Negl. Trop. Dis. 3, e545 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Chadee, D. Dengue cases and Aedes aegypti indices in Trinidad, West Indies. Acta Trop. 112, 174–180 (2009).Article 
CAS 
PubMed 

Google Scholar 
Fustec, B. et al. Complex relationships between Aedes vectors, socio-economics and dengue transmission—lessons learned from a case-control study in northeastern Thailand. PLoS Negl. Trop. Dis. 14, e0008703 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Scarpino, S. V. & Petri, G. On the predictability of infectious disease outbreaks. Nat. Commun. 10, 898 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Batty, M. in Encyclopedia of Complexity and Systems Science (ed. Meyers, R.) 1041–1071 (Springer, 2009).McPhearson, T., Haase, D., Kabisch, N. & Gren, Å. Advancing understanding of the complex nature of urban systems. Ecol. Indic. 70, 566–573 (2016).Rus, K., Kilar, V. & Koren, D. Resilience assessment of complex urban systems to natural disasters: a new literature review. Int. J. Disaster Risk Reduct. 31, 311–330 (2018).Article 

Google Scholar 
Bettencourt, L. M. Introduction to Urban Science: Evidence and Theory of Cities as Complex Systems (MIT Press, 2021).Handbook for Integrated Vector Management (World Health Organization, 2012).Kolimenakis, A. et al. The role of urbanisation in the spread of Aedes mosquitoes and the diseases they transmit—a systematic review. PLoS Negl. Trop. Dis. 15, e0009631 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Evans, M. V., Bhatnagar, S., Drake, J. M., Murdock, C. C. & Mukherjee, S.Socio‐ecological dynamics in urban systems: an integrative approach to mosquito‐borne disease in Bengaluru, India. People Nat. 4, 730–743 (2022).Article 

Google Scholar 
Cook, E. M., Hall, S. J. & Larson, K. L. Residential landscapes as social–ecological systems: a synthesis of multi-scalar interactions between people and their home environment. Urban Ecosyst. 15, 19–52 (2012).Article 

Google Scholar 
Bai, X., McAllister, R. R., Beaty, R. M. & Taylor, B. Urban policy and governance in a global environment: complex systems, scale mismatches and public participation. Curr. Opin. Environ. Sustain. 2, 129–135 (2010).Article 

Google Scholar 
Batty, M. Inventing Future Cities (MIT Press, 2018).McPhearson, T. et al. Advancing urban ecology toward a science of cities. BioScience 66, 198–212 (2016).Article 

Google Scholar 
Grimm, N. B., Cook, E. M., Hale, R. L. & Iwaniec, D. M. in The Routledge Handbook of Urbanization and Global Environmental Change 227–236 (Routledge, 2015).Haase, D. et al. A quantitative review of urban ecosystem service assessments: concepts, models, and implementation. Ambio 43, 413–433 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Filatova, T., Parker, D. & Van der Veen, A. Agent-based urban land markets: agent’s pricing behavior, land prices and urban land use change. J. Artif. Soc. Soc. Simul. 12, 3 (2009).
Google Scholar 
Acuto, M., Parnell, S. & Seto, K. C. Building a global urban science. Nat. Sustain. 1, 2–4 (2018).Article 

Google Scholar 
Collins, M. & Kapucu, N. Early warning systems and disaster preparedness and response in local government. Disaster Prev. Manag. 17, 587–600 (2008).Article 

Google Scholar 
Ahern, J. From fail-safe to safe-to-fail: sustainability and resilience in the new urban world. Landsc. Urban Plan. 100, 341–343 (2011).Article 

Google Scholar 
Gordon-Larsen, P., Nelson, M. C., Page, P. & Popkin, B. M. Inequality in the built environment underlies key health disparities in physical activity and obesity. Pediatrics 117, 417–424 (2006).Article 
PubMed 

Google Scholar 
Zhou, S. & Lin, R. Spatial–temporal heterogeneity of air pollution: the relationship between built environment and on-road PM2.5 at micro scale. Transp. Res. D Transp. Environ. 76, 305–322 (2019).Article 

Google Scholar 
Frank, L. D. & Engelke, P. Multiple impacts of the built environment on public health: walkable places and the exposure to air pollution. Int. Reg. Sci. Rev. 28, 193–216 (2005).Article 

Google Scholar 
Diuk-Wasser, M. A., VanAcker, M. C. & Fernandez, M. P. Impact of land use changes and habitat fragmentation on the eco-epidemiology of tick-borne diseases. J. Med. Entomol. 58, 1546–1564 (2021).Article 
PubMed 

Google Scholar 
Sengupta, U., Rauws, W. S. & De Roo, G.Planning and complexity: engaging with temporal dynamics, uncertainty and complex adaptive systems. Environ. Plann. B Plann. Des. 43, 970–974 (2016).Article 

Google Scholar 
Shi, Y. et al. Assessment methods of urban system resilience: from the perspective of complex adaptive system theory. Cities 112, 103141 (2021).Article 

Google Scholar 
Holland, J. H. Signals and Boundaries: Building Blocks for Complex Adaptive Systems (MIT Press, 2012).Preiser, R., Biggs, R., De Vos, A. & Folke, C. Social-ecological systems as complex adaptive systems. Ecol. Soc. 23, 46–61 (2018).Article 

Google Scholar 
Levin, S. et al. Social–ecological systems as complex adaptive systems: modeling and policy implications. Environ. Dev. Econ. 18, 111–132 (2013).Article 

Google Scholar 
Waldrop, M. M. Complexity: The Emerging Science at the Edge of Order and Chaos (Simon and Schuster, 1993).Nel, D., du Plessis, C. & Landman, K. Planning for dynamic cities: introducing a framework to understand urban change from a complex adaptive systems approach. Int. Plan. Stud. 23, 250–263 (2018).Article 

Google Scholar 
Sharifi, A. Resilient urban forms: a macro-scale analysis. Cities 85, 1–14 (2019).Article 

Google Scholar 
Borgström, S. T., Elmqvist, T., Angelstam, P. & Alfsen-Norodom, C. Scale mismatches in management of urban landscapes. Ecol. Soc. 11, 16 (2006).Article 

Google Scholar 
Walker, B. H., Carpenter, S. R., Rockstrom, J., Crépin, A.-S. & Peterson, G. D. Drivers, “slow” variables, “fast” variables, shocks, and resilience. Ecol. Soc. 17, 30 (2012).Article 

Google Scholar 
Carpenter, S. R. & Turner, M. G. Hares and tortoises: interactions of fast and slow variables in ecosystems. Ecosystems 3, 495–497 (2000).Article 

Google Scholar 
Peters, D. P., Bestelmeyer, B. T. & Turner, M. G. Cross-scale interactions and changing pattern–process relationships: consequences for system dynamics. Ecosystems 10, 790–796 (2007).Article 

Google Scholar 
Crépin, A.-S. Using fast and slow processes to manage resources with thresholds. Environ. Resour. Econ. 36, 191–213 (2007).Article 

Google Scholar 
Soranno, P. A. et al. Cross‐scale interactions: quantifying multi‐scaled cause–effect relationships in macrosystems. Front. Ecol. Environ. 12, 65–73 (2014).Article 

Google Scholar 
Pickett, S. T. et al. Theoretical perspectives of the Baltimore Ecosystem Study: conceptual evolution in a social–ecological research project. BioScience 70, 297–314 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Gunderson, L. H., Holling, C. S. & Light, S. S. Barriers and Bridges to the Renewal of Ecosystems and Institutions (Columbia Univ. Press, 1995).Turner, M. G., Dale, V. H. & Gardner, R. H. Predicting across scales: theory development and testing. Landsc. Ecol. 3, 245–252 (1989).Article 

Google Scholar 
Wu, J. & Loucks, O. L. From balance of nature to hierarchical patch dynamics: a paradigm shift in ecology. Q. Rev. Biol. 70, 439–466 (1995).Article 

Google Scholar 
Flores, A., Pickett, S. T., Zipperer, W. C., Pouyat, R. V. & Pirani, R. Adopting a modern ecological view of the metropolitan landscape: the case of a greenspace system for the New York City region. Landsc. Urban Plan. 39, 295–308 (1998).Article 

Google Scholar 
Fauchald, P. & Tveraa, T. Hierarchical patch dynamics and animal movement pattern. Oecologia 149, 383–395 (2006).Article 
PubMed 

Google Scholar 
Linton, J. & Budds, J. The hydrosocial cycle: defining and mobilizing a relational–dialectical approach to water. Geoforum 57, 170–180 (2014).Article 

Google Scholar 
Knox, P. & Pinch, S. Urban Social Geography: an Introduction (Routledge, 2014).Geels, F. W. From sectoral systems of innovation to socio-technical systems: insights about dynamics and change from sociology and institutional theory. Res. Policy 33, 897–920 (2004).Article 

Google Scholar 
West, S., Haider, L. J., Stålhammar, S. & Woroniecki, S. A relational turn for sustainability science? Relational thinking, leverage points and transformations. Ecosyst. People 16, 304–325 (2020).Article 

Google Scholar 
Jones, M. Phase space: geography, relational thinking, and beyond. Prog. Hum. Geogr. 33, 487–506 (2009).Article 

Google Scholar 
Wohl, S. Considering how morphological traits of urban fabric create affordances for complex adaptation and emergence. Prog. Hum. Geogr. 40, 30–47 (2016).Article 

Google Scholar 
Herold, M., Scepan, J. & Clarke, K. C. The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environ. Plan. A 34, 1443–1458 (2002).Article 

Google Scholar 
Morrison, A. C. et al. Temporal and geographic patterns of Aedes aegypti (Diptera: Culicidae) production in Iquitos, Peru. J. Med. Entomol. 41, 1123–1142 (2004).Article 
PubMed 

Google Scholar 
LaCon, G. et al. Shifting patterns of Aedes aegypti fine scale spatial clustering in Iquitos, Peru. PLoS Negl. Trop. Dis. 8, e3038 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Lai, S. et al. Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005–2015. PLoS Negl. Trop. Dis. 12, e0006743 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Gergel, S. E. & Turner, M. G. Learning Landscape Ecology: a Practical Guide to Concepts and Techniques (Springer, 2017).Hosseini, P. R. et al. Does the impact of biodiversity differ between emerging and endemic pathogens? The need to separate the concepts of hazard and risk. Phil. Trans. R. Soc. B Biol. Sci. 372, 20160129 (2017).Article 

Google Scholar 
LaDeau, S. L., Allan, B. F., Leisnham, P. T. & Levy, M. Z. The ecological foundations of transmission potential and vector‐borne disease in urban landscapes. Funct. Ecol. 29, 889–901 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Rowley, W. A. & Graham, C. L. The effect of temperature and relative humidity on the flight performance of female Aedes aegypti. J. Insect Physiol. 14, 1251–1257 (1968).Article 
CAS 
PubMed 

Google Scholar 
Evans, M. V. et al. Microclimate and larval habitat density predict adult Aedes albopictus abundance in urban areas. Am. J. Trop. Med. Hyg. 101, 362–370 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Alto, B. W. & Juliano, S. A. Temperature effects on the dynamics of Aedes albopictus (Diptera: Culicidae) populations in the laboratory. J. Med. Entomol. 38, 548–556 (2001).Article 
CAS 
PubMed 

Google Scholar 
Streutker, D. R. A remote sensing study of the urban heat island of Houston, Texas. Int. J. Remote Sens. 23, 2595–2608 (2002).Article 

Google Scholar 
Fikrig, K. et al. Sugar feeding patterns of New York Aedes albopictus mosquitoes are affected by saturation deficit, flowers, and host seeking. PLoS Negl. Trop. Dis. 14, e0008244 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Samson, D. M. et al. Resting and energy reserves of Aedes albopictus collected in common landscaping vegetation in St. Augustine, Florida. J. Am. Mosq. Control Assoc. 29, 231–236 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Grove, J. M., Locke, D. H. & O’Neil-Dunne, J. P. An ecology of prestige in New York City: examining the relationships among population density, socio-economic status, group identity, and residential canopy cover. Environ. Manag. 54, 402–419 (2014).Article 

Google Scholar 
Leong, M., Dunn, R. R. & Trautwein, M. D. Biodiversity and socioeconomics in the city: a review of the luxury effect. Biol. Lett. 14, 20180082 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Aronson, M. F. et al. Biodiversity in the city: key challenges for urban green space management. Front. Ecol. Environ. 15, 189–196 (2017).Article 

Google Scholar 
Hemme, R. R., Thomas, C. L., Chadee, D. D. & Severson, D. W. Influence of urban landscapes on population dynamics in a short-distance migrant mosquito: evidence for the dengue vector Aedes aegypti. PLoS Negl. Trop. Dis. 4, e634 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
García-Betancourt, T., Higuera-Mendieta, D. R., González-Uribe, C., Cortés, S. & Quintero, J. Understanding water storage practices of urban residents of an endemic dengue area in Colombia: perceptions, rationale and socio-demographic characteristics. PLoS ONE 10, e0129054 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Plummer, R., de Loë, R. & Armitage, D. A systematic review of water vulnerability assessment tools. Water Resour. Manag. 26, 4327–4346 (2012).Article 

Google Scholar 
Ledogar, R. J. et al. Mobilising communities for Aedes aegypti control: the SEPA approach. BMC Public Health 17, 103–114 (2017).Article 

Google Scholar 
Michalos, A. C. Encyclopedia of Quality of Life and Well-being Research (Springer Netherlands, 2014).Reiner, R. C. Jr, Stoddard, S. T. & Scott, T. W. Socially structured human movement shapes dengue transmission despite the diffusive effect of mosquito dispersal. Epidemics 6, 30–36 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Whiteford, L. M. The ethnoecology of dengue fever. Med. Anthropol. Q. 11, 202–223 (1997).Article 
CAS 
PubMed 

Google Scholar 
Ibarra, A. M. S. et al. A social–ecological analysis of community perceptions of dengue fever and Aedes aegypti in Machala, Ecuador. BMC Public Health 14, 1135 (2014).Article 

Google Scholar 
Mitchell-Foster, K. L. Interdisciplinary Knowledge Translation and Evaluation Strategies for Participatory Dengue Prevention in Machala, Ecuador. PhD thesis, Univ. British Columbia (2013).Kropf, K.Aspects of urban form. Urban Morphol. 13, 105–120 (2009).Article 

Google Scholar 
Rose, L. A. Topographical constraints and urban land supply indexes. J. Urban Econ. 26, 335–347 (1989).Article 

Google Scholar 
Liu, F. “Interrupted Development”: The Effects of Blighted Neighborhoods and Topographic Barriers on Cities. PhD thesis, George Washington Univ. (2006).Durand-Lasserve, A. & Selod, H. in Urban Land Markets 101–132 (Springer, 2009).Talen, E. City Rules: How Regulations Affect Urban Form (Island Press, 2012).Scheer, B. C. The Evolution of Urban Form: Typology for Planners and Architects (Routledge, 2017).Dimoudi, A., Kantzioura, A., Zoras, S., Pallas, C. & Kosmopoulos, P. Investigation of urban microclimate parameters in an urban center. Energy Build. 64, 1–9 (2013).Article 

Google Scholar 
Middel, A., Häb, K., Brazel, A. J., Martin, C. A. & Guhathakurta, S. Impact of urban form and design on mid-afternoon microclimate in Phoenix local climate zones. Landsc. Urban Plan. 122, 16–28 (2014).Article 

Google Scholar 
Honório, N. A. et al. Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an urban endemic dengue area in the State of Rio de Janeiro, Brazil. Mem. Inst. Oswaldo Cruz 98, 191–198 (2003).Article 
PubMed 

Google Scholar 
Seto, K. C. et al. in Climate Change 2014: Mitigation of Climate Change (eds Edenhofer, O. et al.) 923–1000 (Cambridge Univ. Press, 2014).Romeo-Aznar, V., Freitas, L. P., Cruz, O. G., King, A. & Pascual, M. Fine-scale heterogeneity in population density predicts wave dynamics in dengue epidemics. Nat. Commun. 13, 996 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lafferty, K. D. et al. Local extinction of the Asian tiger mosquito (Aedes albopictus) following rat eradication on Palmyra Atoll. Biol. Lett. 14, 20170743 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez, M. C., Dupont-Courtade, L. & Oueslati, W. Air pollution and urban structure linkages: evidence from European cities. Renew. Sustain. Energy Rev. 53, 1–9 (2016).Article 

Google Scholar 
Venter, Z. S., Krog, N. H. & Barton, D. N. Linking green infrastructure to urban heat and human health risk mitigation in Oslo, Norway. Sci. Total Environ. 709, 136193 (2020).Article 
CAS 
PubMed 

Google Scholar 
Little, E., Barrera, R., Seto, K. C. & Diuk-Wasser, M. Co-occurrence patterns of the dengue vector Aedes aegypti and Aedes mediovitattus, a dengue competent mosquito in Puerto Rico. EcoHealth 8, 365–375 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Pereira dos Santos, T. et al. Potential of Aedes albopictus as a bridge vector for enzootic pathogens at the urban–forest interface in Brazil. Emerg. Microbes Infect. 7, 191 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Cardoso, J. et al. Yellow fever virus in Haemagogus leucocelaenus and Aedes serratus mosquitoes, southern Brazil, 2008. Emerg. Infect. Dis. 16, 1918–1924 (2010).Article 
PubMed Central 

Google Scholar 
Grobbelaar, A. A. et al. Resurgence of yellow fever in Angola, 2015–2016. Emerg. Infect. Dis. 22, 1854–1855 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Tonkiss, F. Cities by Design: the Social Life of Urban Form (John Wiley & Sons, 2014).Hillier, B., Greene, M. & Desyllas, J. Self-generated neighbourhoods: the role of urban form in the consolidation of informal settlements. Urban Des. Int. 5, 61–96 (2000).Article 

Google Scholar 
Li, X., Mou, Y., Wang, H., Yin, C. & He, Q. How does polycentric urban form affect urban commuting? Quantitative measurement using geographical big data of 100 cities in China. Sustainability 10, 4566 (2018).Article 

Google Scholar 
Wen, T.-H., Lin, M.-H., Teng, H.-J. & Chang, N.-T. Incorporating the human–Aedes mosquito interactions into measuring the spatial risk of urban dengue fever. Appl. Geogr. 62, 256–266 (2015).Article 

Google Scholar 
Achee, N. L. et al. A critical assessment of vector control for dengue prevention. PLoS Negl. Trop. Dis. 9, e0003655 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Scott, T. W. & Morrison, A. C. Vector dynamics and transmission of dengue virus: implications for dengue surveillance and prevention strategies: vector dynamics and dengue prevention. Curr. Top. Microbiol. Immunol. 338, 115–128 (2010).PubMed 

Google Scholar 
Delmelle, E., Kim, C., Xiao, N. & Chen, W. Methods for space–time analysis and modeling: an overview. Int. J. Appl. Geospat. Res. 4, 1–18 (2013).Article 

Google Scholar 
Kua, K. P. & Lee, S. W. H. Randomized trials of housing interventions to prevent malaria and Aedes-transmitted diseases: a systematic review and meta-analysis. PLoS ONE 16, e0244284 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chareonviriyaphap, T. et al. The use of an experimental hut for evaluating the entering and exiting behavior of Aedes aegypti (Diptera: Culicidae), a primary vector of dengue in Thailand. J. Vector Ecol. 30, 344–346 (2005).PubMed 

Google Scholar 
Maneerat, S. & Daudé, E. A spatial agent-based simulation model of the dengue vector Aedes aegypti to explore its population dynamics in urban areas. Ecol. Model. 333, 66–78 (2016).Article 

Google Scholar 
Barbu, C. M. et al. The effects of city streets on an urban disease vector. PLoS Comput. Biol. 9, e1002801 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stewart Ibarra, A. M. et al. Dengue vector dynamics (Aedes aegypti) influenced by climate and social factors in Ecuador: implications for targeted control. PLoS ONE 8, e78263 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Mesch, G. S. & Manor, O. Social ties, environmental perception, and local attachment. Environ. Behav. 30, 504–519 (1998).Article 

Google Scholar 
Matthews, L. & Haydon, D. Introduction. Cross-scale influences on epidemiological dynamics: from genes to ecosystems. J. R. Soc. Interface 4, 763–765 (2007).Article 
PubMed Central 

Google Scholar 
Strauss, A. T., Shoemaker, L. G., Seabloom, E. W. & Borer, E. T. Cross‐scale dynamics in community and disease ecology: relative timescales shape the community ecology of pathogens. Ecology 100, e02836 (2019).Article 
PubMed 

Google Scholar 
Schreiber, S. J. et al. Cross-scale dynamics and the evolutionary emergence of infectious diseases. Virus Evol. 7, veaa105 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ramalho, C. E. & Hobbs, R. J. Time for a change: dynamic urban ecology. Trends Ecol. Evol. 27, 179–188 (2012).Article 
PubMed 

Google Scholar 
Waggoner, J. J. et al. Homotypic dengue virus reinfections in Nicaraguan children. J. Infect. Dis. 214, 986–993 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ezeakacha, N. F. & Yee, D. A. The role of temperature in affecting carry-over effects and larval competition in the globally invasive mosquito Aedes albopictus. Parasit. Vectors 12, 123 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Evans, M. V. et al. Carry-over effects of urban larval environments on the transmission potential of dengue-2 virus. Parasit. Vectors 11, 426 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Lowe, R. et al. Combined effects of hydrometeorological hazards and urbanisation on dengue risk in Brazil: a spatiotemporal modelling study. Lancet Planet. Health 5, e209–e219 (2021).Article 
PubMed 

Google Scholar 
Chen, S.-C. et al. Lagged temperature effect with mosquito transmission potential explains dengue variability in southern Taiwan: insights from a statistical analysis. Sci. Total Environ. 408, 4069–4075 (2010).Article 
CAS 
PubMed 

Google Scholar 
Elsinga, J. et al. Knowledge, attitudes, and preventive practices regarding dengue in Maracay, Venezuela. Am. J. Trop. Med. Hyg. 99, 195–203 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Wong, L. P., Shakir, S. M. M., Atefi, N. & AbuBakar, S. Factors affecting dengue prevention practices: nationwide survey of the Malaysian public. PLoS ONE 10, e0122890 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Des Roches, S. et al. Socio‐eco‐evolutionary dynamics in cities. Evol. Appl. 14, 248–267 (2021).Article 
PubMed 

Google Scholar 
Pickett, S. T. et al. Urban ecological systems: linking terrestrial ecological, physical, and socioeconomic components of metropolitan areas. Annu. Rev. Ecol. Syst. 32, 127–157 (2001).Article 

Google Scholar 
Combs, M. A. et al. Socio‐ecological drivers of multiple zoonotic hazards in highly urbanized cities. Glob. Change Biol. 28, 1705–1724 (2022).Article 
CAS 

Google Scholar 
Zhou, Q.A review of sustainable urban drainage systems considering the climate change and urbanization impacts. Water 6, 976–992 (2014).Article 

Google Scholar 
Stewart-Ibarra, A. M. et al. Co-developing climate services for public health: stakeholder needs and perceptions for the prevention and control of Aedes-transmitted diseases in the Caribbean. PLoS Negl. Trop. Dis. 13, e0007772 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hastings, A. Timescales, dynamics, and ecological understanding. Ecology 91, 3471–3480 (2010).Article 
PubMed 

Google Scholar 
Lippi, C. A. et al. A network analysis framework to improve the delivery of mosquito abatement services in Machala, Ecuador. Int. J. Health Geogr. 19, 3 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Projection of the Ecuadorian Population, per Calendar Years, by Cantons 2010–2020 (National Institute of Statistics and Census, 2012).Pertumbuhan Ekonomi Indonesia Triwulan II (Badann Pusat Statistik, 2021).Rašić, G. et al. Aedes aegypti has spatially structured and seasonally stable populations in Yogyakarta, Indonesia. Parasit. Vectors 8, 610 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Schmidt, T. L. et al. Genome-wide SNPs reveal the drivers of gene flow in an urban population of the Asian tiger mosquito, Aedes albopictus. PLoS Negl. Trop. Dis. 11, e0006009 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Schmidt, T. L., Filipović, I., Hoffmann, A. A. & Rašić, G. Fine-scale landscape genomics helps explain the slow spatial spread of Wolbachia through the Aedes aegypti population in Cairns, Australia. Heredity 120, 386–395 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tantowijoyo, W. et al. Stable establishment of wMel Wolbachia in Aedes aegypti populations in Yogyakarta, Indonesia. PLoS Negl. Trop. Dis. 14, e0008157 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Telle, O. et al. The spread of dengue in an endemic urban milieu—the case of Delhi, India. PLoS ONE 11, e0146539 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Telle, O. et al. Social and environmental risk factors for dengue in Delhi city: a retrospective study. PLoS Negl. Trop. Dis. 15, e0009024 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Stokes, E. C. & Seto, K. C. Characterizing and measuring urban landscapes for sustainability. Environ. Res. Lett. 14, 045002 (2019).Article 

Google Scholar 
Jackson-Smith, D. B. et al. Differentiating urban forms: a neighborhood typology for understanding urban water systems. Cities Environ. 9, 5 (2016).
Google Scholar 
Population Census by Age (Department of Provincial Administration, accessed March 2022); https://stat.bora.dopa.go.th/new_stat/webPage/statByAge.phpSalje, H. et al. Revealing the microscale spatial signature of dengue transmission and immunity in an urban population. Proc. Natl Acad. Sci. USA 109, 9535–9538 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Salje, H. et al. Reconstructing unseen transmission events to infer dengue dynamics from viral sequences. Nat. Commun. 12, 1810 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Chai, B. & Seto, K. C. Conceptualizing and characterizing micro-urbanization: a new perspective applied to Africa. Landsc. Urban Plan. 190, 103595 (2019).Article 

Google Scholar 
Zhu, G., Liu, J., Tan, Q. & Shi, B. Inferring the spatio-temporal patterns of dengue transmission from surveillance data in Guangzhou, China. PLoS Negl. Trop. Dis. 10, e0004633 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Ab Hamid, N. et al. Vertical infestation profile of Aedes in selected urban high-rise residences in Malaysia. Trop. Med. Infect. Dis. 5, 114 (2020).Article 
PubMed Central 

Google Scholar 
Sun, H. et al. Spatio-temporal analysis of the main dengue vector populations in Singapore. Parasit. Vectors 14, 41 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ho, C.-M. et al. Surveillance for dengue fever vectors using ovitraps at Kaohsiung and Tainan in Taiwan. Formos. Entomol. 25, 159–174 (2005).
Google Scholar 
McKenzie, D. & Ray, I. Urban water supply in India: status, reform options and possible lessons. Water Policy 11, 442–460 (2009).Article 

Google Scholar 
Qian, S. S., Cuffney, T. F., Alameddine, I., McMahon, G. & Reckhow, K. H. On the application of multilevel modeling in environmental and ecological studies. Ecology 91, 355–361 (2010).Article 
PubMed 

Google Scholar 
Parham, P. E. et al. Climate, environmental and socio-economic change: weighing up the balance in vector-borne disease transmission. Phil. Trans. R. Soc. B Biol. Sci. 370, 20130551 (2015).Article 

Google Scholar 
Slocum, M. G., Beckage, B., Platt, W. J., Orzell, S. L. & Taylor, W. Effect of climate on wildfire size: a cross-scale analysis. Ecosystems 13, 828–840 (2010).Article 

Google Scholar 
Chiu, C.-H., Wen, T.-H., Chien, L.-C. & Yu, H.-L. A probabilistic spatial dengue fever risk assessment by a threshold-based-quantile regression method. PLoS ONE 9, e106334 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Higuera-Mendieta, D. R., Cortés-Corrales, S., Quintero, J. & González-Uribe, C. KAP surveys and dengue control in Colombia: disentangling the effect of sociodemographic factors using multiple correspondence analysis. PLoS Negl. Trop. Dis. 10, e0005016 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhang, J. H., Yuan, J. & Wang, T. Direct cost of dengue hospitalization in Zhongshan, China: associations with demographics, virus types and hospital accreditation. PLoS Negl. Trop. Dis. 11, e0005784 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Tam, C. C. et al. Estimates of dengue force of infection in children in Colombo, Sri Lanka. PLoS Negl. Trop. Dis. 7, e2259 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. & Castillo-Chavez, C. The role of residence times in two-patch dengue transmission dynamics and optimal strategies. J. Theor. Biol. 374, 152–164 (2015).Article 
PubMed 

Google Scholar 
Adams, B. & Kapan, D. D. Man bites mosquito: understanding the contribution of human movement to vector-borne disease dynamics. PLoS ONE 4, e6763 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Colizza, V. & Vespignani, A. Epidemic modeling in metapopulation systems with heterogeneous coupling pattern: theory and simulations. J. Theor. Biol. 251, 450–467 (2008).Article 
PubMed 

Google Scholar 
Otero, M., Schweigmann, N. & Solari, H. G. A stochastic spatial dynamical model for Aedes aegypti. Bull. Math. Biol. 70, 1297–1325 (2008).Article 
PubMed 

Google Scholar 
Otero, M. & Solari, H. G. Stochastic eco-epidemiological model of dengue disease transmission by Aedes aegypti mosquito. Math. Biosci. 223, 32–46 (2010).Article 
CAS 
PubMed 

Google Scholar 
Li, X. & Liu, X. Embedding sustainable development strategies in agent‐based models for use as a planning tool. Int. J. Geogr. Inf. Sci. 22, 21–45 (2008).Article 

Google Scholar 
Mozaffaree Pour, N. & Oja, T. Urban expansion simulated by integrated cellular automata and agent-based models; an example of Tallinn, Estonia. Urban Sci. 5, 85 (2021).Article 

Google Scholar 
Gilbert, N. Agent-Based Models Vol. 153 (Sage Publications, 2019).Roster, K. & Rodrigues, F. A. Neural networks for dengue prediction: a systematic review. Preprint at https://arxiv.org/abs/2106.12905 (2021).Zhao, N. et al. Machine learning and dengue forecasting: comparing random forests and artificial neural networks for predicting dengue burden at national and sub-national scales in Colombia. PLoS Negl. Trop. Dis. 14, e0008056 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Zhai, Y. et al. Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata. Int. J. Geogr. Inf. Sci. 34, 1475–1499 (2020).Article 

Google Scholar 
Verma, D. & Jana, A. LULC classification methodology based on simple Convolutional Neural Network to map complex urban forms at finer scale: evidence from Mumbai. Preprint at https://arxiv.org/abs/1909.09774 (2019).Djenontin, I. N. S. & Meadow, A. M. The art of co-production of knowledge in environmental sciences and management: lessons from international practice. Environ. Manag. 61, 885–903 (2018).Article 

Google Scholar 
Meschede, C. & Mainka, A. Including citizen participation formats for drafting and implementing local sustainable development strategies. Urban Sci. 4, 13 (2020).Article 

Google Scholar 
Mansfield, R. G., Batagol, B. & Raven, R. “Critical agents of change?”: opportunities and limits to children’s participation in urban planning. J. Plan. Lit. 36, 170–186 (2021).Article 

Google Scholar 
Curtis, A., Quinn, M., Obenauer, J. & Renk, B. M. Supporting local health decision making with spatial video: dengue, Chikungunya and Zika risks in a data poor, informal community in Nicaragua. Appl. Geogr. 87, 197–206 (2017).Article 

Google Scholar 
Norström, A. V. et al. Principles for knowledge co-production in sustainability research. Nat. Sustain. 3, 182–190 (2020).Article 

Google Scholar 
Dickens, L. & Butcher, M. Going public? Re‐thinking visibility, ethics and recognition through participatory research praxis. Trans. Inst. Br. Geogr. 41, 528–540 (2016).Article 

Google Scholar 
Wallerstein, N. et al. Power dynamics in community-based participatory research: a multiple-case study analysis of partnering contexts, histories, and practices. Health Educ. Behav. 46, 19S–32S (2019).Article 
PubMed 

Google Scholar 
Parra, C. et al. Synergies between technology, participation, and citizen science in a community-based dengue prevention program. Am. Behav. Sci. 64, 1850–1870 (2020).Article 

Google Scholar 
Lozano–Fuentes, S. et al. Cell phone-based system (Chaak) for surveillance of immatures of dengue virus mosquito vectors. J. Med. Entomol. 50, 879–889 (2013).Article 
PubMed 

Google Scholar 
Kelvin, A. A. et al. ZIKATracker: a mobile app for reporting cases of ZIKV worldwide. J. Infect. Dev. Ctries. 10, 113–115 (2016).Article 
PubMed 

Google Scholar 
Fernandez, M. P. et al. Usability and feasibility of a smartphone app to assess human behavioral factors associated with tick exposure (The Tick App): quantitative and qualitative study. JMIR mHealth uHealth 7, e14769 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hamer, S. A., Curtis-Robles, R. & Hamer, G. L. Contributions of citizen scientists to arthropod vector data in the age of digital epidemiology. Curr. Opin. Insect Sci. 28, 98–104 (2018).Article 
PubMed 

Google Scholar 
Van Leeuwen, J. P., Hermans, K., Jylhä, A., Quanjer, A. J. & Nijman, H. Effectiveness of virtual reality in participatory urban planning: a case study. In Proc. Media Architecture Biennale 128–136 (Association for Computing Machinery, 2018).Kahila-Tani, M. Reshaping the Planning Process Using Local Experiences: Utilising PPGIS in Participatory Urban Planning. PhD thesis, Aalto Univ. (2015).Iwaniec, D. M. et al. The co-production of sustainable future scenarios. Landsc. Urban Plan. 197, 103744 (2020).Article 

Google Scholar 
Dickin, S. K., Schuster-Wallace, C. J. & Elliott, S. J. Mosquitoes & vulnerable spaces: mapping local knowledge of sites for dengue control in Seremban and Putrajaya Malaysia. Appl. Geogr. 46, 71–79 (2014).Article 

Google Scholar 
Chircop, A., Bassett, R. & Taylor, E. Evidence on how to practice intersectoral collaboration for health equity: a scoping review. Crit. Public Health 25, 178–191 (2015).Article 

Google Scholar 
Gamache, S., Diallo, T. A., Shankardass, K. & Lebel, A. The elaboration of an intersectoral partnership to perform health impact assessment in urban planning: the experience of Quebec City (Canada). Int. J. Environ. Res. Public Health 17, 7556 (2020).Article 
PubMed Central 

Google Scholar 
Herdiana, H., Sari, J. F. K. & Whittaker, M. Intersectoral collaboration for the prevention and control of vector borne diseases to support the implementation of a global strategy: a systematic review. PLoS ONE 13, e0204659 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Lee, S. A., Economou, T., de Castro Catão, R., Barcellos, C. & Lowe, R. The impact of climate suitability, urbanisation, and connectivity on the expansion of dengue in 21st century Brazil. PLoS Negl. Trop. Dis. 15, e0009773 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Johansson, M. A., Cummings, D. A. & Glass, G. E. Multiyear climate variability and dengue—El Nino southern oscillation, weather, and dengue incidence in Puerto Rico, Mexico, and Thailand: a longitudinal data analysis. PLoS Med. 6, e1000168 (2009).Article 
PubMed 
PubMed Central 

Google Scholar 
Barrera, R., Amador, M. & MacKay, A. J. Population dynamics of Aedes aegypti and dengue as influenced by weather and human behavior in San Juan, Puerto Rico. PLoS Negl. Trop. Dis. 5, e1378 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Hess, G. Disease in metapopulation models: implications for conservation. Ecology 77, 1617–1632 (1996).Article 

Google Scholar 
Hanski, I. Metapopulation dynamics: does it help to have more of the same? Trends Ecol. Evol. 4, 113–114 (1989).Article 
CAS 
PubMed 

Google Scholar 
Masui, H. et al. Assessing potential countermeasures against the dengue epidemic in non-tropical urban cities. Theor. Biol. Med. Model. 13, 12 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Stone, C. M., Schwab, S. R., Fonseca, D. M. & Fefferman, N. H. Contrasting the value of targeted versus area-wide mosquito control scenarios to limit arbovirus transmission with human mobility patterns based on different tropical urban population centers. PLoS Negl. Trop. Dis. 13, e0007479 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
O’Reilly, K. M. et al. Projecting the end of the Zika virus epidemic in Latin America: a modelling analysis. BMC Med. 16, 180 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Santé, I., García, A. M., Miranda, D. & Crecente, R. Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc. Urban Plan. 96, 108–122 (2010).Article 

Google Scholar 
Yang, J., Gong, J., Tang, W. & Liu, C. Patch-based cellular automata model of urban growth simulation: integrating feedback between quantitative composition and spatial configuration. Comput. Environ. Urban Syst. 79, 101402 (2020).Article 

Google Scholar 
Rozos, E., Butler, D. & Makropoulos, C. An integrated system dynamics–cellular automata model for distributed water-infrastructure planning. Water Sci. Technol. Water Supply 16, 1519–1527 (2016).Article 

Google Scholar 
Enduri, M. K. & Jolad, S. Dynamics of dengue disease with human and vector mobility. Spat. Spatiotemporal Epidemiol. 25, 57–66 (2018).Article 
PubMed 

Google Scholar 
Medeiros, L. C. et al. Modeling the dynamic transmission of dengue fever: investigating disease persistence. PLoS Negl. Trop. Dis. 5, e942 (2011).Article 
PubMed Central 

Google Scholar 
Ali, A. M., Shafiee, M. E. & Berglund, E. Z. Agent-based modeling to simulate the dynamics of urban water supply: climate, population growth, and water shortages. Sustain. Cities Soc. 28, 420–434 (2017).Article 

Google Scholar 
Philippon, D. et al. in Multi-Agent Based Simulation XVII. MABS 2016. Lecture Notes in Computer Science Vol 10399 (eds Nardin, L. & Antunes, L.) 111–127 (Springer, 2016).Agyemang, F. S., Silva, E. & Fox, S.Modelling and simulating ‘informal urbanization’: an integrated agent-based and cellular automata model of urban residential growth in Ghana. Urban Anal. City Sci. 0, 1–15 (2022).
Google Scholar 
Chouhan, S. S., Kaul, A. & Singh, U. P. Image segmentation using computational intelligence techniques. Arch. Comput. Methods Eng. 26, 533–596 (2019).Article 

Google Scholar 
Andersson, V. O., Birck, M. A. F. & Araujo, R. M. Towards predicting dengue fever rates using convolutional neural networks and street-level images. Proc. 2018 Int. Jt Conf. Neural Netw. 1–8 (IEEE, 2018).Chrysler, A., Gunarso, R., Puteri, T. & Warnars, H. A Literature Review of Crowd-Counting System on Convolutional Neural Network 012029 (IOP Conference Series: Earth and Environmental Science Volume 729, IOP Publishing, 2021).Bharambe, A., Chandorkar, A. A. & Kalbande, D. A deep learning approach for dengue tweet classification. Proc. 3rd Int. Conf. Invent. Res. Comput. Appl. 1043–1047 (IEEE, 2021).Kumar, A. & Garg, G. Sentiment analysis of multimodal twitter data. Multimed. Tools Appl. 78, 24103–24119 (2019).Article 

Google Scholar 
Marin, A. & Wellman, B. in The SAGE Handbook of Social Network Analysis Ch. 2 (2011).Snijders, T. A. & Steglich, C. E. Representing micro–macro linkages by actor-based dynamic network models. Sociol. Methods Res. 44, 222–271 (2015).Article 
PubMed 

Google Scholar 
Warren, C. R., Burton, R., Buchanan, O. & Birnie, R. V. Limited adoption of short rotation coppice: the role of farmers’ socio-cultural identity in influencing practice. J. Rural Stud. 45, 175–183 (2016).Article 

Google Scholar 
Beal Cohen, A. A., Muneepeerakul, R. & Kiker, G. Intra-group decision-making in agent-based models. Sci. Rep. 11, 17709 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Frederiks, E. R., Stenner, K. & Hobman, E. V. Household energy use: applying behavioural economics to understand consumer decision-making and behaviour. Renew. Sustain. Energy Rev. 41, 1385–1394 (2015).Article 

Google Scholar 
Spiegel, J. et al. Barriers and bridges to prevention and control of dengue: the need for a social–ecological approach. EcoHealth 2, 273–290 (2005).Article 

Google Scholar 
Arellano, C. et al. Knowledge and beliefs about dengue transmission and their relationship with prevention practices in Hermosillo, Sonora. Front. Public Health 3, 142 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gertler, M. S. & Wolfe, D. A. Local social knowledge management: community actors, institutions and multilevel governance in regional foresight exercises. Futures 36, 45–65 (2004).Article 

Google Scholar 
Brown, R. R., Farrelly, M. A. & Loorbach, D. A. Actors working the institutions in sustainability transitions: the case of Melbourne’s stormwater management. Glob. Environ. Change 23, 701–718 (2013).Article 

Google Scholar 
Castilla-Rho, J. C., Mariethoz, G., Rojas, R., Andersen, M. S. & Kelly, B. F. An agent-based platform for simulating complex human–aquifer interactions in managed groundwater systems. Environ. Model. Softw. 73, 305–323 (2015).Article 

Google Scholar 
Sabatier, P. A. Toward better theories of the policy process. PS Polit. Sci. Polit. 24, 147–156 (1991).Article 

Google Scholar 
Abrantes, P. et al. Modelling urban form: a multidimensional typology of urban occupation for spatial analysis. Environ. Plan. B Urban Anal. City Sci. 46, 47–65 (2019).Article 

Google Scholar 
McGarigal, K. FRAGSTATS: Spatial Pattern Analysis Program for Quantifying Landscape Structure Vol. 351 (US Department of Agriculture, Forest Service, Pacific Northwest Research Station, 1995).Vazquez-Prokopec, G. M. et al. Using GPS technology to quantify human mobility, dynamic contacts and infectious disease dynamics in a resource-poor urban environment. PLoS ONE 8, e58802 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ligtenberg, A., van Lammeren, R. J., Bregt, A. K. & Beulens, A. J. Validation of an agent-based model for spatial planning: a role-playing approach. Comput. Environ. Urban Syst. 34, 424–434 (2010).Article 

Google Scholar  More

Dehling, D. M., Jordano, P., Schaefer, H. M., Böhning-Gaese, K. & Schleuning, M. Morphology predicts species’ functional roles and their degree of specialization in plant–frugivore interactions. Proc. R. Soc. B 283, 20152444 (2016).PubMed 
PubMed Central 

Google Scholar 
Grant, P. R. Inheritance of size and shape in a population of Darwin’s finches, Geospiza conirostris. Proc. R. Soc. Lond. B 220, 219–236 (1983).
Google Scholar 
Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).PubMed 

Google Scholar 
Bergmann, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Gött. Stud. 3, 595–708 (1847).
Google Scholar 
Allen, J. A. The influence of physical conditions in the genesis of species. Radic. Rev. 1, 108–140 (1877).
Google Scholar 
Altshuler, D. L. & Dudley, R. The physiology and biomechanics of avian flight at high altitude. Integr. Comp. Biol. 46, 62–71 (2006).PubMed 

Google Scholar 
Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: is there any evidence? Evol. Appl. 7, 156–168 (2014).PubMed 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).PubMed 

Google Scholar 
Yom-Tov, Y., Yom-Tov, S., Wright, J., Thorne, C. J. R. & Du Feu, R. Recent changes in body weight and wing length among some British passerine birds. Oikos 112, 91–101 (2006).
Google Scholar 
Van Buskirk, J., Mulvihill, R. S. & Leberman, R. C. Declining body sizes in North American birds associated with climate change. Oikos 119, 1047–1055 (2010).
Google Scholar 
Weeks, B. C. et al. Shared morphological consequences of global warming in North American migratory birds. Ecol. Lett. 23, 316–325 (2020).PubMed 

Google Scholar 
Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).CAS 
PubMed 

Google Scholar 
DeSante, D. F., Saracco, J. F., O’Grady, D. R., Burton, K. M. & Walker, B. L. Methodological considerations of the Monitoring Avian Productivity and Survivorship (MAPS) program. Stud. Avian Biol. 29, 28–45 (2004).West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).CAS 
PubMed 

Google Scholar 
Jirinec, V. et al. Morphological consequences of climate change for resident birds in intact Amazonian rainforest. Sci. Adv. 7, eabk1743 (2021).PubMed 
PubMed Central 

Google Scholar 
Dubiner, S. & Meiri, S. Widespread recent changes in morphology of Old World birds, global warming the immediate suspect. Glob. Ecol. Biogeogr. 31, 791–801 (2022).
Google Scholar 
Ballinger, M. A. & Nachman, M. W. The contribution of genetic and environmental effects to Bergmann’s rule and Allen’s rule in house mice. Am. Nat. https://doi.org/10.1086/719028 (2022).Andrew, S. C., Hurley, L. L., Mariette, M. M. & Griffith, S. C. Higher temperatures during development reduce body size in the zebra finch in the laboratory and in the wild. J. Evol. Biol. 30, 2156–2164 (2017).CAS 
PubMed 

Google Scholar 
Siepielski, A. M. et al. No evidence that warmer temperatures are associated with selection for smaller body sizes. Proc. R. Soc. B 286, 20191332 (2019).PubMed 
PubMed Central 

Google Scholar 
Salewski, V., Siebenrock, K.-H., Hochachka, W. M., Woog, F. & Fiedler, W. Morphological change to birds over 120 years is not explained by thermal adaptation to climate change. PLoS ONE 9, e101927 (2014).PubMed 
PubMed Central 

Google Scholar 
Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl Acad. Sci. USA 116, 21609–21615 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 

Google Scholar 
Futuyma, D. J. Evolutionary constraint and ecological consequences. Evolution 64, 1865–1884 (2010).PubMed 

Google Scholar 
Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rollinson, C. R. et al. Working across space and time: nonstationarity in ecological research and application. Front. Ecol. Environ. 19, 66–72 (2021).
Google Scholar 
Riemer, K., Guralnick, R. P. & White, E. P. No general relationship between mass and temperature in endothermic species. eLife 7, e27166 (2018).PubMed 
PubMed Central 

Google Scholar 
Ryding, S., Klaassen, M., Tattersall, G. J., Gardner, J. L. & Symonds, M. R. Shape-shifting: changing animal morphologies as a response to climatic warming. Trends Ecol. Evol. 36, 1036–1048 (2021).PubMed 

Google Scholar 
Baldwin, M. W., Winkler, H., Organ, C. L. & Helm, B. Wing pointedness associated with migratory distance in common-garden and comparative studies of stonechats (Saxicola torquata). J. Evol. Biol. 23, 1050–1063 (2010).CAS 
PubMed 

Google Scholar 
Förschler, M. I. & Bairlein, F. Morphological shifts of the external flight apparatus across the range of a passerine (Northern Wheatear) with diverging migratory behaviour. PLoS ONE 6, e18732 (2011).PubMed 
PubMed Central 

Google Scholar 
Macpherson, M. P., Jahn, A. E. & Mason, N. A. Morphology of migration: associations between wing shape, bill morphology and migration in kingbirds (Tyrannus). Biol. J. Linn. Soc. 135, 71–83 (2022).
Google Scholar 
Newton, I. The Migration Ecology of Birds (Elsevier, 2010).Clegg, S. M., Kelly, J. F., Kimura, M. & Smith, T. B. Combining genetic markers and stable isotopes to reveal population connectivity and migration patterns in a neotropical migrant, Wilson’s warbler (Wilsonia pusilla). Mol. Ecol. 12, 819–830 (2003).CAS 
PubMed 

Google Scholar 
Bell, C. P. Leap-frog migration in the fox sparrow: minimizing the cost of spring migration. Condor 99, 470–477 (1997).
Google Scholar 
Billerman, S., Keeney, B., Rodewald, P. & Schulenberg, T. (eds) Birds of the World (Cornell Laboratory of Ornithology, 2020).Desrochers, A. Morphological response of songbirds to 100 years of landscape change in North America. Ecology 91, 1577–1582 (2010).CAS 
PubMed 

Google Scholar 
Swaddle, J. P. & Lockwood, R. Morphological adaptations to predation risk in passerines. J. Avian Biol. 29, 172–176 (1998).
Google Scholar 
Chown, S. L. & Klok, C. J. Altitudinal body size clines: latitudinal effects associated with changing seasonality. Ecography 26, 445–455 (2003).
Google Scholar 
Hsiung, A. C., Boyle, W. A., Cooper, R. J. & Chandler, R. B. Altitudinal migration: ecological drivers, knowledge gaps, and conservation implications: animal altitudinal migration review. Biol. Rev. 93, 2049–2070 (2018).PubMed 

Google Scholar 
Barras, A. G., Liechti, F. & Arlettaz, R. Seasonal and daily movement patterns of an alpine passerine suggest high flexibility in relation to environmental conditions. J. Avian Biol. 52, jav.02860 (2021).
Google Scholar 
Spence, A. R. & Tingley, M. W. Body size and environment influence both intraspecific and interspecific variation in daily torpor use across hummingbirds. Funct. Ecol. 35, 870–883 (2021).CAS 

Google Scholar 
Moreau, R. E. Variation in the western Zosteropidae (Aves). Bull. Br. Mus. Nat. Hist. Zool. 4, 311–433 (1957).
Google Scholar 
Hamilton, T. H. The adaptive significances of intraspecific trends of variation in wing length and body size among bird species. Evolution 15, 180–194 (1961).
Google Scholar 
Hodkinson, I. D. Terrestrial insects along elevation gradients: species and community responses to altitude. Biol. Rev. 80, 489–513 (2005).PubMed 

Google Scholar 
Feinsinger, P., Colwell, R. K., Terborgh, J. & Chaplin, S. B. Elevation and the morphology, flight energetics, and foraging ecology of tropical hummingbirds. Am. Nat. 113, 481–497 (1979).
Google Scholar 
Aldrich, J. W. Ecogeographical Variation in Size and Proportions of Song Sparrows (Melospiza melodia) (American Ornithological Society, 1984).Sun, Y. et al. The role of climate factors in geographic variation in body mass and wing length in a passerine bird. Avian Res. 8, 1 (2017).Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582 (2021).PubMed 

Google Scholar 
McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).PubMed 

Google Scholar 
Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).PubMed 
PubMed Central 

Google Scholar 
Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).CAS 
PubMed 

Google Scholar 
Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Birds track their Grinnellian niche through a century of climate change. Proc. Natl Acad. Sci. USA 106, 19637–19643 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Youngflesh, C. et al. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol. 5, 987–994 (2021).PubMed 

Google Scholar 
Blueweiss, L. et al. Relationships between body size and some life history parameters. Oecologia 37, 257–272 (1978).CAS 
PubMed 

Google Scholar 
Kleiber, M. Body size and metabolic rate. Physiol. Rev. 27, 511–541 (1947).CAS 
PubMed 

Google Scholar 
Yodzis, P. & Innes, S. Body size and consumer-resource dynamics. Am. Nat. 139, 1151–1175 (1992).
Google Scholar 
Prum, R. O. Interspecific social dominance mimicry in birds: social mimicry in birds. Zool. J. Linn. Soc. 172, 910–941 (2014).
Google Scholar 
Pyle, P. Identification Guide to North American Birds: A Compendium of Information on Identifying, Ageing, and Sexing ‘Near-Passerines’ and Passerines in the Hand (Slate Creek Press, 1997).Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
Google Scholar 
Danielson, J. J. & Gesch, D. B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) (US Geological Survey, 2011).Thornton, M. M. et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 (ORNL Distributed Active Archive Center, 2020).Greenewalt, C. H. The flight of birds: the significant dimensions, their departure from the requirements for dimensional similarity, and the effect on flight aerodynamics of that departure. Trans. Am. Philos. Soc. 65, 1–67 (1975).
Google Scholar 
Longo, G. & Montévil, M. Perspectives on Organisms: Biological Time, Symmetries, and Singularities (Springer, 2014).Harvey, P. H. in Scaling in Biology (eds Brown, J. H. & West, G. B.) 253–265 (Oxford Univ. Press, 2000).Orme, D. et al. The caper package: comparative analysis of phylogenetics and evolution in R. R package version 5 (2013).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 

Google Scholar 
Nudds, R. L., Kaiser, G. W. & Dyke, G. J. Scaling of avian primary feather length. PLoS ONE 6, e15665 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nudds, R. Wing-bone length allometry in birds. J. Avian Biol. 38, 515–519 (2007).
Google Scholar 
Anderson, S. C., Branch, T. A., Cooper, A. B. & Dulvy, N. K. Black-swan events in animal populations. Proc. Natl Acad. Sci. USA 114, 3252–3257 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0 (Stan Development Team, 2018); http://mc-stan.orgCarpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).Youngflesh, C. MCMCvis: tools to visualize, manipulate, and summarize MCMC output. J. Open Source Softw. 3, 640 (2018).
Google Scholar 
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
Google Scholar 
Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. A 182, 389–402 (2019).
Google Scholar 
McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2018).Data Zone (BirdLife International, 2019); http://datazone.birdlife.org/species/requestdisCramp, S. & Brooks, D. Handbook of the Birds of Europe, the Middle East and North Africa. The Birds of the Western Palearctic, Vol. VI. Warblers (Oxford Univ. Press, 1992).Che-Castaldo, J., Che-Castaldo, C. & Neel, M. C. Predictability of demographic rates based on phylogeny and biological similarity. Conserv. Biol. 32, 1290–1300 (2018).PubMed 

Google Scholar 
Villemereuil, P., de, Wells, J. A., Edwards, R. D. & Blomberg, S. P. Bayesian models for comparative analysis integrating phylogenetic uncertainty. BMC Evol. Biol. 12, 102 (2012).PubMed 
PubMed Central 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
PubMed 

Google Scholar 
Hendry, A. P. & Kinnison, M. T. Perspective: the pace of modern life: measuring rates of contemporary microevolution. Evolution 53, 1637–1653 (1999).PubMed 

Google Scholar 
Gingerich, P. Rates of evolution: effects of time and temporal scaling. Science 222, 159–162 (1983).CAS 
PubMed 

Google Scholar 
Bird, J. P. et al. Generation lengths of the world’s birds and their implications for extinction risk. Conserv. Biol. 34, 1252–1261 (2020).Gingerich, P. D. Rates of evolution. Annu. Rev. Ecol. Evol. Syst. 40, 657–675 (2009).
Google Scholar 
Bürger, R. & Lynch, M. Evolution and extinction in a changing environment: a quantitative-genetic analysis. Evolution 49, 151–163 (1995).PubMed 

Google Scholar 
Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29 (2008).PubMed 

Google Scholar  More

Intergovernmental Panel on Climate Change (IPCC). The Ocean and Cryosphere in a Changing Climate: Special Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2022). https://doi.org/10.1017/9781009157964.Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37 (2012).ADS 

Google Scholar 
Gattuso, J.-P. et al. Contrasting futures for ocean and society from different anthropogenic CO2 emissions scenarios. Science 349, aac4722 (2015).PubMed 

Google Scholar 
Hall-Spencer, J. M. & Harvey, B. P. Ocean acidification impacts on coastal ecosystem services due to habitat degradation. Emerg. Top. Life Sci. 3, 197–206 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Straub, S. C. et al. Resistance, extinction, and everything in between—The diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).
Google Scholar 
Connell, S. D., Kroeker, K. J., Fabricius, K. E., Kline, D. I. & Russell, B. D. The other ocean acidification problem: CO2 as a resource among competitors for ecosystem dominance. Philos. Trans. R. Soc. B Biol. Sci. 368, 20120442 (2013).
Google Scholar 
Kroeker, K. J., Micheli, F., Gambi, M. C. & Martz, T. R. Divergent ecosystem responses within a benthic marine community to ocean acidification. Proc. Natl. Acad. Sci. 108, 14515–14520 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Harvey, B. P., Kon, K., Agostini, S., Wada, S. & Hall-Spencer, J. M. Ocean acidification locks algal communities in a species-poor early successional stage. Glob. Change Biol. 27, 2174–2187 (2021).ADS 
CAS 

Google Scholar 
Sunday, J. M. et al. Ocean acidification can mediate biodiversity shifts by changing biogenic habitat. Nat. Clim. Change 7, 81–85 (2017).ADS 
CAS 

Google Scholar 
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).
Google Scholar 
Schiel, D. R. & Foster, M. S. The population biology of large brown seaweeds: Ecological consequences of multiphase life histories in dynamic coastal environments. Annu. Rev. Ecol. Evol. Syst. 37, 343–372 (2006).
Google Scholar 
Wernberg, T. & Filbee-Dexter, K. Missing the marine forest for the trees. Mar. Ecol. Prog. Ser. 612, 209–215 (2019).ADS 

Google Scholar 
Cheminée, A. et al. Nursery value of Cystoseira forests for Mediterranean rocky reef fishes. J. Exp. Mar. Biol. Ecol. 442, 70–79 (2013).
Google Scholar 
Smale, D. A., Burrows, M. T., Moore, P., O’Connor, N. & Hawkins, S. J. Threats and knowledge gaps for ecosystem services provided by kelp forests: A northeast Atlantic perspective. Ecol. Evol. 3, 4016–4038 (2013).PubMed 
PubMed Central 

Google Scholar 
Carbajal, P., Gamarra Salazar, A., Moore, P. J. & Pérez-Matus, A. Different kelp species support unique macroinvertebrate assemblages, suggesting the potential community-wide impacts of kelp harvesting along the Humboldt Current System. Aquat. Conserv. Mar. Freshw. Ecosyst. 32, 14–27 (2022).
Google Scholar 
Filbee-Dexter, K. & Wernberg, T. Rise of turfs: A new battlefront for globally declining kelp forests. Bioscience 68, 64–76 (2018).
Google Scholar 
Pessarrodona, A. et al. Homogenization and miniaturization of habitat structure in temperate marine forests. Glob. Change Biol. 27, 5262–5275 (2021).CAS 

Google Scholar 
Orfanidis, S. et al. Effects of natural and anthropogenic stressors on Fucalean brown seaweeds across different spatial scales in the Mediterranean Sea. Front. Mar. Sci. 8, 1330 (2021).
Google Scholar 
Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. Proc. Natl. Acad. Sci. 113, 13785–13790 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Capdevila, P. et al. Warming impacts on early life stages increase the vulnerability and delay the population recovery of a long-lived habitat-forming macroalga. J. Ecol. 107, 1129–1140 (2019).
Google Scholar 
Irving, A. D., Balata, D., Colosio, F., Ferrando, G. A. & Airoldi, L. Light, sediment, temperature, and the early life-history of the habitat-forming alga Cystoseira barbata. Mar. Biol. 156, 1223–1231 (2009).
Google Scholar 
Smith, K. E., Moore, P. J., King, N. G. & Smale, D. A. Examining the influence of regional-scale variability in temperature and light availability on the depth distribution of subtidal kelp forests. Limnol. Oceanogr. 67, 314–328 (2022).ADS 

Google Scholar 
Smale, D. A. et al. Climate-driven substitution of foundation species causes breakdown of a facilitation cascade with potential implications for higher trophic levels. J. Ecol. 00, 1–13 (2022).
Google Scholar 
Hollarsmith, J. A., Buschmann, A. H., Camus, C. & Grosholz, E. D. Varying reproductive success under ocean warming and acidification across giant kelp (Macrocystis pyrifera) populations. J. Exp. Mar. Biol. Ecol. 522, 151247 (2020).
Google Scholar 
Verdura, J. et al. Local-scale climatic refugia offer sanctuary for a habitat-forming species during a marine heatwaves. J. Ecol. 109, 1758–1773 (2021).
Google Scholar 
Mariani, S. et al. Past and present of Fucales from shallow and sheltered shores in Catalonia. Reg. Stud. Mar. Sci. 32, 100824 (2019).
Google Scholar 
Smale, D. A. Impacts of ocean warming on kelp forest ecosystems. New Phytol. 225, 1447–1454 (2020).PubMed 

Google Scholar 
Coelho, S. M., Rijstenbil, J. W. & Brown, M. T. Impacts of anthropogenic stresses on the early development stages of seaweeds. J. Aquat. Ecosyst. Stress Recov. 7, 317–333 (2000).CAS 

Google Scholar 
de Caralt, S., Verdura, J., Vergés, A., Ballesteros, E. & Cebrian, E. Differential effects of pollution on adult and recruits of a canopy-forming alga: Implications for population viability under low pollutant levels. Sci. Rep. 10, 17825 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Capdevila, P. et al. Recruitment patterns in the Mediterranean deep-water alga Cystoseira zosteroides. Mar. Biol. 162, 1165–1174 (2015).CAS 

Google Scholar 
Vadas, R. L., Johnson, S. & Norton, T. A. Recruitment and mortality of early post-settlement stages of benthic algae. Br. Phycol. J. 27, 331–351 (1992).
Google Scholar 
Koch, M., Bowes, G., Ross, C. & Zhang, X.-H. Climate change and ocean acidification effects on seagrasses and marine macroalgae. Glob. Change Biol. 19, 103–132 (2013).ADS 

Google Scholar 
Shih, P. M. et al. Biochemical characterization of predicted Precambrian RuBisCO. Nat. Commun. 7, 10382 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cornwall, C. E. et al. Inorganic carbon physiology underpins macroalgal responses to elevated CO2. Sci. Rep. 7, 46297 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hepburn, C. D. et al. Diversity of carbon use strategies in a kelp forest community: Implications for a high CO2 ocean. Glob. Change Biol. 17, 2488–2497 (2011).ADS 

Google Scholar 
Porzio, L., Buia, M. C. & Hall-Spencer, J. M. Effects of ocean acidification on macroalgal communities. J. Exp. Mar. Biol. Ecol. 400, 278–287 (2011).CAS 

Google Scholar 
Kroeker, K. J. et al. Impacts of ocean acidification on marine organisms: Quantifying sensitivities and interaction with warming. Glob. Change Biol. 19, 1884–1896 (2013).ADS 

Google Scholar 
Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms: Biological responses to ocean acidification. Ecol. Lett. 13, 1419–1434 (2010).PubMed 

Google Scholar 
Rindi, F. et al. Coralline algae in a changing Mediterranean Sea: How can we predict their future, if we do not know their present?. Front. Mar. Sci. 6, 723 (2019).
Google Scholar 
James, R. K., Hepburn, C. D., Cornwall, C. E., McGraw, C. M. & Hurd, C. L. Growth response of an early successional assemblage of coralline algae and benthic diatoms to ocean acidification. Mar. Biol. 161, 1687–1696 (2014).CAS 

Google Scholar 
Comeau, S. & Cornwall, C. E. Contrasting effects of ocean acidification on coral reef “animal forests” versus seaweed “kelp forests.” In Marine Animal Forests: The Ecology of Benthic Biodiversity Hotspots (eds Rossi, S. et al.) 1–25 (Springer International Publishing, 2016) https://doi.org/10.1007/978-3-319-17001-5_29-1.Chapter 

Google Scholar 
Airoldi, L. Effects of disturbance, life histories, and overgrowth on coexistence of algal crusts and turfs. Ecology 81, 798–814 (2000).
Google Scholar 
Asnaghi, V. et al. Colonisation processes and the role of coralline algae in rocky shore community dynamics. J. Sea Res. 95, 132–138 (2015).ADS 

Google Scholar 
Bulleri, F., Bertocci, I. & Micheli, F. Interplay of encrusting coralline algae and sea urchins in maintaining alternative habitats. Mar. Ecol. Prog. Ser. 243, 101–109 (2002).ADS 

Google Scholar 
Villas Bôas, A. B. & Figueiredo, M. A. D. O. Are anti-fouling effects in coralline algae species specific?. Braz. J. Oceanogr. 52, 11–18 (2004).
Google Scholar 
Bulleri, F., Benedetti-Cecchi, L., Acunto, S., Cinelli, F. & Hawkins, S. J. The influence of canopy algae on vertical patterns of distribution of low-shore assemblages on rocky coasts in the northwest Mediterranean. J. Exp. Mar. Biol. Ecol. 267, 89–106 (2002).
Google Scholar 
Maggi, E., Bertocci, I., Vaselli, S. & Benedetti-Cecchi, L. Connell and Slatyer’s models of succession in the biodiversity era. Ecology 92, 1399–1406 (2011).CAS 
PubMed 

Google Scholar 
Irving, A. D., Connell, S. D., Johnston, E. L., Pile, A. J. & Gillanders, B. M. The response of encrusting coralline algae to canopy loss: An independent test of predictions on an Antarctic coast. Mar. Biol. 147, 1075–1083 (2005).
Google Scholar 
Irving, A. D., Connell, S. D. & Elsdon, T. S. Effects of kelp canopies on bleaching and photosynthetic activity of encrusting coralline algae. J. Exp. Mar. Biol. Ecol. 310, 1–12 (2004).
Google Scholar 
Melville, A. J. & Connell, S. D. Experimental effects of kelp canopies on subtidal coralline algae. Austral. Ecol. 26, 102–108 (2001).
Google Scholar 
Breitburg, D. L. Residual effects of grazing: Inhibition of competitor recruitment by encrusting coralline algae. Ecology 65, 1136–1143 (1984).
Google Scholar 
Bulleri, F., Bruno, J. F., Silliman, B. R. & Stachowicz, J. J. Facilitation and the niche: Implications for coexistence, range shifts and ecosystem functioning. Funct. Ecol. 30, 70–78 (2016).
Google Scholar 
van der Heide, T., Angelini, C., de Fouw, J. & Eklöf, J. S. Facultative mutualisms: A double-edged sword for foundation species in the face of anthropogenic global change. Ecol. Evol. 11, 29–44 (2021).PubMed 

Google Scholar 
Molinari-Novoa, E. A. & Guiry, E. Reinstatement of the genera Gongolaria Boehmer and Ericaria Stackhouse (Sargassaceae, Phaeophyceae). Notulae Algarum 1–10 (2020).Celis-Plá, P. S. M., Martinez, B., Korbee, N., Hall-Spencer, J. M. & Figueroa, F. L. Ecophysiological responses to elevated CO2 and temperature in Cystoseira tamariscifolia (Phaeophyceae). Clim. Change 142, 67–81 (2017).ADS 

Google Scholar 
Falace, A. et al. Is the South-Mediterranean canopy-forming Ericaria giacconei (= Cystoseira hyblaea) a loser from ocean warming?. Front. Mar. Sci. 8, 1758 (2021).
Google Scholar 
Hernández, C. A., Sangil, C., Fanai, A. & Hernández, J. C. Macroalgal response to a warmer ocean with higher CO2 concentration. Mar. Environ. Res. 136, 99–105 (2018).PubMed 

Google Scholar 
Falace, A., Kaleb, S., Fuente, G. D. L., Asnaghi, V. & Chiantore, M. Ex situ cultivation protocol for Cystoseira amentacea var. stricta (Fucales, Phaeophyceae) from a restoration perspective. PLoS ONE 13, e0193011 (2018).PubMed 
PubMed Central 

Google Scholar 
Bevilacqua, S. et al. Climatic anomalies may create a long-lasting ecological phase shift by altering the reproduction of a foundation species. Ecology 100, 1–4 (2019).
Google Scholar 
Savonitto, G. et al. Addressing reproductive stochasticity and grazing impacts in the restoration of a canopy-forming brown alga by implementing mitigation solutions. Aquat. Conserv. Mar. Freshw. Ecosyst. 31, 1611–1623 (2021).
Google Scholar 
Mangialajo, L. et al. Zonation patterns and interspecific relationships of fucoids in microtidal environments. J. Exp. Mar. Biol. Ecol. 412, 72–80 (2012).
Google Scholar 
Verlaque, M., Boudouresque, C.-F. & Perret-Boudouresque, M. Mediterranean seaweeds listed as threatened under the Barcelona Convention: A critical analysis. Sci. Rep. Port-Cros Natl. Park. 33, 179–214 (2019).
Google Scholar 
Benedetti-Cecchi, L. & Cinelli, F. Effects of canopy cover, herbivores and substratum type on patterns of Cystoseira spp. settlement and recruitment in littoral rockpools. Mar. Ecol. Prog. Ser. 90, 183–191 (1992).ADS 

Google Scholar 
Fuente, G. D. L., Chiantore, M., Asnaghi, V., Kaleb, S. & Falace, A. First ex situ outplanting of the habitat-forming seaweed Cystoseira amentacea var. stricta from a restoration perspective. PeerJ 7, e7290 (2019).
Google Scholar 
Orlando-Bonaca, M. et al. First restoration experiment for Gongolaria barbata in Slovenian coastal waters. What can go wrong?. Plants 10, 239 (2021).PubMed 
PubMed Central 

Google Scholar 
Christie, H. et al. Shifts between sugar kelp and turf algae in Norway: Regime shifts or fluctuations between different opportunistic seaweed species?. Front. Mar. Sci. 6, 72 (2019).
Google Scholar 
Orlando-Bonaca, M., Pitacco, V. & Lipej, L. Loss of canopy-forming algal richness and coverage in the northern Adriatic Sea. Ecol. Indic. 125, 107501 (2021).
Google Scholar 
Thibaut, T., Blanfune, A., Boudouresque, C.-F. & Verlaque, M. Decline and local extinction of Fucales in French Riviera: The harbinger of future extinctions?. Mediterr. Mar. Sci. 16, 206–224 (2015).
Google Scholar 
Thibaut, T., Pinedo, S., Torras, X. & Ballesteros, E. Long-term decline of the populations of Fucales (Cystoseira spp. and Sargassum spp.) in the Albères coast (France, North-western Mediterranean). Mar. Pollut. Bull. 50, 1472–1489 (2005).CAS 
PubMed 

Google Scholar 
Leal, P. P. et al. Copper pollution exacerbates the effects of ocean acidification and warming on kelp microscopic early life stages. Sci. Rep. 8, 14763 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Fernández, P. A., Navarro, J. M., Camus, C., Torres, R. & Buschmann, A. H. Effect of environmental history on the habitat-forming kelp Macrocystis pyrifera responses to ocean acidification and warming: A physiological and molecular approach. Sci. Rep. 11, 2510 (2021).PubMed 
PubMed Central 

Google Scholar 
Lind, A. C. & Konar, B. Effects of abiotic stressors on kelp early life-history stages. Algae 32, 223–233 (2017).CAS 

Google Scholar 
Fernández, P. A. et al. Nitrogen sufficiency enhances thermal tolerance in habitat-forming kelp: Implications for acclimation under thermal stress. Sci. Rep. 10, 3186 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Celis-Plá, P. S. M. et al. Macroalgal responses to ocean acidification depend on nutrient and light levels. Front. Mar. Sci. 2, 26 (2015).
Google Scholar 
Mancuso, F. P. et al. Influence of ambient temperature on the photosynthetic activity and phenolic content of the intertidal Cystoseira compressa along the Italian coastline. J. Appl. Phycol. 31, 3069–3076 (2019).CAS 

Google Scholar 
Vergés, A. et al. The tropicalization of temperate marine ecosystems: Climate-mediated changes in herbivory and community phase shifts. Proc. R. Soc. B Biol. Sci. 281, 20140846 (2014).
Google Scholar 
Vergés, A. et al. Tropical rabbitfish and the deforestation of a warming temperate sea. J. Ecol. 102, 1518–1527 (2014).
Google Scholar 
Gaitán-Espitia, J. D. et al. Interactive effects of elevated temperature and pCO2 on early-life-history stages of the giant kelp Macrocystis pyrifera. J. Exp. Mar. Biol. Ecol. 457, 51–58 (2014).
Google Scholar 
Leal, P. P., Hurd, C. L., Fernández, P. A. & Roleda, M. Y. Ocean acidification and kelp development: Reduced pH has no negative effects on meiospore germination and gametophyte development of Macrocystis pyrifera and Undaria pinnatifida. J. Phycol. 53, 557–566 (2017).CAS 
PubMed 

Google Scholar 
Roleda, M. Y., Morris, J. N., McGraw, C. M. & Hurd, C. L. Ocean acidification and seaweed reproduction: Increased CO2 ameliorates the negative effect of lowered pH on meiospore germination in the giant kelp Macrocystis pyrifera (Laminariales, Phaeophyceae). Glob. Change Biol. 18, 854–864 (2011).ADS 

Google Scholar 
Zhang, X. et al. Elevated CO2 concentrations promote growth and photosynthesis of the brown alga Saccharina japonica. J. Appl. Phycol. https://doi.org/10.1007/s10811-020-02108-1 (2020).Article 

Google Scholar 
Falkenberg, L. J., Russell, B. D. & Connell, S. D. Contrasting resource limitations of marine primary producers: Implications for competitive interactions under enriched CO2 and nutrient regimes. Oecologia 172, 575–583 (2013).ADS 
PubMed 

Google Scholar 
Nagelkerken, I., Russell, B. D., Gillanders, B. M. & Connell, S. D. Ocean acidification alters fish populations indirectly through habitat modification. Nat. Clim. Change 6, 89–93 (2016).ADS 
CAS 

Google Scholar 
Connell, S. D. & Russell, B. D. The direct effects of increasing CO2 and temperature on non-calcifying organisms: increasing the potential for phase shifts in kelp forests. Proc. R. Soc. B Biol. Sci. 277, 1409–1415 (2010).
Google Scholar 
Cornwall, C. E., Comeau, S. & McCulloch, M. T. Coralline algae elevate pH at the site of calcification under ocean acidification. Glob. Change Biol. 23, 4245–4256 (2017).ADS 

Google Scholar 
Martin, S. & Gattuso, J.-P. Response of Mediterranean coralline algae to ocean acidification and elevated temperature. Glob. Change Biol. 15, 2089–2100 (2009).ADS 

Google Scholar 
Cornwall, C. E. et al. Diffusion boundary layers ameliorate the negative effects of ocean acidification on the temperate coralline macroalga Arthrocardia corymbosa. PLoS ONE 9, e97235 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gefen-Treves, S. et al. The microbiome associated with the reef builder Neogoniolithon sp. in the eastern Mediterranean. Microorganisms 9, 1374 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Johnson, C. R. & Mann, K. H. The crustose coralline alga, Phymatolithon Foslie, inhibits the overgrowth of seaweeds without relying on herbivores. J. Exp. Mar. Biol. Ecol. 96, 127–146 (1986).
Google Scholar 
Keats, D. W., Knight, M. A. & Pueschel, C. M. Antifouling effects of epithallial shedding in three crustose coralline algae (Rhodophyta, Coralinales) on a coral reef. J. Exp. Mar. Biol. Ecol. 213, 281–293 (1997).
Google Scholar 
Mancuso, F., D’Hondt, S., Willems, A., Airoldi, L. & Clerck, O. Diversity and temporal dynamics of the epiphytic bacterial communities associated with the canopy-forming seaweed Cystoseira compressa (Esper) Gerloff and Nizamuddin. Front. Microbiol. 7, 476 (2016).PubMed 
PubMed Central 

Google Scholar 
Blanfuné, A., Boudouresque, C. F., Verlaque, M. & Thibaut, T. The ups and downs of a canopy-forming seaweed over a span of more than one century. Sci. Rep. 9, 1–10 (2019).
Google Scholar 
Cebrian, E. et al. A roadmap for the restoration of Mediterranean macroalgal forests. Front. Mar. Sci. 8, 1456 (2021).
Google Scholar 
Gianni, F. et al. Conservation and restoration of marine forests in the Mediterranean Sea and the potential role of Marine Protected Areas. Adv. Oceanogr. Limnol. 4, 83–101 (2013).
Google Scholar 
Gorman, D. & Connell, S. D. Recovering subtidal forests in human-dominated landscapes. J. Appl. Ecol. 46, 1258–1265 (2009).
Google Scholar 
Riquet, F. et al. Highly restricted dispersal in habitat-forming seaweed may impede natural recovery of disturbed populations. Sci. Rep. 11, 16792 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Halpern, B. S., McLeod, K. L., Rosenberg, A. A. & Crowder, L. B. Managing for cumulative impacts in ecosystem-based management through ocean zoning. Ocean Coast. Manag. 51, 203–211 (2008).
Google Scholar 
Verdura, J., Sales, M., Ballesteros, E., Cefalì, M. E. & Cebrian, E. Restoration of a canopy-forming alga based on recruitment enhancement: Methods and long-term success assessment. Front. Plant Sci. 9, 1832 (2018).PubMed 
PubMed Central 

Google Scholar 
Kwiatkowski, L. et al. Twenty-first century ocean warming, acidification, deoxygenation, and upper-ocean nutrient and primary production decline from CMIP6 model projections. Biogeosciences 17, 3439–3470 (2020).ADS 
CAS 

Google Scholar 
Dickson, A. G., Sabine, C. L. & Christian, J. R. Guide to Best Practices for Ocean CO2 Measurements. https://repository.oceanbestpractices.org/handle/11329/249 (2007).Spencer Davies, P. Short-term growth measurements of corals using an accurate buoyant weighing technique. Mar. Biol. 101, 389–395. https://doi.org/10.1007/BF00428135 (1989).Article 

Google Scholar 
Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models Using lme4. ArXiv14065823 Stat (2015).R: The R Project for Statistical Computing. https://www.r-project.org/.Fox, J. & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, 2018).
Google Scholar 
Lenth, R. V. et al. emmeans: Estimated Marginal Means, aka Least-Squares Means (2022). More

Gill, J. A. et al. Proc. R. Soc. B 281, 20132161 (2014).Article 

Google Scholar 
Tomotani, B. M. et al. Glob. Chang. Biol. 24, 823–835 (2018).Article 

Google Scholar 
Teplitsky, C., Mills, J. A., Alho, J. S., Yarrall, J. W. & Merilä, J. Proc. Natl Acad. Sci. USA 105, 13492–13496 (2008).Article 
CAS 

Google Scholar 
Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Trends Ecol. Evol. 26, 285–291 (2011).Article 

Google Scholar 
Youngflesh, C., Saracco, J. F., Siegel, R. B. & Tingley, M. W. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-022-01893-x (2022).Article 

Google Scholar 
Hughes, E. C. et al. Ecol. Lett. 25, 598–610 (2022).Article 

Google Scholar 
Tobias, J. A. et al. Ecol. Lett. 25, 581–597 (2022).Article 

Google Scholar 
Shine, R. Q. Rev. Biol. 64, 419–461 (1989).Article 
CAS 

Google Scholar 
Dubiner, S. & Meiri, S. Glob. Ecol. Biogeogr. 31, 791–801 (2022).Article 

Google Scholar 
Jirinec, V. et al. Sci. Adv. 7, eabk1743 (2021).Article 

Google Scholar 
Weeks, B. C. et al. Ecol. Lett. 23, 316–325 (2020).Article 

Google Scholar 
Parmesan, C. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Bonamour, S., Chevin, L. M., Charmantier, A. & Teplitsky, C. Phil. Trans. R. Soc. Lond. B 374, 20180178 (2019).Article 

Google Scholar 
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Ecol. Lett. 15, 365–377 (2012).Article 

Google Scholar 
Hickling, R., Roy, D. B., Hill, J. K., Fox, R. & Thomas, C. D. Glob. Change Biol. 12, 450–455 (2006).Article 

Google Scholar 
Forero-Medina, G., Joppa, L. & Pimm, S. L. Conserv. Biol. 25, 163–171 (2011).Article 

Google Scholar 
Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1983).Berg, M. P. & Ellers, J. Evol. Ecol. 24, 617–629 (2010).Article 

Google Scholar  More

Mangrove cover change variables. We used the Global Mangrove Watch (GMW) v2.0 dataset from 1996 to 201656 to calculate four response variables across landscape mangrove geomorphic units24 over two time periods, 1996–2007 and 2007–2016: (1) percent net loss (units that had a net change in mangrove cover of 0), (3) percent gross loss (units that had a decrease in mangrove cover, not accounting for any increase), and (4) percent gross gain (units that had an increase in mangrove cover, not accounting for any decrease). Percent variables were calculated relative to the area at the start of the time period and were log transformed to meet the assumptions of the statistical models. We initially also considered 5 primary response variables (Supplementary Table 3), including net change in mangrove area ranging from negative (loss) to zero (no change) to positive (gain), however, the data did not meet model assumptions of equal variance (Supplementary Table 9). It was therefore necessary to separate areas of net loss and net gain and areas of gross loss and gross gain to remove zeros and log-transform to achieve normal distribution. Area of mangrove change was correlated with size of the mangrove geomorphic unit (higher area of mangrove loss or gain in bigger units), therefore we included geomorphic unit size as an explanatory variable in the models with primary response variables. We selected the transformations of these primary variables – percent net loss, percent net gain, percent gross loss, and percent gross gain to include in the analysis, because the percent changes control for differences in relative sizes of geomorphic units and because net change alone can underestimate the extent of change57.Examining mangrove change across geomorphic settings is likely to be relevant to socioeconomic and environmental conditions. Mangroves occur in the intertidal zone in diverse coastal geomorphic settings (e.g., deltas, estuaries, lagoons) shaped by rivers, tides, and waves58,59. The distribution, structure, and productivity of mangroves varies spatially with regional climate and local geomorphological processes (e.g., river discharge, tidal range, hydroperiod, and wave activity) that control soil biogeochemistry60,61,62,63. These geomorphic settings are defined by natural landscape boundaries (e.g., catchments/bays) which also often delineate boundaries of human settlements. A global mangrove biophysical typology v2.2 dataset64 was used for the delineation of landscape mangrove geomorphic units, which used a composite of the GMW dataset from the 1996, 2007, 2010, and 2016 timesteps to classify the maximal extent of mangrove cover into 4394 units (classified as delta, estuarine, lagoon or open coast). The mangrove geomorphic units do not include non-mangrove patches, unless they have been lost from the unit over time. The mean size of geomorphic units was 33.63 ha. Some splits of geomorphic units were undertaken to reduce size and divide by country boundaries. The four largest deltas (northern Brazil Delta ID 70000, Sundarbans Delta ID 70004, Niger Delta ID 70009, and Papua coast Delta ID 70013) were split into 4, 5, 4, and 2 units, respectively to aid with data processing. Mangrove geomorphic units that overlapped two countries (Peru/Ecuador, Singapore/Malaysia, and Papua New Guinea/Australia) were split by the national boundary.The country governing each geomorphic unit was assigned to match national-level variables to geomorphic units. To capture mangroves that are mapped outside of country coastline boundaries, we did a union of the GADM country shapefile v3.665 and the Exclusive Economic Zones (EEZs) v1166. The following manual country designations were made to resolve overlapping claims in the EEZs: (1) Hong Kong was merged with China as Hong Kong does not have a mapped EEZ; (2) The overlapping claim of Sudan/Egypt was maintained as a joint Sudan/Egypt designation, as this is an area of disputed land called the Halayib Triangle. However, for this study, mangrove units within this area were assigned to Egypt because Egypt currently has military control over the area; (3) Mayotte (claimed by France and Comoros) was assigned to Mayotte as it is a separate overseas territory of France recognised in GADM that has different socioeconomic variables; (4) The protected zone established under the Torres Strait Treaty was assigned to Australia as these islands are Australian territory.Areas of mangrove cover in 1996, 2007, and 2016, and gross losses and gains in each geomorphic units over the two time periods were assessed in ArcMap 10.867. Percent losses and gains were calculated in R 4.0.268. In using the GMW mapping, a minimum mapping unit of 1 ha is recommended for reliable results5, therefore we removed all geomorphic units less than 1 ha from the analysis, which reduced the available sample size from 4394 across 108 countries to 4235 units across 108 countries. In calculating percent net gains, 11 and 12 of the units returned infinity values for 1996–2007 and 2007–2016, respectively, because there was no initial mangrove cover. In these instances, 100% gain was assigned to these units.Socioeconomic variables (Supplementary Table 4)Economic growthPrevious global analyses of mangroves have been limited by data availability on economic activity to national metrics, such as a country’s Gross Domestic Product (GDP)12,18. Night-time lights satellite data provide local measures of economic activity that are comparable through time and available globally9,69. The data improve estimates of GDP in low to middle income countries69 and are strongly correlated with local indicators of human development70 and electricity consumption and GDP at the national-level71. We used the Night-time Lights Time Series v472 stable lights data, where transient lights that are deemed ephemeral, e.g., fires, have been filtered out and non-lit areas set to zero73, choosing the newer satellites where applicable70. As a proxy for local economic growth, we calculated the change in annual average stable lights within a 100 km buffer of the centroid of each geomorphic unit from 1996 to 2007 and 2007 to 2013 (no data available past 2013) using the ‘raster’ package in R74. The 100 km buffer was chosen to account for pressures from human activity within and surrounding the mangrove area, and to avoid bias with larger spatial units70.Market accessibilityTravel time to the nearest major market (national or provincial capital, landmark city, or major population centre) has been shown to be a stronger predictor of fish biomass on coral reefs than population density or linear distance to markets27. We used the global map of travel time to cities for 201575 to estimate the average travel time from each geomorphic unit to the nearest city via surface transport using the ‘raster’ package in R74, as an indicator of access to markets to trade commodities (e.g., rice, shrimp, palm oil).Economic complexityPrevious studies have examined the effect of GDP on mangrove change18, however, this is a blunt measure of country capability. Measuring a country’s economic complexity, that is the diversified capability of a nation’s economy, is preferable. For example, a country with high GDP but low economic complexity can be prone to regulatory capture by high-value natural resource industries and resource corruption26. Therefore, we used the Economic Complexity Index (ECI)76 for countries as an indicator of regulatory independence. The ECI had better coverage of countries in later years (Supplementary Table 4), therefore the ECI for the end of the time periods was used (2007 and 2016), although we recognise this may reduce the detection of trends because of potential time lags in impacts.DemocracyWe used the Varieties of Democracy (VDEM) index v10 which measures a country’s degree of freedom of association, clean elections, freedom of expression, elected executives, and suffrage77, and has been indicated to influence NDC ambition in countries to address climate change78. We adopted the VDEM index for the start of the time periods (1996 and 2007) to account for potential time lags in impacts.Community forestry supportWe determined the extent that community forestry (CF) is implemented across countries through a systematic review of articles returned in the Web of Science database (Core collection; Thomson Reuters, New York, U.S.A.). We used the search terms: TS = (“community forestry” OR “community-based forestry” OR “social forestry”) AND (TI = ”country” OR AB = ”country”) to identify how many CF case studies were reported in each country, and whether any were in mangroves. As scientific literature is biased towards particular regions, we also reviewed relevant FAO global studies79,80,81 and online databases (ICCA registry82 and REDD projects database83) to identify additional case studies (Supplementary Fig. 5). We then generated scores of 0–3 for each country based on summing values assessed using these criteria: +1 (1–50 CF case studies); +2 ( >50 CF case studies); +1 (CF case study in mangroves). There may have been some double counting as we counted the number of case studies in each article, and we will have missed CF projects not published or communicated in English. However, this is likely to have had a limited impact on the scoring method.Indigenous landThe proportion of Indigenous peoples’ land versus other land per country was calculated from national-level data84. Whilst this study involved Indigenous peoples’ land mapping at a global scale, the spatial data was not published, and thus we could only evaluate the influence of Indigenous land at the national level rather than local level.Restoration effortThe number of mangrove restoration sites per country was calculated from combining two datasets collated by C. Lovelock (2020) and Y.M. Gatt and T.A. Worthington (2020) identifying mangrove restoration project locations from web searches in English and for scientific and grey literature using Google Scholar. Duplications were removed and the number of sites was used as an indicator of effort. This will underrepresent effort in countries with few, large sites, and where restoration projects are not published or communicated in English.Climate commitmentsThe Paris Agreement is a global programme for countries to commit to climate action by submitting Nationally Determined Contributions (NDCs) to the United Nations Framework for the Convention of Climate Change (UNFCCC). First, we reviewed NDCs for mangrove-holding nations from the NDC Registry85 submitted as of 07/01/2021 to determine the extent that mangroves or coastal ecosystems were included in national climate policy (scoring method in Supplementary Table 4). We hypothesised that countries with mangrove or coastal ecosystem NDCs may be more likely to promote mangrove conservation or restoration. While the first NDCs were submitted around 2015, at the end of our time series, we suspected higher commitments would point towards a stronger baseline in environmental governance. Most countries submitted updated or second NDCs during 2021 however these were not considered relevant to the time periods assessed. Google Translate was used to interpret NDCs in languages other than English.Ramsar wetlandsThe ecological character of Ramsar wetlands have been found to be significantly better than those of wetlands generally86. The area of Ramsar coastal and marine wetlands from the Ramsar Sites Information Service87 was calculated per country. Thirty-eight mangrove-holding countries are not signatories to the Ramsar Convention, and these countries were assigned a value of 0. The area of Ramsar wetlands per country was scaled by dividing by the country’s area of mangroves in 1996.Environmental governanceWe assessed the Environmental Performance Index (EPI)88 as an indicator of a country’s effectiveness in environmental governance. The biodiversity and habitat (BDH) issue category assesses countries’ actions toward retaining natural ecosystems and protecting the full range of biodiversity within their borders. We took the BDH score for 2020 for the 2007–2016 time period and the BDH score for 2010 for the 1996–2007 time period (calculated by subtracting the ten-year change from BDH 2020). However, due to collinearity with other variables this index was excluded from the analysis (see statistical analysis).Protected area managementWe also assessed Marine Protected Area (MPA) staff capacity as an indicator of the effectiveness of management of protected areas for countries. We used published global marine protected area (MPA) management data14 which is based on the Management Effectiveness Tracking Tool (METT), the World Bank MPA Score Card, and the NOAA Coral Reef Conservation Programme’s MPA Management Assessment Checklist. Adequate staff capacity was the most important factor in explaining fish responses to MPA management globally, followed by budget capacity, but they were significantly correlated14. We, therefore, calculated the mean staff capacity across MPAs per country as our indicator. Mangroves can be included in terrestrial protected areas, which are not represented in this dataset, however, this measure provides an indicator of national governance of protected areas. However, due to collinearity with other variables this indicator was excluded from the analysis (see statistical analysis). The extent of protected areas was not included in the analysis because it has already been found to influence mangrove loss18.Biophysical variables (Supplementary Table 5)Coastal geomorphic typeMangrove extent change likely varies among different coastal geomorphic settings because human activities or environmental changes occur more commonly in some geomorphic settings than others. For example, losses of lagoonal mangroves were nearly twice as large as those in other geomorphic types24. Landscape geomorphic units from the global mangrove typology dataset v2.264 were classified as delta, estuary, lagoon or open coast.Sediment availabilityMangrove expansion and retreat are driven by sediment deposition and erosion, which are influenced by sediment availability from rivers and wave action, and alterations in hydrodynamic regimes47,89. We used the sediment trapping index from the global free-flowing rivers (FFR) dataset90 to indicate sediment availability from rivers within different geomorphic units. A mangrove catchment dataset was created based on the HydroSHEDS database91. River networks that intersected with mangrove geomorphic units were linked to that unit’s ID. Where rivers intersected multiple units, they were manually assigned by visual inspection. River basins that intersected either with the geomorphic units directly or the river networks were also linked to that unit’s ID. The FFR dataset90 was then spatially joined to the mangrove catchment dataset to identify the most downstream (i.e., the coastal outlet) segment of each FFR and its associated sediment trapping index. Not all geomorphic units (n = 3475) were linked to an FFR, however, an individual unit could be linked with several FFRs. Therefore, the unit sediment trapping index was the weighted mean of the river values, with weighting based on each FFR’s average long-term (1971–2000) naturalised discharge (m3s−1), with discharge set to the minimum value for segments with zero flow. Geomorphic units without connecting FFRs were given an index of zero (no sediment trapping). The sediment trapping index represents the percentage of the potential sediment load trapped by anthropogenic barriers along the river section. The focus on river barriers may obscure larger scale oceanic patterns that influence mangrove losses and gains (e.g., movement of mud banks from the Amazon River over 1000’s of kilometres92) or increases in sediment that could be coming from soils with catchment deforestation and erosion.Habitat fragmentationMany countries with high mangrove loss have been associated with elevated fragmentation of mangrove forests, although the relationship is not consistent at the global scale93. We calculated the clumpiness index of mangrove patches within geomorphic units within each time period, as this habitat fragmentation metric is independent of areal extent93. Whilst habitat fragmentation can be human-driven, clumpiness measures the patchy distribution of mangroves, which can also be due to natural factors inducing edge effects. We used a similar approach to Bryan-Brown, et al.86 to quantify the clumpiness index. The ‘landscape’ was defined as the combined extent of the mangrove geomorphic units across four timesteps (1996, 2007, 2010, and 2016) from the GMW dataset56. For the three focal years in this study (1996, 2007, and 2016) each geomorphic unit (n = 4394) was converted into a two-class polygon, where class one represented mangroves present during that time step and class two mangroves present in the other time steps (i.e., areas of mangrove loss). The polygons were transformed to a projected coordinate system (World Cylindrical Equal Area) and converted to rasters with a resolution of 25 m. Each raster was imported into R version 3.6.394, with clumpiness calculated using the package ‘landscapemetrics’ v1.5.095.Clumpiness describes how patches are dispersed across the landscape and ranges between minus one, where patches are maximally disaggregated, to one, where patches are maximally aggregated, a value of zero represents a case whereby patches are randomly distributed across the landscape. The clumpiness index requires that both classes are present in the landscape, therefore a no data value (NA) was returned for units where no loss of mangroves had occurred, or where there was 100% gain of mangroves in a later time period. The number of directions in which patches were connected was set to eight. The following manual fixes were conducted for NA values returned: 1) Where NA was returned for units where no loss of mangroves had occurred in another time period, i.e., class 1 (mangrove present) = 1 and class 2 (mangrove loss) = 0, assume +1 (maximally clumped); and 2) Where NA was returned for units where there was 100% gain of mangroves in a later time period, i.e., class 1 (mangrove present) = 0, class 2 (mangrove present) = 1 (100% gain), assume −1 (maximally disaggregated).Tidal amplitudeIn settings of low tidal range, mangrove vertical accretion is less likely to keep pace with rapid sea level rise3. However, in settings of high tidal range, mangroves may be more extensive and vulnerable to conversion to aquaculture or agriculture because of larger tidal flat extents. The Finite Element Solution global tide model (FES2014)96 is considered one of the most accurate tide models for shallow coastal areas97 and was selected to estimate the mean tidal amplitude within each geomorphic unit using the principal lunar semi-diurnal or M2 tidal amplitude as this is this most dominant tidal constituent98. To account for potential variation in the tidal amplitude across large geomorphic units, the raster pixel value for M2 tidal amplitude96 closest to the centroid of each mangrove patch within each unit was calculated, with the smallest value set at 0.01 m. For each geomorphic unit, the tidal amplitude was calculated as the weighted mean of the patch values, with weighting based on the patch area relative to the total unit area.Antecedent sea-level riseThe distribution of mangroves on shorelines changes over time with sediment accretion, erosion, subsidence, and sea-level rise (SLR)99, and periods of low sea level can cause mangrove dieback100. We used regional mean sea-level trends between January 1993 and December 2015 from the global sea level Essential Climate Variable (ECV) product v.2101,102 to estimate the mean antecedent SLR for each geomorphic unit. Spatial variation in regional sea-level trends generally range between −5 and +5 mm yr−1 (global mean of 3 mm yr−1)13. Extreme values ( >5 mm yr−1) observed in the dataset are subject to high levels of uncertainty (Sea Level CCI team, pers. comm.), and were therefore truncated to 5 mm yr−1. The raster pixel value for SLR102 closest to the centroid of each mangrove patch within each geomorphic unit was calculated. The geomorphic unit antecedent SLR values was calculated as the weighted mean of the patch values within the unit.DroughtWhilst long-term precipitation and temperature influence mangrove distribution globally62, periods of low rainfall have been reported to cause extensive mangrove dieback at regional scales, particularly when combined with high temperatures and low sea levels103. We used the Standardized Precipitation-Evapotranspiration Index (SPEI) from the global SPEI database v.2.6104 as an index of drought severity. SPEI is derived from precipitation and temperature and is considered an improved drought index that allows spatial and temporal comparability105,106. The mean SPEI raster pixel value was calculated for each time period and then averaged across the geomorphic units using the ‘ncdf4’107 and ‘raster’ packages74 in R.Tropical storm frequencyLarge-scale destruction of mangroves across regions have been reported from strong winds, high energy waves, and storm surges associated with tropical storms108. We used the International Best Track Archive for Climate Stewardship (IBTrACS) dataset since 1980 v4109 to calculate the number of tropical cyclone occurrences (points along their paths) within a 200 km buffer of the centroid of geomorphic units within each time period using the sf package110 in R. Maximum wind velocity and surface pressures are likely experienced within 100 km of a cyclone’s eye111, therefore the 200 km buffer zone was selected to cover the average size of geomorphic units (33.63 ha), and all tropical storms potentially influencing mangrove growth. Whilst tropical storms affect only 42% of the world’s mangroves60, they are likely to be important stressors within cyclone-impacted countries.Minimum temperatureExtreme low temperature events were a driver of mangrove loss in subtropical regions, such as Florida and Louisianan of the US, and China28,112. We used the WorldClim bioclimatic variable 6 (minimum temperature of the coldest month averaged for the years 1970–2000)113 to calculate the mean minimum temperature across the geomorphic units using the ‘sf’110 and ‘raster’ packages74 in R. Where NAs were returned due to no overlapping raster layer, the value of the closest raster pixel to the centroid of the geomorphic unit was assigned.Statistical analysisWe used multi-level linear modelling to investigate relationships between mangrove cover change variables and socioeconomic and biophysical variables to consider landscape (level 1) and country (level 2) predictors in a hierarchical approach114. For each response variable, we modelled the response for 1996–2007 and 2007–2016, using explanatory variables specific to the time-period where available. Data inspection revealed that high percent loss or gain was concentrated in small geomorphic units, therefore to avoid bias in our results, we removed geomorphic units less than 100 ha from the analysis, which further reduced the available sample size to 3134 units across 95 countries. Statistical analysis was undertaken in R 4.0.268.The response variables were log-transformed to fit normal distribution. We tested for collinearity between our explanatory variables using Pearson’s correlation coefficient (r  > 0.5) (Supplementary Tables 6 and 7). MPA staff capacity and EPI were excluded from our models because MPA staff capacity was correlated with ECI 2007 and ECI 2016 (both r = 0.54), and EPI 2020 was correlated with VDEM 2016 (r = 0.63). To improve model fit, travel time to the nearest city, mangrove restoration effort and Ramsar wetland area (relative) were log+1-transformed, and tidal amplitude was log-transformed.Two linear multi-level (mixed-effects) models were fitted for each response variable using the lme function in the ‘lme4’ package115 (Supplementary Table 8). First, a random intercept model with intercepts of landscape-level predictors varying by country was fitted. Then a random intercept and slope (coefficients) model with intercepts of landscape-level predictors varying by country, as well as slopes for socioeconomic predictors considered to have between-country variation (travel time to nearest city and night-time lights growth) was fitted, as we expect that mangrove cover change may respond to economic growth and market accessibility depending on national governance. A likelihood ratio test between the null linear model and the null random intercept model for each response variable showed that effects varied across countries and therefore we included country as a random effect (Supplementary Table 9). We also conducted likelihood ratio tests between the random intercept model and the random coefficient model to test whether the effect of travel time and night-time lights on mangrove change varies across countries. If significant, the model including random slopes for travel time and night-time lights was used (Supplementary Table 9). Mixed-effects models were fitted by maximum likelihood and model fit was validated by inspection of residual plots for the four response variables included in the analysis; percent net loss, percent net gain, percent gross loss, and percent gross gain (Supplementary Table 9).To test for spatial autocorrelation we performed spatial autoregressive (SAR) models using the errorsarlm function in the ‘spatialreg’ package116. SAR models were first fitted using a range of neighbourhood distances (50, 500, and 1000 km in 100 km intervals) for the net change variable117. Distance of 500 km showed the smallest AIC and was therefore adopted for all response variables. Neighbourhood lists of the centroid coordinates of the geomorphic units were defined with the row-standardised (‘W’) coding using the ‘spdep’ package118. We then produced Moran’s I correlograms using the correlog function in the ‘ncf’ package119 and the centroid coordinates of the geomorphic units. Correlograms for the multi-level model and SAR model were compared for each response variable (Supplementary Fig. 4). The SAR models did not improve spatial autocorrelation for any of the mangrove cover change variables and therefore the multi-level models were adopted.Hotspot estimatesWe defined hotspots as geomorphic units where raw values of percent net and gross loss and gain between 2007 and 2016 ((gamma)) differed by more than two standard deviations (sd) from the country average ((mu)).$${{{{{{rm{More}}}}}}},{{{{{{rm{loss}}}}}}}/{{{{{{rm{more}}}}}}},{{{{{{rm{gain}}}}}}}=left(gamma -mu right) , > , (2,times {{{{{{rm{sd}}}}}}})$$
(1)
$${{{{{{rm{Less}}}}}}},{{{{{{rm{loss}}}}}}},/,{{{{{{rm{less}}}}}}},{{{{{{rm{gain}}}}}}}=left(gamma -mu right) , < , -(2,times {{{{{{rm{sd}}}}}}})$$ (2) We excluded countries with only one geomorphic unit. Large deviations of the raw value from the country average were found for small units at a threshold below 50 km2, therefore we removed all units smaller than 50 km2 to overcome bias of hotspots towards smaller sites. This likely removed the identification of several hotspots. For example, Myanmar has had some large gains due to river sediments in the Gulf of Martaban (net gain of 100 % in Estuary 5834 and 39 % in Open Coast 62244), however, these areas were small (8 and 2 km2, respectively) and were therefore removed from the hotspot estimates.We analysed the factors contributing to hotspots by spatial investigation of satellite imagery in Google Earth with mangrove specialists from those countries. The hotspots were also assessed against protected area datasets for those countries120,121,122,123.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

We found a significant positive relationship between pollen- and plant richness regardless of differences in plant diversity, landscape structure and environmental conditions between the two study regions. This finding represents a major step stone towards more accurate paleoecological reconstructions of plant diversity in temperate Central Europe, as previous studies on this topic have mostly been conducted in boreal and boreal-nemoral zones8,11, in high mountain habitats10 or in southern Europe9,12.Methodological differences e.g., in diversity indices, data transformations or sample sizes used make comparison between studies difficult. Nevertheless, the strongest relationships seem to be found when habitats with contrasting patterns of plant diversity are compared, such as forests and alpine vegetation7 or forests, peatlands and grasslands11. Also in our study, we found the strongest correlations when complete datasets combining forested and open habitats were analysed together for both study regions. As it is well known that plant richness is generally lower in forests than in open landscapes across temperate and boreal regions28, this finding may seem rather trivial. However, it is important for paleoecological reconstruction because Holocene changes in diversity in temperate regions were largely driven by changes in the relative abundance of major habitat types (such as forests, grasslands, wetlands and man-made habitats), and not just by changes in species richness within these habitats5,6.Regarding individual habitats, the pollen-plant diversity relationship is often rather strong and significant in grasslands and other open habitats8,11; for example the WCM open-habitat subset in this study. Open habitats are generally richer in species, thus providing a longer gradient of species richness compensating for the taxonomical imprecision of the pollen analysis. In forested sites with less species, we found mostly non-significant relationships. Moreover, two other factors may play a role.First, high pollen productivity of trees biases the diversity relationship according to the studies from northern Europe16. However, a study from an elevational transect in southern Norway showed that the strongest bias in representation occurs only in the boreal forest biome, which is dominated by high pollen producers10. Our dominant vegetation component, Picea and Quercus, have intermediate to high pollen productivity (2–2.5), whereas true high pollen producers such as Alnus and Betula ( > 3) are less abundant in our study area (Supplementary Fig. S2). Adjustment of pollen counts by PPEs led to stronger relationship between pollen and floristic richness only in the WCM open-habitat subset (Supplementary Fig. S4).Second, interception of pollen by the tree canopies29 and subsequent washout to the forest floor affects the diversity relationship of forest sites more than pollen productivity. This noise described also as a vegetation filtering30 can be illustrated in our dataset by pollen of long-distance transport from Ambrosia artemisiifolia-type, which has the closest source populations ca. 50 km south-eastwards from WCM region31; or pollen of Artemisia, growing in open habitats. Both pollen taxa are more abundant in the forest than in open sites (Supplementary Fig. S3).Regarding the application of these results for the interpretation of fossil record, we suggest to consider only marked changes of pollen richness in the past and to avoid overinterpretation of small differences, as the non-significant relationships obtained in both forest datasets suggest some limitations of the method.We showed that the pollen-plant diversity relationship may be at least partly disentangled by knowing the exact spatial position of plant species in broader surroundings of the pollen sampling sites. Changes in the relationship with changing spatial scale are largely driven by the numbers of species newly appearing as the radius of surveyed area increases, especially as new habitats are added (Fig. 5, Supplementary Fig. S5). Remarkably, in the BMH region it increases with distance, whereas the opposite trend was observed in the WCM region. This discrepancy may be explained by non-uniform richness patterns in different habitats and by different landscape structure (i.e. spatial arrangement of different habitats) in the two study regions.At open-habitat sites in the WCM area, most species generally appeared within the first 40 m. This observation is consistent with the knowledge of extremely high fine-scale plant diversity in the local steppic meadows, where a substantial portion of the species pool occurs on a scale of tens of square meters32. Moreover, the grain size of the habitat mosaic in the WCM region is finer than in the BMH region. Therefore, the closest pollen-plant diversity relationship across habitats in the WCM region is achieved over shorter distances. Although habitats such as built-up areas and roads occurring at distances greater than 40 m may be species-rich and compositionally different from the grasslands and forests, it appears that high fine-scale plant diversity (in our case in WCM open-habitat subset) limits the influence of the surrounding landscape on pollen richness and reduces the source area of pollen richness. Several studies of the relevant source area of pollen report analogous results33,34,35. A weakening relationship between pollen diversity and plant diversity with distance has also been observed in the Mediterranean region9, although their interpretations are limited by field survey methodology.The appearance of open habitats within forests led to the increase of species numbers and the local maxima of adjusted R2 in both regions. While in the BMH forest the appearance of forest roads at about 70 m was crucial, meadows and orchards at about 250 m played a similar role in the WCM forest subset. In the WCM open-habitat subset diversity patterns in the first tens of metres were crucial, while in the BMH open-habitat subset increased correlation of floristic and pollen richness appeared only at 400 and 550 m; at this distance many species appeared due to the frequent transition of meadow complexes to shrubby habitats and built-up areas. Also other studies from semi-open landscapes found a high correlation between pollen richness and landscape openness17,26,27.Estimating the source area of pollen variance as a regression of pollen and floristic variance implies that the resulting distance of 100–250 m represents all datasets. Although they differ in species richness, openness and habitats, the relationship between variances is fairly linear. The exception is the WCM open-habitat subset suggesting that the spatial scale at which the pollen variance corresponds to the floristic variance cannot be generalized.The strong effect of high pollen richness in the WCM open-habitat subset is also visible in the comparison of pollen and floristic variance. At 150 m, the WCM open-habitat subset had much lower floristic variance than the other subsets. Floristic variance in this subset corresponding to the pollen variance and the pattern of the other datasets lay at 6 m (Fig. 6b). Again, this may be caused by the high fine-scale diversity of the meadows, which include most pollen types present in the surrounding landscape. Only a few new species appeared in broader surroundings and at 150 m, WCM open habitats are more similar than other analysed habitats. The fact that extremely high alpha diversity is compensated by low beta diversity has already been reported from the open habitats of the White Carpathians36. The linearity and the significance of the variance relationship within the rest of the datasets indicate robustness and possible applicability to a variety of fossil records.The mechanism of establishing the source area of pollen variance was similar to that mentioned for the source area of pollen richness. The appearance of new habitats with new species (Fig. 5) like open habitat for forest sites (WCM forest subset) or built-up areas for open sites (BMH open-habitat subset), caused small to negligible increases of floristic variance. Moreover, the high yet insignificant relationship of the variances at the distance between 250 and 600 m (Fig. 6a) corresponds to the distance of the second range of fit between floristic and pollen richness (Fig. 4a).Beta diversity, understood as directional turnover (temporal or spatial), is becoming more frequently used in pollen analysis22,24 than beta diversity as a non-directional variation. According to Nieto-Lugilde et al.25 pollen-based turnover correlates with forest-inventory-based turnover. We extend this finding from woody taxa to all species and from directional turnover to non-directional variance. Moreover, forest sites with high contributions to pollen beta diversity also show an increased contribution to floristic beta diversity (Fig. 4b).The reference data on plant diversity report 1477 species in 15 mapping squares covered by our survey for the BMH region and 2045 species in 14 squares for the WCM region37. It means that we recorded 54.1 and 53.7%, respectively, of the known regional species pool in the two regions. We consider this as a rather good result and the close agreement in representativeness between the two regions speaks for consistency in data quality between the datasets. We advise that future studies covering wider areas and various biomes should preferentially use high-quality floristic data collected in targeted field surveys rather than database data or data from simplified field surveys. Only then we will be able to understand the pollen-plant diversity relationships more realistically and in a spatially explicit manner.In order to interpret fossil pollen richness in the light of our present results, we need to consider landscape openness, which can be roughly inferred from the ratio of arboreal and non-arboreal pollen. Variation of pollen richness during the forest phases of the records should be interpreted more carefully, especially in cases of low variation. In all other cases, the pollen richness is significantly linked to the plant richness within a distance of ten to several hundreds of meters, depending on the distance of the expected species-rich patches. More