Ecology

In the version of this article initially published, there were errors in equations and notations in the Methods “Model development” subsection which arose during manuscript preparation; the errors affect presentation of the study but not the analysis, results, or code provided with the article. Clarifications to text and equations follow.In Equation (1), “N” replaces “Normal”; in Equations (2), (3), (7) and in text directly below Equations (3), (5) and (7), “ys,i,z” now replaces “Δxs,t1, t2.” In the two paragraphs below Equation (2), “t2 = 2016” and “t1 = 2001” now replace “2016” and “2001” in five instances. Further, Equations (5)–(7) have been revised as follows:$$begin{array}{ll}fleft( {x_{s,t}} right) = {{{mathrm{exp}}}} & left( {beta _0 + mathop {sum }limits_{i = 1}^{I = 5} beta _{1,i} x_{s,i,t} + mathop {sum }limits_{i = 1}^{I = 5} mathop {sum }limits_{k = i}^{K = 5} beta _{2,i,k}x_{s,i,t}x_{k,s,t}}right. \ & quad quad left. {+ mathop {sum }limits_{i = 1}^{I = 5} mathop {sum }limits_{k = 1, k neq i}^{K = 5} beta _{3,i,k}x_{s,i,t}x_{k,s,t}} right)end{array} {rm{Revised}} {rm{Eq}}. (5)$$$$begin{array}{ll}fleft( {x_{s,t}} right) \ = expleft( {beta _0 + mathop {sum }limits_{i = 1}^{I = 5} mathop {sum }limits_{j = 1}^{J = 2} beta _{0,i,j,}x_{i,s,t}^j + mathop {sum }limits_{i = 1}^{I = 5} mathop {sum }limits_{k = i + 1}^{K = 6} beta _{1,i,k}x_{i,s,t}x_{k,s,t}} right) {mathrm{Original}} {rm{Eq}}. (5)end{array}$$$$y_{s,i,z} = left{ {begin{array}{*{20}{l}} {y_{s,i,1} = left| {Delta x_{s,i}} right|,} hfill & {y_{s,i,2} = 0,} hfill & {{{{mathrm{if}}}},Delta x_{s,i} < 0} hfill \ {y_{s,i,1} = 0,} hfill & {y_{s,i,2} = Delta x_{s,i}} hfill & {{{{mathrm{otherwise}}}}} hfill end{array}} right. {rm{Revised}} {rm{Eq}}. (6)$$$$x_{i,s,} = left{ {begin{array}{*{20}{l}} {x_{1,i,s} = left| {Delta x_{i,s}} right|,} hfill & {x_{2,i,s} = 0,} hfill & {if,Delta x_{i,s} < 0} hfill \ {x_{1,i,s} = 0,} hfill & {x_{2,i,s} = Delta x_{i,s},} hfill & {otherwise} hfill end{array}} right. {rm{Original}} {rm{Eq}}. (6)$$$$omega left( {y_{s,i,z};gamma } right) = {{{mathrm{exp}}}}left( {mathop {sum }limits_{i = 1}^{I = 5} mathop {sum }limits_{z = 1}^{Z = 2} - gamma _{i,z} y_{s,i,z}} right) {rm{Revised}} {rm{Eq}}. (7)$$$$omega left( {Delta x_{s,t_1,t_2};gamma } right) = expleft( {mathop {sum }limits_{i = 1}^{I = 5} - gamma _{i,z}Delta x_{z,s,i}} right) {rm{Original}} {rm{Eq}}. (7)$$All changes have been made in the HTML and PDF versions of the article. More

Brett, C. E. Sequence stratigraphy, paleoecology, and evolution: biotic clues and responses to sea-level fluctuations. Palaios 13, 241–262 (1998).Article 

Google Scholar 
Brett, C. E., Hendy, A. J. W., Bartholomew, A. J., Bonelli, J. R. & McLaughlin, P. I. Response of shallow marine biotas to sea-level fluctuations: a review of faunal replacement and the process of habitat tracking. Palaios 22, 228–244 (2007).Article 

Google Scholar 
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).Article 

Google Scholar 
Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).CAS 
PubMed 
Article 

Google Scholar 
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).CAS 
PubMed 
Article 

Google Scholar 
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).PubMed 
Article 

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

Google Scholar 
Rillo, M. C., Woolley, S. & Hillebrand, H. Drivers of global pre‐industrial patterns of species turnover in planktonic foraminifera. Ecography 2022, e05892 (2021).Article 

Google Scholar 
Van der Putten, W. H., Macel, M. & Visser, M. E. Predicting species distribution and abundance responses to climate change: why it is essential to include biotic interactions across trophic levels. Phil. Trans. R. Soc. B 365, 2025–2034 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Antão, L. H. et al. Temperature-related biodiversity change across temperate marine and terrestrial systems. Nat. Ecol. Evol. 4, 927–933 (2020).PubMed 
Article 

Google Scholar 
Chen, I. C. et al. Asymmetric boundary shifts of tropical montane Lepidoptera over four decades of climate warming. Glob. Ecol. Biogeogr. 20, 34–45 (2011).Article 

Google Scholar 
García Molinos, J. et al. Climate velocity and the future global redistribution of marine biodiversity. Nat. Clim. Change 6, 83–88 (2015).Article 

Google Scholar 
Beaugrand, G., Edwards, M., Raybaud, V., Goberville, E. & Kirby, R. R. Future vulnerability of marine biodiversity compared with contemporary and past changes. Nat. Clim. Change 5, 695–701 (2015).Article 

Google Scholar 
Benedetti, F. et al. Major restructuring of marine plankton assemblages under global warming. Nat. Commun. 12, 5226 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Occhipinti-Ambrogi, A. Global change and marine communities: alien species and climate change. Mar. Pollut. Bull. 55, 342–352 (2007).CAS 
PubMed 
Article 

Google Scholar 
Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).Article 

Google Scholar 
Burrows, M. T. et al. Ocean community warming responses explained by thermal affinities and temperature gradients. Nat. Clim. Change 9, 959–963 (2019).Article 

Google Scholar 
Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Jonkers, L. et al. Integrating palaeoclimate time series with rich metadata for uncertainty modelling: strategy and documentation of the PalMod 130k marine palaeoclimate data synthesis. Earth Syst. Sci. Data 12, 1053–1081 (2020).Article 

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

Google Scholar 
Yasuhara, M., Tittensor, D. P., Hillebrand, H. & Worm, B. Combining marine macroecology and palaeoecology in understanding biodiversity: microfossils as a model. Biol. Rev. 92, 199–215 (2017).PubMed 
Article 

Google Scholar 
Aze, T. et al. A phylogeny of Cenozoic macroperforate planktonic foraminifera from fossil data. Biol. Rev. 86, 900–927 (2011).PubMed 
Article 

Google Scholar 
Takagi, H. et al. Characterizing photosymbiosis in modern planktonic foraminifera. Biogeosciences 16, 3377–3396 (2019).CAS 
Article 

Google Scholar 
Schiebel, R. & Hemleben, C. Planktic Foraminifers in the Modern Ocean (Springer, 2017).Morey, A. E., Mix, A. C. & Pisias, N. G. Planktonic foraminiferal assemblages preserved in surface sediments correspond to multiple environment variables. Quat. Sci. Rev. 24, 925–950 (2005).Article 

Google Scholar 
Fenton, I. S., Pearson, P. N., Dunkley Jones, T. & Purvis, A. Environmental predictors of diversity in recent planktonic foraminifera as recorded in marine sediments. PLoS ONE 11, e0165522 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Rutherford, S., D’Hondt, S. & Prell, W. Environmental controls on the geographic distribution of zooplankton diversity. Nature 400, 749–753 (1999).CAS 
Article 

Google Scholar 
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).CAS 
PubMed 
Article 

Google Scholar 
Yasuhara, M., Hunt, G., Dowsett, H. J., Robinson, M. M. & Stoll, D. K. Latitudinal species diversity gradient of marine zooplankton for the last three million years. Ecol. Lett. 15, 1174–1179 (2012).PubMed 
Article 

Google Scholar 
Yasuhara, M. et al. Past and future decline of tropical pelagic biodiversity. Proc. Natl Acad. Sci. USA 117, 12891–12896 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jonkers, L., Hillebrand, H. & Kucera, M. Global change drives modern plankton communities away from the pre-industrial state. Nature 570, 372–375 (2019).CAS 
PubMed 
Article 

Google Scholar 
Beaugrand, G., Reid, P. C., Ibañez, F., Lindley, J. A. & Edwards, M. Reorganization of North Atlantic marine copepod biodiversity and climate. Science 296, 1692–1694 (2002).CAS 
PubMed 
Article 

Google Scholar 
Hinder, S. L. et al. Changes in marine dinoflagellate and diatom abundance under climate change. Nat. Clim. Change 2, 271–275 (2012).Article 

Google Scholar 
Southward, A. J., Hawkins, S. J. & Burrows, M. T. Seventy years’ observations of changes in distribution and abundance of zooplankton and intertidal organisms in the western English Channel in relation to rising sea temperature. J. Therm. Biol. 20, 127–155 (1995).Article 

Google Scholar 
Fenton, I. S. et al. Triton, a new species-level database of Cenozoic planktonic foraminiferal occurrences. Sci. Data 8, 160 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Kucera, M., Rosell-Melé, A., Schneider, R., Waelbroeck, C. & Weinelt, M. Multiproxy approach for the reconstruction of the glacial ocean surface (MARGO). Quat. Sci. Rev. 24, 813–819 (2005).Kucera, M. et al. Reconstruction of sea-surface temperatures from assemblages of planktonic foraminifera: multi-technique approach based on geographically constrained calibration data sets and its application to glacial Atlantic and Pacific Oceans. Quat. Sci. Rev. 24, 951–998 (2005).Article 

Google Scholar 
Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. Sci. Data 4, 170109 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Ezard, T. H. G., Aze, T., Pearson, P. N. & Purvis, A. Interplay between changing climate and species’ ecology drives macroevolutionary dynamics. Science 332, 349–351 (2011).CAS 
PubMed 
Article 

Google Scholar 
Fenton, I. S. et al. The impact of Cenozoic cooling on assemblage diversity in planktonic foraminifera. Phil. Trans. R. Soc. B 371, 20150224 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Lowery, C. M. & Fraass, A. J. Morphospace expansion paces taxonomic diversification after end Cretaceous mass extinction. Nat. Ecol. Evol. 3, 900–904 (2019).PubMed 
Article 

Google Scholar 
Wade, B. S., Pearson, P. N., Berggren, W. A. & Pälike, H. Review and revision of Cenozoic tropical planktonic foraminiferal biostratigraphy and calibration to the geomagnetic polarity and astronomical time scale. Earth Sci. Rev. 104, 111–142 (2011).Article 

Google Scholar 
Antell, G. S., Fenton, I. S., Valdes, P. J. & Saupe, E. E. Thermal niches of planktonic foraminifera are static throughout glacial-interglacial climate change. Proc. Natl. Acad. Sci. USA https://doi.org/10.1073/pnas.2017105118 (2021).Fauth, J. E. et al. Simplifying the jargon of community ecology: a conceptual approach. Am. Nat. 147, 282–286 (1996).Article 

Google Scholar 
Jackson, S. T. & Overpeck, J. T. Responses of plant populations and communities to environmental changes of the late Quaternary. Paleobiology 26, 194–220 (2000).Article 

Google Scholar 
Bard, E., Rostek, F., Turon, J.-L. & Gendreau, S. Hydrological impact of Heinrich events in the subtropical Northeast Atlantic. Science 289, 1321–1324 (2000).CAS 
PubMed 
Article 

Google Scholar 
Broecker, W. S. Massive iceberg discharges as triggers for global climate change. Nature 372, 421–424 (1994).CAS 
Article 

Google Scholar 
Ruddiman, W. F. Late Quaternary deposition of ice-rafted sand in the subpolar North Atlantic (lat 40° to 65°N). Geol. Soc. Am. Bull. 88, 1813–1827 (1977).Article 

Google Scholar 
Hubbell, S. P. The Unified Neutral Theory of Biodiversity and Biogeography (Princeton Univ. Press, 2001).Liow, L. H., Van Valen, L. & Stenseth, N. C. Red Queen: from populations to taxa and communities. Trends Ecol. Evol. 26, 349–358 (2011).PubMed 
Article 

Google Scholar 
Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).Article 

Google Scholar 
Jackson, S. T. & Sax, D. F. Balancing biodiversity in a changing environment: extinction debt, immigration credit and species turnover. Trends Ecol. Evol. 25, 153–160 (2010).PubMed 
Article 

Google Scholar 
Williams, J. W., Ordonez, A. & Svenning, J. C. A unifying framework for studying and managing climate-driven rates of ecological change. Nat. Ecol. Evol. 5, 17–26 (2021).PubMed 
Article 

Google Scholar 
Van Meerbeeck, C. J., Renssen, H. & Roche, D. M. How did Marine Isotope Stage 3 and Last Glacial Maximum climates differ? Perspectives from equilibrium simulations. Clim. Past 5, 33–51 (2009).Article 

Google Scholar 
Jonkers, L. & Kučera, M. Global analysis of seasonality in the shell flux of extant planktonic Foraminifera. Biogeosciences 12, 2207–2226 (2015).Article 

Google Scholar 
Ofstad, S. et al. Development, productivity, and seasonality of living planktonic foraminiferal faunas and Limacina helicina in an area of intense methane seepage in the Barents Sea. J. Geophys. Res. Biogeosci. 125, e2019JG005387 (2020).CAS 
Article 

Google Scholar 
Bova, S., Rosenthal, Y., Liu, Z., Godad, S. P. & Yan, M. Seasonal origin of the thermal maxima at the Holocene and the last interglacial. Nature 589, 548–553 (2021).CAS 
PubMed 
Article 

Google Scholar 
Rillo, M. C. et al. On the mismatch in the strength of competition among fossil and modern species of planktonic Foraminifera. Glob. Ecol. Biogeogr. 28, 1866–1878 (2019).Article 

Google Scholar 
Lisiecki, L. E. & Stern, J. V. Regional and global benthic δ18O stacks for the last glacial cycle. Paleoceanography 31, 1368–1394 (2016).Article 

Google Scholar 
Reimer, P. J. et al. IntCal13 and Marine13 radiocarbon age calibration curves 0–50,000 years cal BP. Radiocarbon 55, 1869–1887 (2013).CAS 
Article 

Google Scholar 
Butzin, M., Köhler, P. & Lohmann, G. Marine radiocarbon reservoir age simulations for the past 50,000 years. Geophys. Res. Lett. 44, 8473–8480 (2017).CAS 
Article 

Google Scholar 
Langner, M. & Mulitza, S. Technical Note: PaleoDataView—A software toolbox for the collection, homogenization and visualization of marine proxy data. Clim 15, 2067–2072 (2019).
Google Scholar 
Mix, A. C., Bard, E. & Schneider, R. Environmental processes of the ice age: land, oceans, glaciers (EPILOG). Quat. Sci. Rev. 20, 627–657 (2001).Article 

Google Scholar 
Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. Nature 599, 239–244 (2021).CAS 
PubMed 
Article 

Google Scholar 
Horn, H. S. Measurement of ‘overlap’ in comparative ecological studies. Am. Nat. 100, 419–424 (1966).Article 

Google Scholar 
Jost, L., Chao, A. & Chazdon, R. L. in Biological diversity: frontiers in measurement and assessment (eds Anne E. Magurran & Brian J. McGill) 66–84 (Oxford University Press, 2011).Ferrier, S., Manion, G., Elith, J. & Richardson, K. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers. Distrib. 13, 252–264 (2007).Article 

Google Scholar 
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 379–423 (1948).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Firke, S. janitor: Simple tools for examining and cleaning dirty data. R package version 2.1.0 https://CRAN.R-project.org/package=janitor (2021).Oksanen, J. et al. vegan: Community ecology package. R package version 2.5-7 https://CRAN.R-project.org/package=vegan (2020).Hallett, L. M. et al. codyn: an R package of community dynamics metrics. Methods Ecol. Evol. 7, 1146–1151 (2016).Article 

Google Scholar 
Juggins, S. rioja: Analysis of quaternary science data. R package version 0.9-26 https://cran.r-project.org/package=rioja (2020).Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).Article 

Google Scholar 
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Hijmans, R. J. raster: Geographic data analysis and modeling. R package version 3.4-13 https://CRAN.R-project.org/package=raster (2021).Garnier, S. viridis: Default color maps from ‘matplotlib’. R package version 0.6.1 https://CRAN.R-project.org/package=viridis (2021.)Locarnini, R. A. et al. World Ocean Atlas 2018, Vol. 1: Temperature. NOAA Atlas NESDIS 81 (NOAA, 2019). More

Cockell, C. S. et al. Habitability: a review. Astrobiology 16, 89–117 (2016).ADS 
Article 

Google Scholar 
Michalski, J. R. et al. The Martian subsurface as a potential window into the origin of life. Nat. Geosci. 11, 21–26 (2018).ADS 
Article 

Google Scholar 
Fairén, A. G. et al. Stability against freezing of aqueous solutions on early Mars. Nature 459, 401–404 (2009).ADS 
Article 

Google Scholar 
Clifford, S. M. et al. Depth of the Martian cryosphere: Revised estimates and implications for the existence and detection of subpermafrost groundwater. J. Geophys. Res. 115, E07001 (2010).ADS 
Article 

Google Scholar 
Rivera-Valentín, E. G., Chevrier, V. F., Soto, A. & Martínez, G. Distribution and habitability of (meta)stable brines on present-day Mars. Nat. Astron. 4, 756–761 (2020).ADS 
Article 

Google Scholar 
Stevens, A. H., Patel, M. R. & Lewis, S. R. Numerical modelling of the transport of trace gases including methane in the subsurface of Mars. Icarus 250, 587–594 (2015).ADS 
Article 

Google Scholar 
Sholes, S. F., Krissansen-Totton, J. & Catling, D. C. A maximum subsurface biomass on mars from untapped free energy: CO and H2 as potential antibiosignatures. Astrobiology 19, 655–668 (2019).ADS 
Article 

Google Scholar 
Wordsworth, R. D. The climate of early Mars. Annu. Rev. Earth Planet. Sci. 44, 381–408 (2016).ADS 
Article 

Google Scholar 
Liu, J. et al. Anoxic chemical weathering under a reducing greenhouse on early Mars. Nat. Astron. 5, 503–509 (2021).ADS 
Article 

Google Scholar 
Battistuzzi, F. U., Feijao, A. & Hedges, S. B. A genomic timescale of prokaryote evolution: insights into the origin of methanogenesis, phototrophy, and the colonization of land. BMC Evol. Biol. 4, 44 (2004).Article 

Google Scholar 
Martin, W. F. & Sousa, F. L. Early microbial evolution: the age of anaerobes. Cold Spring Harbor Perspect. Biol 8, a018127 (2016).Article 

Google Scholar 
Sauterey, B. et al. Co-evolution of primitive methane-cycling ecosystems and early Earth’s atmosphere and climate. Nat. Commun. 11, 2705 (2020).ADS 
Article 

Google Scholar 
Affholder, A. et al. Bayesian analysis of Enceladus’s plume data to assess methanogenesis. Nat. Astron. 5, 805–814 (2021).ADS 
Article 

Google Scholar 
Wordsworth, R. et al. Transient reducing greenhouse warming on early Mars. Geophys. Res. Lett. 44, 665–671 (2017).ADS 
Article 

Google Scholar 
Turbet, M., Boulet, C. & Karman, T. Measurements and semi-empirical calculations of CO2 + CH4 and CO2 + H2 collision-induced absorption across a wide range of wavelengths and temperatures. Application for the prediction of early Mars surface temperature. Icarus 346, 113762 (2020).Article 

Google Scholar 
Price, P. B. & Sowers, T. Temperature dependence of metabolic rates for microbial growth, maintenance, and survival. Proc. Nat. Acad. Sci. USA 101, 4631–4636 (2004).ADS 
Article 

Google Scholar 
Taubner, R.-S. et al. Biological methane production under putative Enceladus-like conditions. Nat. Commun. 9, 748 (2018).ADS 
Article 

Google Scholar 
Ramirez, R. M. A warmer and wetter solution for early Mars and the challenges with transient warming. Icarus 297, 71–82 (2017).ADS 
Article 

Google Scholar 
Kharecha, P., Kasting, J. & Siefert, J. A coupled atmosphere–ecosystem model of the early Archean Earth. Geobiology 3, 53–76 (2005).Article 

Google Scholar 
Tarnas, J. D. et al. Radiolytic H2 production on Noachian Mars: implications for habitability and atmospheric warming. Earth Planet. Sci. Lett. 502, 133–145 (2018).ADS 
Article 

Google Scholar 
Yung, Y. L. et al. Methane on Mars and habitability: challenges and responses. Astrobiology 18, 1221–1242 (2018).ADS 
Article 

Google Scholar 
Knutsen, E. W. et al. Comprehensive investigation of Mars methane and organics with ExoMars/NOMAD. Icarus 357, 114266 (2021).Article 

Google Scholar 
Cockell, C. S. Trajectories of martian habitability. Astrobiology 14, 182–203 (2014).ADS 
Article 

Google Scholar 
Westall, F. et al. Biosignatures on Mars: What, where, and how? Implications for the search for Martian life. Astrobiology 15, 998–1029 (2015).ADS 
Article 

Google Scholar 
Lepot, K. Signatures of early microbial life from the Archean (4 to 2.5 Ga) eon. Earth Sci. Rev. 209, 103296 (2020).Article 

Google Scholar 
Fastook, J. L. & Head, J. W. Glaciation in the late noachian icy highlands: Ice accumulation, distribution, flow rates, basal melting, and top-down melting rates and patterns. Planet. Space Sci. 106, 82–98 (2015).ADS 
Article 

Google Scholar 
Fassett, C. I. & Head, J. W. Valley network-fed, open-basin lakes on Mars: distribution and implications for Noachian surface and subsurface hydrology. Icarus 198, 37–56 (2008).ADS 
Article 

Google Scholar 
Tanaka, K. L. et al. Geologic Map of Mars: U.S. Geological Survey Scientific Investigations Map 3292, Scale 1000,000 (US Geological Survey, 2014); https://doi.org/10.3133/sim3292Sun, V. Z. & Stack, K. M. Geologic Map of Jezero Crater and the Nili Planum Region, Mars: U.S. Geological Survey Scientific Investigations Map 3464, Scale 1000 (US Geological Survey, 2020); https://doi.org/10.3133/sim3464Ward, P. The Medea Hypothesis (Princeton Univ. Press, 2009).Chopra, A. & Lineweaver, C. H. The Case for a Gaian bottleneck: the biology of habitability. Astrobiology 16, 7–22 (2016).ADS 
Article 

Google Scholar 
Arney, G. et al. The Pale Orange Dot: The Spectrum and Habitability of Hazy Archean Earth. Astrobiology 16, 873–899 (2016).Batalha, N. et al. Testing the early Mars H2-CO2 greenhouse hypothesis with a 1-D photochemical model. Icarus 258, 337–349 (2015).ADS 
Article 

Google Scholar 
Stüeken, E. E. et al. Isotopic evidence for biological nitrogen fixation by molybdenum-nitrogenase from 3.2 Gyr. Nature 520, 666–669 (2015).ADS 
Article 

Google Scholar 
Cockell, C. S. et al. Minimum units of habitability and their abundance in the universe. Astrobiology 21, 481–489 (2021).ADS 
Article 

Google Scholar 
Adams, D. et al. Nitrogen fixation at early Mars. Astrobiology 21, 968–980 (2021).ADS 
Article 

Google Scholar 
Fergason, R. L., Hare, T. M. and Laura, J. HRSC and MOLA Blended Digital Elevation Model at 200m v2. Astrogeology PDS Annex (US Geological Survey, 2018); http://bit.ly/HRSC_MOLA_Blend_v0Sauterey, B. MarsEcosys v.1.0. Zenodo https://doi.org/10.5281/zenodo.6963348 (2022). More

Seasonal fluctuations in animal population dynamics are among the most fundamental attributes of life on Earth. A long recognized but poorly understood example is the dramatic seasonal fluctuation in the abundance of malaria vectors in the semi-arid savannah and Sahel regions of Africa. In these regions, the vector mosquitoes largely disappear during a prolonged 3- to 8-month dry season, when lack of rain causes the aquatic larval habitats to disappear. As a result, malaria transmission plummets. When the rains return, the mosquito vectors rapidly reappear, leading to a resurgence of malaria transmission. How the vector populations are able to persist through the prolonged dry season and rapidly rebound with the onset of rains is referred to as the ‘dry-season malaria paradox’, and has remained an enduring mystery of malariology for nearly 100 years. Writing in Nature Ecology & Evolution, Faiman et al.1 help to resolve this mystery by using an innovative isotopic labelling strategy: they demonstrate that at least approximately 20% of the local population of the malaria vector Anopheles coluzzi in the West African Sahel survive the dry season locally by undergoing summer dormancy, known as aestivation. More

Seibel, B. A. & Birk, M. A. Nat. Clim. Change https://doi.org/10.1038/s41558-022-01491-6 (2022).Article 

Google Scholar 
Urban, M. C. et al. Science 353, aad8466 (2016).Article 

Google Scholar 
Pörtner, H. O. & Knust, R. Science 315, 95–97 (2007).Article 

Google Scholar 
Verberk, W. C. E. P., Bilton, D. T., Calosi, P. & Spicer, J. I. Ecology 92, 1565–1572 (2011).Article 

Google Scholar 
Rubalcaba, J. G., Verberk, W. C., Hendriks, A. J., Saris, B. & Woods, H. A. Proc. Natl Acad. Sci. USA 117, 31963–31968 (2020).CAS 
Article 

Google Scholar 
Deutsch, C. et al. Proc. Natl Acad. Sci. USA 119, e2201345119 (2022).CAS 
Article 

Google Scholar 
Stramma, L. et al. Nat. Clim. Change 2, 33–37 (2012).CAS 
Article 

Google Scholar 
Vergés, A. et al. Proc. R. Soc. B 281, 20140846 (2014).Article 

Google Scholar  More

Knowlton, N. et al. in Life in the World’s Oceans 65–78 (Wiley-Blackwell, 2010).Fisher, R. et al. Species richness on coral reefs and the pursuit of convergent global estimates. Curr. Biol. 25, 500–505. https://doi.org/10.1016/j.cub.2014.12.022 (2015).CAS 
Article 
PubMed 

Google Scholar 
Brandl, S. J., Goatley, C. H. R., Bellwood, D. R. & Tornabene, L. The hidden half: Ecology and evolution of cryptobenthic fishes on coral reefs. Biol. Rev. 93, 1846–1873. https://doi.org/10.1111/brv.12423 (2018).Article 
PubMed 

Google Scholar 
Appeltans, W. et al. The magnitude of global marine species diversity. Curr. Biol. 22, 2189–2202. https://doi.org/10.1016/j.cub.2012.09.036 (2012).CAS 
Article 
PubMed 

Google Scholar 
Carvalho, S. et al. Beyond the visual: Using metabarcoding to characterize the hidden reef cryptobiome. Proc. R. Soc. B Biol. Sci. https://doi.org/10.1098/rspb.2018.2697 (2019).Article 

Google Scholar 
Kramer, M. J., Bellwood, O., Fulton, C. J. & Bellwood, D. R. Refining the invertivore: Diversity and specialisation in fish predation on coral reef crustaceans. Mar. Biol. 162, 1779–1786. https://doi.org/10.1007/s00227-015-2710-0 (2015).CAS 
Article 

Google Scholar 
Brandl, S. J. et al. Demographic dynamics of the smallest marine vertebrates fuel coral reef ecosystem functioning. Science 364, 1189–1192. https://doi.org/10.1126/science.aav3384 (2019).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Kramer, M. J., Bellwood, D. R. & Bellwood, O. Cryptofauna of the epilithic algal matrix on an inshore coral reef, Great Barrier Reef. Coral Reefs 31, 1007–1015. https://doi.org/10.1007/s00338-012-0924-x (2012).ADS 
Article 

Google Scholar 
Rocha, L. A. et al. Specimen collection: An essential tool. Science 344, 814–815. https://doi.org/10.1126/science.344.6186.814 (2014).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Berumen, M. L. et al. The status of coral reef ecology research in the Red Sea. Coral Reefs 32, 737–748. https://doi.org/10.1007/s00338-013-1055-8 (2013).ADS 
Article 

Google Scholar 
Paknia, O., Sh, H. R. & Koch, A. Lack of well-maintained natural history collections and taxonomists in megadiverse developing countries hampers global biodiversity exploration. Org. Divers. Evol. 15, 619–629. https://doi.org/10.1007/s13127-015-0202-1 (2015).Article 

Google Scholar 
Knowlton, N. & Leray, M. Censusing marine life in the twentyfirst Century. Genome 58, 238–238 (2015).
Google Scholar 
Yu, D. W. et al. Biodiversity soup: Metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol. Evol. 3, 613–623. https://doi.org/10.1111/j.2041-210X.2012.00198.x (2012).Article 

Google Scholar 
Ransome, E. et al. The importance of standardization for biodiversity comparisons: A case study using autonomous reef monitoring structures (ARMS) and metabarcoding to measure cryptic diversity on Mo’orea coral reefs, French Polynesia. PLoS ONE https://doi.org/10.1371/journal.pone.0175066 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Coker, D. J., DiBattista, J. D., Sinclair-Taylor, T. H. & Berumen, M. L. Spatial patterns of cryptobenthic coral-reef fishes in the Red Sea. Coral Reefs 37, 193–199. https://doi.org/10.1007/s00338-017-1647-9 (2018).ADS 
Article 

Google Scholar 
Pearman, J. K. et al. Cross-shelf investigation of coral reef cryptic benthic organisms reveals diversity patterns of the hidden majority. Sci. Rep. 8, 8090. https://doi.org/10.1038/s41598-018-26332-5 (2018).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Pearman, J. K. et al. Disentangling the complex microbial community of coral reefs using standardized Autonomous Reef Monitoring Structures (ARMS). Mol. Ecol. 28, 3496–3507. https://doi.org/10.1111/mec.15167 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Selkoe, K. A. et al. The DNA of coral reef biodiversity: Predicting and protecting genetic diversity of reef assemblages. Proc. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rspb.2016.0354 (2016).Article 

Google Scholar 
DiBattista, J. D. et al. Digging for DNA at depth: Rapid universal metabarcoding surveys (RUMS) as a tool to detect coral reef biodiversity across a depth gradient. PeerJ https://doi.org/10.7717/peerj.6379 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
DiBattista, J. D. et al. Assessing the utility of eDNA as a tool to survey reef-fish communities in the Red Sea. Coral Reefs 36, 1245–1252. https://doi.org/10.1007/s00338-017-1618-1 (2017).ADS 
Article 

Google Scholar 
Nester, G. M. et al. Development and evaluation of fish eDNA metabarcoding assays facilitate the detection of cryptic seahorse taxa (family: Syngnathidae). Environ. DNA 2, 614–626 (2020).Article 

Google Scholar 
West, K. M. et al. eDNA metabarcoding survey reveals fine-scale coral reef community variation across a remote, tropical island ecosystem. Mol. Ecol. 29, 1069–1086. https://doi.org/10.1111/mec.15382 (2020).CAS 
Article 
PubMed 

Google Scholar 
DiBattista, J. D. et al. Environmental DNA can act as a biodiversity barometer of anthropogenic pressures in coastal ecosystems. Sci. Rep. https://doi.org/10.1038/s41598-020-64858-9 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Worm, B. et al. Impacts of biodiversity loss on ocean ecosystem services. Science 314, 787–790. https://doi.org/10.1126/science.1132294 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Spalding, M. et al. Mapping the global value and distribution of coral reef tourism. Mar. Policy 82, 104–113. https://doi.org/10.1016/j.marpol.2017.05.014 (2017).Article 

Google Scholar 
Thomsen, P. F. & Willerslev, E. Environmental DNA – An emerging tool in conservation for monitoring past and present biodiversity. Biol. Cons. 183, 4–18. https://doi.org/10.1016/j.biocon.2014.11.019 (2015).Article 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83. https://doi.org/10.1126/science.aan8048 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Monroe, A. A. et al. In situ observations of coral bleaching in the central Saudi Arabian Red Sea during the 2015/2016 global coral bleaching event. PLoS ONE https://doi.org/10.1371/journal.pone.0195814 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Roth, F. et al. Coral reef degradation affects the potential for reef recovery after disturbance. Mar. Environ. Res. 142, 48–58. https://doi.org/10.1016/j.marenvres.2018.09.022 (2018).CAS 
Article 
PubMed 

Google Scholar 
Foster, T. & Gilmour, J. P. Seeing red: Coral larvae are attracted to healthy-looking reefs. Mar. Ecol. Prog. Ser. 559, 65–71. https://doi.org/10.3354/meps11902 (2016).ADS 
CAS 
Article 

Google Scholar 
Karcher, D. B. et al. Nitrogen eutrophication particularly promotes turf algae in coral reefs of the central Red Sea. PeerJ https://doi.org/10.7717/peerj.8737 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Pancrazi, I., Ahmed, H., Cerrano, C. & Montefalcone, M. Synergic effect of global thermal anomalies and local dredging activities on coral reefs of the Maldives. Marine Pollut. Bull. https://doi.org/10.1016/j.marpolbul.2020.111585 (2020).Article 

Google Scholar 
Vercelloni, J. et al. Forecasting intensifying disturbance effects on coral reefs. Glob. Change Biol. 26, 2785–2797. https://doi.org/10.1111/gcb.15059 (2020).ADS 
Article 

Google Scholar 
González-Barrios, F. J., Cabral-Tena, R. A. & Alvarez-Filip, L. Recovery disparity between coral cover and the physical functionality of reefs with impaired coral assemblages. Glob. Change Biol. 27, 640–651. https://doi.org/10.1111/gcb.15431 (2020).ADS 
Article 

Google Scholar 
Rice, M. M., Ezzat, L. & Burkepile, D. E. Corallivory in the anthropocene: Interactive effects of anthropogenic stressors and corallivory on coral reefs. Front. Marine Sci. https://doi.org/10.3389/fmars.2018.00525 (2019).Article 

Google Scholar 
Lin, Y.-J. et al. Long-term ecological changes in fishes and macro-invertebrates in the world’s warmest coral reefs. Sci. Total Environ. 750, 142254. https://doi.org/10.1016/j.scitotenv.2020.142254 (2021).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Loreau, M. & de Mazancourt, C. Biodiversity and ecosystem stability: A synthesis of underlying mechanisms. Ecol. Lett. 16, 106–115. https://doi.org/10.1111/ele.12073 (2013).Article 
PubMed 

Google Scholar 
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67. https://doi.org/10.1038/nature11148 (2012).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Handley, L. L. How will the “molecular revolution’ contribute to biological recording?. Biol. J. Lin. Soc. 115, 750–766. https://doi.org/10.1111/bij.12516 (2015).Article 

Google Scholar 
Ducklow, H. W., Doney, S. C. & Steinberg, D. K. Contributions of long-term research and time-series observations to marine ecology and biogeochemistry. Ann. Rev. Mar. Sci. 1, 279–302. https://doi.org/10.1146/annurev.marine.010908.163801 (2009).Article 
PubMed 

Google Scholar 
Hughes, B. B. et al. Long-term studies contribute disproportionately to ecology and policy. Bioscience 67, 271–281. https://doi.org/10.1093/biosci/biw185 (2017).Article 

Google Scholar 
Kraft, N. J. B. et al. Community assembly, coexistence and the environmental filtering metaphor. Funct. Ecol. 29, 592–599. https://doi.org/10.1111/1365-2435.12345 (2015).Article 

Google Scholar 
Leibold, M. A. et al. The metacommunity concept: A framework for multi-scale community ecology. Ecol. Lett. 7, 601–613. https://doi.org/10.1111/j.1461-0248.2004.00608.x (2004).Article 

Google Scholar 
Vellend, M. The Theory of Ecological Communities (MPB-57). (Princeton University Press, 2016).Condon, R. H. et al. Recurrent jellyfish blooms are a consequence of global oscillations. Proc. Natl. Acad. Sci. U.S.A. 110, 1000–1005. https://doi.org/10.1073/pnas.1210920110 (2013).ADS 
Article 
PubMed 

Google Scholar 
Boero, F., Kraberg, A. C., Krause, G. & Wiltshire, K. H. Time is an affliction: Why ecology cannot be as predictive as physics and why it needs time series. J. Sea Res. 101, 12–18. https://doi.org/10.1016/j.seares.2014.07.008 (2015).ADS 
Article 

Google Scholar 
Pearman, J. K., Anlauf, H., Irigoien, X. & Carvalho, S. Please mind the gap – Visual census and cryptic biodiversity assessment at central Red Sea coral reefs. Mar. Environ. Res. 118, 20–30. https://doi.org/10.1016/j.marenvres.2016.04.011 (2016).CAS 
Article 
PubMed 

Google Scholar 
David, R. et al. Lessons from photo analyses of autonomous reef monitoring structures as tools to detect (bio-)geographical, spatial, and environmental effects. Mar. Pollut. Bull. 141, 420–429. https://doi.org/10.1016/j.marpolbul.2019.02.066 (2019).CAS 
Article 
PubMed 

Google Scholar 
Pennesi, C. & Danovaro, R. Assessing marine environmental status through microphytobenthos assemblages colonizing the autonomous reef monitoring structures (ARMS) and their potential in coastal marine restoration. Mar. Pollut. Bull. 125, 56–65. https://doi.org/10.1016/j.marpolbul.2017.08.001 (2017).CAS 
Article 
PubMed 

Google Scholar 
Chang, J. J. M., Ip, Y. C. A., Bauman, A. G. & Huang, D. MinION-in-ARMS: Nanopore sequencing to expedite barcoding of specimen-rich macrofaunal samples from Autonomous Reef Monitoring Structures. Front. Marine Sci. https://doi.org/10.3389/fmars.2020.00448 (2020).Article 

Google Scholar 
Hazeri, G. et al. Latitudinal species diversity and density of cryptic crustacean (Brachyura and Anomura) in micro-habitat Autonomous Reef Monitoring Structures across Kepulauan Seribu, Indonesia. Biodivers. J. Biol. Divers. 20 (2019).Al-Rshaidat, M. M. D. et al. Deep COI sequencing of standardized benthic samples unveils overlooked diversity of Jordanian coral reefs in the northern Red Sea. Genome 59, 724–737. https://doi.org/10.1139/gen-2015-0208 (2016).CAS 
Article 
PubMed 

Google Scholar 
Pearman, J. K. et al. Pan-regional marine benthic cryptobiome biodiversity patterns revealed by metabarcoding Autonomous Reef Monitoring Structures. Mol. Ecol. https://doi.org/10.1111/mec.15692 (2020).Article 
PubMed 

Google Scholar 
Leray, M. & Knowlton, N. DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. Proc. Natl. Acad. Sci. U.S.A. 112, 2076–2081. https://doi.org/10.1073/pnas.1424997112 (2015).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Obst, M. et al. A marine biodiversity observation network for genetic monitoring of hard-bottom communities (ARMS-MBON). Front. Marine Sci. https://doi.org/10.3389/fmars.2020.572680 (2020).Article 

Google Scholar 
Hughes, T. P. et al. Ecological memory modifies the cumulative impact of recurrent climate extremes. Nat. Clim. Chang. 9, 40–43. https://doi.org/10.1038/s41558-018-0351-2 (2019).ADS 
Article 

Google Scholar 
Hughes, T. P., Kerry, J. T. & Simpson, T. Large-scale bleaching of corals on the Great Barrier Reef. Ecology 99, 501–501. https://doi.org/10.1002/ecy.2092 (2018).CAS 
Article 
PubMed 

Google Scholar 
Furby, K. A., Bouwmeester, J. & Berumen, M. L. Susceptibility of central Red Sea corals during a major bleaching event. Coral Reefs 32, 505–513. https://doi.org/10.1007/s00338-012-0998-5 (2013).ADS 
Article 

Google Scholar 
Froehlich, C. Y. M., Klanten, O. S., Hing, M. L., Dowton, M. & Wong, M. Y. L. Uneven declines between corals and cryptobenthic fish symbionts from multiple disturbances. Sci. Rep. https://doi.org/10.1038/s41598-021-95778-x (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Bellwood, D. R. et al. Coral recovery may not herald the return of fishes on damaged coral reefs. Oecologia 170, 567–573. https://doi.org/10.1007/s00442-012-2306-z (2012).ADS 
Article 
PubMed 

Google Scholar 
Archana, A. & Baker, D. M. Multifunctionality of an urbanized coastal marine ecosystem. Front. Marine Sci. https://doi.org/10.3389/fmars.2020.557145 (2020).Article 

Google Scholar 
Servis, J. A., Reid, B. N., Timmers, M. A., Stergioula, V. & Naro-Maciel, E. Characterizing coral reef biodiversity: Genetic species delimitation in brachyuran crabs of Palmyra Atoll Central Pacific. Mitochondrial DNA Part A 31, 178–189. https://doi.org/10.1080/24701394.2020.1769087 (2020).CAS 
Article 

Google Scholar 
Chaves-Fonnegra, A. et al. Bleaching events regulate shifts from corals to excavating sponges in algae-dominated reefs. Glob. Change Biol. 24, 773–785. https://doi.org/10.1111/gcb.13962 (2018).ADS 
Article 

Google Scholar 
Perry, C. T. & Morgan, K. M. Post-bleaching coral community change on southern Maldivian reefs: Is there potential for rapid recovery?. Coral Reefs 36, 1189–1194. https://doi.org/10.1007/s00338-017-1610-9 (2017).ADS 
Article 

Google Scholar 
DeCarlo, T. M. The past century of coral bleaching in the Saudi Arabian central Red Sea. PeerJ https://doi.org/10.7717/peerj.10200 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Cortés, J. et al. in Coral Reefs of the Eastern Tropical Pacific: Persistence and Loss in a Dynamic Environment (eds Peter W. Glynn, Derek P. Manzello, & Ian C. Enochs) 203–250 (Springer Netherlands, 2017).Enochs, I. C. & Manzello, D. P. Species richness of motile cryptofauna across a gradient of reef framework erosion. Coral Reefs 31, 653–661. https://doi.org/10.1007/s00338-012-0886-z (2012).ADS 
Article 

Google Scholar 
Timmers, M. A. et al. Biodiversity of coral reef cryptobiota shuffles but does not decline under the combined stressors of ocean warming and acidification. Proc. Natl. Acad. Sci. 118, e2103275118. https://doi.org/10.1073/pnas.2103275118 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Khalil, M. T., Bouwmeester, J. & Berumen, M. L. Spatial variation in coral reef fish and benthic communities in the central Saudi Arabian Red Sea. PeerJ https://doi.org/10.7717/peerj.3410 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Roik, A. et al. Year-long monitoring of physico-chemical and biological variables provide a comparative baseline of coral reef functioning in the central Red Sea. PLoS ONE https://doi.org/10.1371/journal.pone.0163939 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Largier, J. L. Considerations in estimating larval dispersal distances from oceanographic data. Ecol. Appl. 13, S71–S89 (2003).Article 

Google Scholar 
Volkov, I., Banavar, J. R., Hubbell, S. P. & Maritan, A. Patterns of relative species abundance in rainforests and coral reefs. Nature 450, 45–49. https://doi.org/10.1038/nature06197 (2007).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Alsaffar, Z., Cúrdia, J., Borja, A., Irigoien, X. & Carvalho, S. Consistent variability in beta-diversity patterns contrasts with changes in alpha-diversity along an onshore to offshore environmental gradient: The case of Red Sea soft-bottom macrobenthos. Mar. Biodivers. 49, 247–262. https://doi.org/10.1007/s12526-017-0791-3 (2017).Article 

Google Scholar 
Alsaffar, Z. et al. The role of seagrass vegetation and local environmental conditions in shaping benthic bacterial and macroinvertebrate communities in a tropical coastal lagoon. Sci. Rep. https://doi.org/10.1038/s41598-020-70318-1 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Rocha, L. A. et al. Mesophotic coral ecosystems are threatened and ecologically distinct from shallow water reefs. Science 361, 281–284. https://doi.org/10.1126/science.aaq1614 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Soininen, J., Lennon, J. J. & Hillebrand, H. A multivariate analysis of beta diversity across organisms and environments. Ecology 88, 2830–2838. https://doi.org/10.1890/06-1730.1 (2007).Article 
PubMed 

Google Scholar 
Chust, G. et al. Dispersal similarly shapes both population genetics and community patterns in the marine realm. Sci. Rep. https://doi.org/10.1038/srep28730 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Gianuca, A. T., Declerck, S. A. J., Lemmens, P. & De Meester, L. Effects of dispersal and environmental heterogeneity on the replacement and nestedness components of beta-diversity. Ecology 98, 525–533. https://doi.org/10.1002/ecy.1666 (2017).Article 
PubMed 

Google Scholar 
Enochs, I. C., Toth, L. T., Brandtneris, V. W., Afflerbach, J. C. & Manzello, D. P. Environmental determinants of motile cryptofauna on an eastern Pacific coral reef. Mar. Ecol. Prog. Ser. 438, 105-U127. https://doi.org/10.3354/meps09259 (2011).ADS 
Article 

Google Scholar 
Hughes, T. P. et al. Coral reefs in the anthropocene. Nature 546, 82–90. https://doi.org/10.1038/nature22901 (2017).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Fabricius, K. E. Effects of terrestrial runoff on the ecology of corals and coral reefs: Review and synthesis. Mar. Pollut. Bull. 50, 125–146. https://doi.org/10.1016/j.marpolbul.2004.11.028 (2005).CAS 
Article 
PubMed 

Google Scholar 
Chaidez, V., Dreano, D., Agusti, S., Duarte, C. M. & Hoteit, I. Decadal trends in Red Sea maximum surface temperature. Sci. Rep. https://doi.org/10.1038/s41598-018-25731-y (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Hubbell, S. P. in Monographs in Population Biology. The unified neutral theory of biodiversity and biogeography Vol. 32 Monographs in Population Biology i-xiv, 1–375 (2001).Dornelas, M., Connolly, S. R. & Hughes, T. P. Coral reef diversity refutes the neutral theory of biodiversity. Nature 440, 80–82. https://doi.org/10.1038/nature04534 (2006).ADS 
CAS 
Article 
PubMed 

Google Scholar 
Baselga, A. Partitioning the turnover and nestedness components of beta diversity. Glob. Ecol. Biogeogr. 19, 134–143. https://doi.org/10.1111/j.1466-8238.2009.00490.x (2010).Article 

Google Scholar 
Legendre, P. Interpreting the replacement and richness difference components of beta diversity. Glob. Ecol. Biogeogr. 23, 1324–1334. https://doi.org/10.1111/geb.12207 (2014).Article 

Google Scholar 
Hollander, M. & Wolfe, D. A. Nonparametric statistical methods. Ergonomics 18, 701–702 (1975).
Google Scholar 
Kohler, K. E. & Gill, S. M. Coral point count with excel extensions (CPCe): A visual basic program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32, 1259–1269. https://doi.org/10.1016/j.cageo.2005.11.009 (2006).ADS 
Article 

Google Scholar 
Geller, J., Meyer, C., Parker, M. & Hawk, H. Redesign of PCR primers for mitochondrial cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic surveys. Mol. Ecol. Resour. 13, 851–861. https://doi.org/10.1111/1755-0998.12138 (2013).CAS 
Article 
PubMed 

Google Scholar 
Hao, X., Jiang, R. & Chen, T. Clustering 16S rRNA for OTU prediction: A method of unsupervised Bayesian clustering. Bioinformatics 27, 611–618. https://doi.org/10.1093/bioinformatics/btq725 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581. https://doi.org/10.1038/nmeth.3869 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Ranwez, V., Harispe, S., Delsuc, F. & Douzery, E. J. P. MACSE: Multiple alignment of coding SEquences accounting for frameshifts and stop codons. PLoS ONE https://doi.org/10.1371/journal.pone.0022594 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Machida, R. J., Leray, M., Ho, S. L. & Knowlton, N. Data Descriptor: Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data https://doi.org/10.1038/sdata.2017.27 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267. https://doi.org/10.1128/aem.00062-07 (2007).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Generate High-Resolution Venn and Euler Plots v. 1.6.20 (2018).Ginestet, C. ggplot2: Elegant graphics for data analysis. J. R. Stat. Soc. Ser. Stat. Soc. 174, 245–245. https://doi.org/10.1111/j.1467-985X.2010.00676_9.x (2011).Article 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE https://doi.org/10.1371/journal.pone.0061217 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46. https://doi.org/10.1111/j.1442-9993.2001.01070.pp.x (2001).Article 

Google Scholar 
Hervé, M. Testing and plotting procedures for biostatistics v. 0.9-79. Retrieved from https://cran.r-project.org/web/packages/RVAideMemoire/index.html (2021).De Caceres, M. & Legendre, P. Associations between species and groups of sites: Indices and statistical inference. Ecology 90, 3566–3574. https://doi.org/10.1890/08-1823.1 (2009).Article 
PubMed 

Google Scholar 
Legendre, P. & Anderson, M. J. Distance-based redundancy analysis: Testing multispecies responses in multifactorial ecological experiments. Ecol. Monogr. 69, 1–24. https://doi.org/10.1890/0012-9615(1999)069[0001:dbratm]2.0.co;2 (1999).Article 

Google Scholar 
Roberts, D. Ordination and multivariate analysis for ecology v. 2.0-1. Retrieved from http://ecology.msu.montana.edu/labdsv/R (2019).Dray, S., Bauman, D., Blanchet, G., Borcard, D., Clappe, S., Guenard, G. & Wagner, H. Adespatial: Multivariate multiscale spatial analysis v. 0.3-13. Retrieved from https://cran.r-project.org/package=adespatial (2021). More

Overview of U.S. domestic migration flowsThe most recent ACS county-to-county flow dataset26 reports that about 45.6 million people migrated to the U.S. per year during the period 2015–2019, which corresponds to 14.2% of the U.S. population27. Approximately 43.5 million annual moves corresponded to domestic migration (moves within the U.S.28), while 2.1 million accounted for inflows of individuals from other countries (viz. international immigration).With respect to domestic migration, 25.7 million people per year migrated within the same county, thus showing that the highest share of domestic flows (59%) is intra-county. Annually, about 10.4 people moved between different counties within the same state, thus intra-state flows account for 24% of the domestic migration (Supplementary Fig. 1), mainly driven by the search for more affordable housing, better jobs, and for family reasons such as change in marital status29. Long distance moves, captured by inter-state flows, represent the remaining 17% of domestic flows, which comprises about 7.5 million moves per year. Here, we will refer to these domestic migration flows as inflows or outflows, and netflows (inflows-outflows).The United States Office of Management and Budget (OMB) classifies counties as metropolitan, micropolitan, or neither30. A metropolitan statistical area contains a core urban area of at least 50,000 population. A metro area represents a functional delineation of an urban area with a network of strong socioeconomic ties, and provision of infrastructure services31,32,33. A micropolitan statistical area contains an urban core of at lest 10,000 but less than 50,000 inhabitants. There are over 380 metropolitan statistical areas in the U.S., each composed of one or more counties, accounting for about 86% of the total U.S. population and comprising approximately 28% of the land area of the country. For this reason, our analysis focuses on the growth dynamics of MSA counties. Supplementary Fig. 2 shows the 3141 counties (administrative subdivisions of the states) in the U.S., comprising about 321 million inhabitants in the starting year of the ACS 5-Year survey period (2015–2019) of our analysis26.Population growth has two components, namely natural growth and migration. Natural growth accounts for births minus deaths, and migration comprises domestic and international migration. With recent trends showing that births and natural increase have declined in the U.S. and in recent years contribute less to overall city population growth34,35, migration patterns become more relevant to the study of city population growth. Because the ACS flow files contain international inflows only, the relative importance of migrations on population growth is here addressed by x = ∣Inflows−Outflows∣/∣Births−Deaths∣ (Supplementary Figs. 3, 4), which is the ratio between domestic netflows and natural growth. The statistical distribution of this quantity computed for all U.S. counties is well fitted by a lognormal distribution, and shows that x≥1 for 76.5% of counties. For most counties, domestic migration dominates population growth, and understanding the spatial structure of domestic netflows (and their distribution within a city) is crucial to the comprehension of the mechanisms behind the heterogeneity of city population growth.At this spatial granularity, we observe a strong heterogeneity among the U.S. counties (Supplementary Fig. 2) for the period 2015 − 2019, along with examples of specific MSAs. In particular, the relative dispersion of counties relative growth due to netflows is higher than one for about 85% of the metro areas, indicating a large heterogeneity within the same city and pointing towards the spatial structure of domestic migration. The observed difference in the netflows stresses the relevance of our approach: counties belonging to the same city may have specific growth rates due to population flow patterns, thus indicating preferential flow destinations and pinpointing the direction in which the city has expanded.Heterogeneity of inter- and intra-city flowsInter-city flows represent the major component of the total flows (~55%), while intra-city flows represent ~25%. Flows between metro and micro areas, and between metro and non-statistical areas are the smallest components, with ~13% and ~7%, respectively. Given that about 80% of the domestic migration are composed of intra- and inter-city flows, we will focus our attention on describing the structure of intra- and inter-city flows, but in the Supplementary Information we offer a brief analysis of flows between metro and micro areas, and between metro and non-statistical areas.Inter-city flows are not uniform across the U.S. cities. The most intense annual netflows ( >2000 people per year), accounting for approximately 17% of the entire inter-city U.S. netflows, are mainly from New York and Chicago to California and Florida (Fig. 2), and from Los Angeles to neighboring cities. Notably, netflows among the Midwestern cities are mostly negative and below the threshold we set. These flows are mainly responsible for increasing or decreasing the population of a given city. Intra-city flow patterns, illustrated with the 7 most populous U.S. cities with more than 5 counties, are also non-uniform.Fig. 2: Heterogeneity of inter- and intra-city netflows.The map (A) suggests that the domestic redistribution of people between different U.S. metro areas are non-uniform: the black arrows, indicating the direction of the most intense inter-city netflows (higher than 2000 people per year), reveal migration trends from northern and eastern cities to western and southern regions. Cities (composed of one or more counties) are colored according to the relative growth (viz. population growth adjusted by population) of the whole MSA during the 2015–2019 period, and the black intensity and the thickness of the arrows are proportional to the netflows. Alaska and Hawaii are not shown. Panels (B–H), which are close-up of New York (B), Chicago (C), Dallas (D), Houston (E), Washington D.C. (F), Philadelphia (G), Atlanta (H), suggest that the most intense intra-city netflows are oriented radially outwards: people are moving from core to external counties. Here, counties are colored according to their relative growth in the 2015–2019 period and the width of the arrows is proportional to the netflows between origin and destination counties.Full size imageOur analysis reveals that city centers (defined as the core county with the highest population density) are more likely to have negative netflows, indicating that people are leaving the central regions of cities. The arrows in Fig. 2 indicate the direction of the most intense netflows, supporting this finding and highlighting that there is a trend of people moving from internal to external regions, contributing to population growth and spatial expansion of U.S. cities. In fact, we found no correlation between relative population growth (viz. population growth by county size) and distance from the core county (Supplementary Fig. 5A) for the 46 cities with more than 5 counties, with relative growth about 0.03 ± 0.05. On the other hand, we found that relative natural growth (Supplementary Fig. 5B) is negatively correlated with the distance to core county, thus natural growth is less relevant as a component of growth in the outer regions of cities. Consequently, our results show that not only the contribution of each component of growth changes with distance to core county, but also that the internal redistribution of people is an important mechanism of growth, mainly in the external counties.We also examined variability in inter- and intra-city flows within the 50 states (Supplementary Fig. 6). Total flows within a state increase, as expected, with the state population. Two special cases are, however, of interest: (1) two states (Vermont and Rhode Island) with small populations have only one MSA, in which case within-state inter-city flows are zero; and (2) nearly 40%, or 149, of MSAs have only one county, in which case intra-city flows could not be estimated. For all other cases, we observe on average an equal split between inter- and intra-city flows, but with considerable variability among the states, with a mean about 0.5 and standard deviation about 0.2. A generalization of the intra- and inter-city migratory patterns for all 46 cities with more than 5 counties shows that the percentage of migrants from intra- and inter-city flows are of the same order of magnitude (Fig. 3).Fig. 3: Roles of intra- and inter-city flows in driving the heterogeneous population growth of cities.We define the core county as the one with the highest population density, and we plot the percentage of inflows due to intra- (A) and inter-city flows (B) of each county within a city as a function of its distance to the core county. The percentage of outflows due to intra- and inter-city flows are shown in (C) and (D), respectively. The positive correlation of the relative growth with distance due to intra-city flows in (E), along with the lack of correlation due to inter-city flows in (F), indicates that intra-city flows are mainly responsible for increasing the population in the external regions of cities. The sizes of red circles and blue squares are proportional to the city population. The range of distances is split into equally spaced bins. The number of counties n within each bin, from left to right, is 46, 1, 4, 7, 7, 17, 21, 31, 36, 38, 34, 31, 31, 30, 20, 20, 21, 14, 17, 9, 9, 6, 4, 2, 5, 5, 2, 1. The black dots and the error bars indicate the mean and the 90% interval, respectively, of the counties within the corresponding bin. We also show the Pearson correlation coefficient R and the p-value associated with the two-sided test of the null hypothesis of non-correlation.Full size imageApart from the core county, flows from the same city correspond to about 50% of the inflow of people in the counties, presenting a slightly positive correlation with their distance from the city center (Fig. 3A). The low percentage for the core county indicates that it is not the major destination of flows from the same city. The percentage of inflows from other cities is higher in the core county and decays as we move towards the suburbs (Fig. 3B). The moderate negative correlation of this percentage with the distance reveals that inflows from other cities are more likely to concentrate in the core regions of a city.The percentage of outflows directed from the core county to other counties within the same city has a slightly negative correlation with the distance of the origin county to the city center, so it is more likely to find intra-city flows with outflows from internal regions (Fig. 3C). The core county is an exception again, suggesting that it is less likely that someone leaving the core county will move to another county within the same city. The slightly negative correlation of the percentage of outflows directed to other cities suggests that there is a trend of people leaving the core county and the central regions to move to other cities (Fig. 3D). The high percentage of inflows (Fig. 3B) and outflows (Fig. 3D) in the central region due to inter-city flows implies that the central regions of cities are more dynamic and diverse and that people tend to move to counties with similar levels of urbanization. The same pattern is observed for flows between metro and micro areas, and for metro and non-statistical areas, allowing us to conclude that people moving from rural areas are more likely to move to the external regions of a city (Supplementary Fig. 7).The positive correlation of the relative growth with the distance due to intra-city flows (Fig. 3E) shows that the resulting intra-city redistribution of people, given by the difference between inflows and outflows, is such that there is a trend from core county to the external counties (viz. suburbs). When compared to the relative growth due to inter-city flows (Fig. 3F), which do not show any trend and that have negative values for the most distant counties, it becomes clear that intra-city flows play a major role in the population increase observed in outer regions of cities. Interestingly, large circle and square dots in Fig. 3E and F suggest that the loss of people due to inter-city netflows is more intense than the gain of people due to intra-city netflows in some external counties of the largest metro areas, thus explaining the population decline in some outer regions of New York and Chicago (as shown in Fig. 2B and C).The population growth due to intra-city flows is also depicted in Fig. 4. The concentration of flows below the diagonal captures the heterogeneity and the preferential destination of intra-city netflows. We observe that people are more likely to move to lower population density counties when moving from one place to another within the same city, as exemplified by 7 cities in panel A. Panel B summarizes this analysis for the 46 cities with more than 5 counties by showing the fraction ({{{{{{{mathcal{F}}}}}}}}) of intra-city netflows to lower density counties. We note that more than 93% of the cities have ({{{{{{{mathcal{F}}}}}}}} > 0.5) and that there is a positive correlation of ({{{{{{{mathcal{F}}}}}}}}) with the city population, and C shows the rank of cities according to the fraction of intra-city netflows to lower density counties.Fig. 4: People are moving to counties with lower population density.A The population density of the origin (ρo) and destination (ρd) counties of intra-city netflows for New York, Chicago, Dallas, Houston, Washington D.C., Philadelphia, Atlanta, reveal that the majority of the flows occur from high to low-density counties. The size of the symbols are proportional to the intensity of the netflow, and the black line corresponds to y = x. B The fraction of netflows to lower density counties ({{{{{{{mathcal{F}}}}}}}}) has a positive correlation with city population when we consider the 46 MSAs with more than 5 counties, suggesting that intra-city netflows to lower density counties are more frequent as the city size increases. We also show the Pearson correlation coefficient R and the p-value associated with the two-sided test of the null hypothesis of non-correlation. C The ranking of the cities according to ({{{{{{{mathcal{F}}}}}}}}).Full size imagePopulation density does not seem to play a major role in driving flows between counties of different cities. The fraction of inter-city netflows to lower density counties is about 57% when we consider all the 384 MSAs. The heterogeneity in the inter-city netflow pattern can be assessed by analyzing ({{{{{{{mathcal{F}}}}}}}}) versus the population of the destination city (Fig. 5A, B) and ({{{{{{{mathcal{F}}}}}}}}) versus the population of the origin city (Fig. 5C, D). The negative correlation of ({{{{{{{mathcal{F}}}}}}}}) with the population of the destination city in panel A indicates that inflows are more likely to come from lower density counties as the destination city size increases. The positive correlation of ({{{{{{{mathcal{F}}}}}}}}) with the population of the origin city in panel C reveals that outflows tend to be directed to lower density counties as the origin city size increases. The trends observed in panels A and C reveal that inter-city flows are more likely between counties with different population densities rather than between counties with similar population densities. Panels B and D show the rank order of cities according to a function of the destination city size and the origin city size, respectively.Fig. 5: Inter-city flow patterns depend on the population size of the origin and destination cities.Each point corresponds to a particular city. A Fraction ({{{{{{{mathcal{F}}}}}}}}) of netflows going to lower density counties versus the population of the destination city. Inflows to counties of large cities (with population greater than 106, dashed line) usually comes from counties with lower population densities. B Rank of cities according to the share of inflows from lower density counties. C Fraction ({{{{{{{mathcal{F}}}}}}}}) versus the population of the origin city. Outflows from counties of large cities usually go to cities with lower density counties. D The rank of cities according to the share of inter-city netflows to lower density counties is presented. The dots are colored according to the city population density (darker red means higher density). We also show the Pearson correlation coefficient R and the p-value associated with the two-sided test of the null hypothesis of non-correlation.Full size imageWe would expect that there might be preferential locations within a given city to which people move due to various factors such as lower costs of housing and employment opportunities. However, it seems that house prices have little to no effect on intra-city netflows (Supplementary Fig. 8). While the fraction of intra-city netflows to counties with less expensive houses is about 0.8 for cities like New York, Chicago and Washington, this fraction is about 0.2 for cities like Dallas, Houston and Philadelphia. The lack of a clear national pattern highlights the specificity of each city and the heterogeneity of the regional housing market in the U.S.36,37. On the other hand, the fraction of intra-city netflows to counties with lower unemployment rates is higher than 0.5 for about 2/3 of the cities (Supplementary Fig. 9), thus showing that people are more likely to move to counties with lower unemployment rates.Statistical structure of inter-city flowsIntra-city flows capture the internal redistribution of population, without altering the total city population. In this context, we focus on inter-city flows to investigate whether or not extreme flows play an important role in shaping the growth of counties as observed at the city level5. For cities, Verbavatz and Barthelemy5 introduce a stochastic equation to describe population growth, composed of two terms. The first term accounts for out-of-system growth, which includes natural growth and international migration, and the second term accounts for the growth due to domestic netflows. They find that total netflows adjusted by population size can be well approximated by a Lévy distribution, and this heavy-tailed distribution indicates that rare and extreme inter-city flows (viz. migratory shocks) dominate city population growth.Here, we find that, for counties, the distribution of total netflows adjusted by population size, which is represented by ζi and captures the intensity of inter-city migratory flows (see the section “Methods” for details), can be approximated by a Gaussian distribution (Fig. 6). The lack of a heavy tail in the empirical distribution of ζi suggests the absence of extreme flows at the county level, thus indicating that the growth of counties can be described by smoother migratory process than cities. Given that cities do experience migratory shocks5, our findings indicate that cities redistribute inflows among its different counties, leading to a spill-over effect that dampens flow shocks at the county level.Fig. 6: Extreme shocks are dissipated at the county level.The distribution of ζi, which is the sum of the netflows of a county i adjusted by its population, suggests that migratory events are exponentially bounded at the county level since ζi is well described by a Gaussian distribution. The distribution of ζi is computed here for all the counties with at least 50.000 inhabitants. We also show the result of the two-sided KS test under the null hypothesis that ζi follows a Gaussian distribution.Full size imageHeterogeneity of international inflowsThe highest share of international inflows is concentrated in large cities. About 40% of the international inflows are destined to the top 10 (~2.6%) largest metro areas of the U.S. New York is the first with 8.5% of international inflows, followed by Los Angeles and Miami with 5.4% and 5.0%, respectively. Indeed, international inflows Yk scale superlinearly with the population Sk of the metro area k (Fig. 7A), thus larger cities have more immigrants per capita than smaller cities.Fig. 7: International inflow scales superlinearly with city size.Panel (A) shows the number of international immigrants as a function of the city size S for the 384 U.S. metro areas. The performance of the model Y = Y0Sθ, in which θ = 1.19 (95% CI [1.13, 1.24]) and Y0 = 4.10−4 is a normalization constant, is assessed by the coefficient of determination R2. Note that the spread of empirical data around the model narrows as the size of the city increases. Panel (B) shows the rank of the metro areas and the residues, which captures the deviation from the null model thus highligthing cities receiving more/less than expected international inflows. Names of the cities are followed by two-letter state abbreviations.Full size imageInterestingly, this gain with scale is also observed in socioeconomic city metrics as crime, GDP, innovation and wealth creation due to the manifestation of nonlinear agglomeration phenomena38,39,40. Using Y = Y0Sθ as a null model, we can compute deviations from the average behavior by means of residuals given by (log ({Y}_{k}/{Y}_{0}{S}_{k}^{theta }))38. The rank of the residues (Fig. 7B) shows that college towns are among the top metro areas receiving more international inflows than expected, while large cities as Los Angeles, New York, Atlanta, and Chicago are among the metro areas receiving less international inflows than expected.The spatial distribution of international inflows within cities is shown in Supplementary Fig. 10. The highest share of inflows is concentrated at core counties, and the percentage of inflows decreases dramatically with the distance from the core county. This result suggests that inflow of international migrants is an important component of population growth, particularly at the core regions of large cities.Robustness of our findingsPatterns of population redistribution change from time to time in the U.S., and are affected by several factors. For instance, in the 1960s non-metropolitan counties lost about 3 million people due to outflows to metropolitan counties, while the reverse trend was observed in the 1970s when non-metropolitan counties experienced net inflows of about 2.6 million people41. Wardwell and Brown in41 indicate that three factors might be among the main reasons of such change, namely economic decentralization, preference for rural living, and modernization of rural life. The temporal influence of factors as socioeconomic conditions, transportation infrastructure, natural amenities, and land use and development on population growth in rural and suburban areas is explored in42. Changes in rural migration patterns are also studied in43, where age-specific rural migration patterns from 1950 to 1995 are analyzed. In44, the authors explore redistribution trends across U.S. counties from 1980 to 1995 split into three five year periods (1980–1985, 1985–1990, 1990–1995), and45 analyzes changes in age-specific nationwide migration patterns from 1950 to 2010.The spatial structure of migration patterns may indeed change from time to time; our results correspond to the current intra- and inter-city redistribution trends, based on the most recent ACS migration flow files. We present a thorough empirical and statistical analysis of domestic migration flows among U.S. cities ans counties. Our study also introduces a framework that can be used for analyzing and comparing internal redistribution of people across different time periods. Indeed, we extended our analysis for two other time periods, 2005–2009 and 2010–2014. With respect to the spatial distribution of intra- and inter-city flows, similar trends are observed in both periods (Supplementary Figs. 12, 13), namely inter-city flows are responsible for the highest share of inflows to core counties, and intra-city flows are responsible for the highest share of inflows to external counties. We also explored the role of population density in driving netflows between counties within the same metro area in 2005–2009 and 2010–2014. The results (Supplementary Figs. 14 and 15) indicate that 95.7% of cities were dominated by intra-city moves to lower density counties in 2005–2009, and this percentage dropped to 76.1% in 2010–2014. Our findings indicate that the trends we report here are taking place since 2005 but with different intensities.The robustness of our findings is checked with additional migration data from the Internal Revenue Service (IRS), which reports the year-to-year address changes on individual tax returns filled with the IRS46. The results obtained with the analysis of IRS datasets from periods 2015–2016, 2016–2017, 2017–2018, 2018–2019 (Supplementary Figs. 16, 17, 18, 19), reveal similar trends to those we found using ACS data. Particularly, we observe that, for all periods considered, the correlation between intra-city netflow/S and distance to core county is stronger than we found with ACS data, thus highlighting the role of intra-city flows in driving population to external regions of cities. The main difference between both datasets is in the percentage of intra- and inter-city inflows and outflows: while ACS data indicates that both flows have approximately the same contribution to the total flows, the IRS data indicates that, besides the core county, intra-city flows are responsible for about 80% of inflows and outflows of metro areas. More

Pörtner, H. O. et al. IPCC Special Report on the Ocean and Cryosphere in a Changing Cimate (2019).Genevier, L. G. C., Jamil, T., Raitsos, D. E., Krokos, G. & Hoteit, I. Marine heatwaves reveal coral reef zones susceptible to bleaching in the Red Sea. Glob. Chang. Biol. 25, 2338–2351 (2019).ADS 
PubMed 
Article 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science (80-.) 359, 80–83 (2018).ADS 
CAS 
Article 

Google Scholar 
Morris, L. A., Voolstra, C. R., Quigley, K. M., Bourne, D. G. & Bay, L. K. Nutrient availability and metabolism affect the stability of coral–symbiodiniaceae symbioses. Trends Microbiol. 27, 678–689 (2019).CAS 
PubMed 
Article 

Google Scholar 
Suggett, D. J. & Smith, D. J. Coral bleaching patterns are the outcome of complex biological and environmental networking. Glob. Chang. Biol. 26, 68–79 (2020).ADS 
PubMed 
Article 

Google Scholar 
Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast. Shelf Sci. 80, 435–471 (2008).ADS 
Article 

Google Scholar 
Brown, B. E., Dunne, R. P., Scoffin, T. P. & Le Tissier, M. D. A. Solar damage in intertidal corals. Mar. Ecol. Prog. Ser. 105, 219–230 (1994).ADS 
Article 

Google Scholar 
Suggett, D. J. & Smith, D. J. Interpreting the sign of coral bleaching as friend vs. foe. Glob. Chang. Biol. 17, 45–55 (2011).ADS 
Article 

Google Scholar 
Maynard, J. A., Anthony, K. R. N., Marshall, P. A. & Masiri, I. Major bleaching events can lead to increased thermal tolerance in corals. Mar. Biol. 155, 173–182 (2008).Article 

Google Scholar 
Weis, V. M. The susceptibility and resilience of corals to thermal stress: adaptation, acclimatization or both?: NEWS and VIEWS. Mol. Ecol. 19, 1515–1517 (2010).PubMed 
Article 

Google Scholar 
Meyer, E., Aglyamova, G. V. & Matz, M. V. Profiling gene expression responses of coral larvae (Acropora millepora) to elevated temperature and settlement inducers using a novel RNA-Seq procedure. Mol. Ecol. 20, 3599–3616 (2011).CAS 
PubMed 

Google Scholar 
Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science (80-.) 348, 1460–1462 (2015).ADS 
CAS 
Article 

Google Scholar 
Grottoli, A. G. et al. Increasing comparability among coral bleaching experiments. Ecol. Appl. 31, 1–17 (2021).Article 

Google Scholar 
Evensen, N. et al. Empirically derived thermal thresholds of four coral species along the Red Sea using a portable and standardized experimental approach. Coral Reefs 41, 239–252 (2022).Article 

Google Scholar 
Song, M. et al. The impact of acute thermal stress on the metabolome of the black rockfish (Sebastes schlegelii). PLoS ONE 14, 1–23 (2019).Article 

Google Scholar 
Kim, K. S. et al. Physiological responses to short-term thermal stress in mayfly (Neocloeon triangulifer) larvae in relation to upper thermal limits. J. Exp. Biol. 220, 2598–2605 (2017).PubMed 
Article 

Google Scholar 
Juárez, O. E. et al. Transcriptomic and metabolic response to chronic and acute thermal exposure of juvenile geoduck clams Panopea globosa. Mar. Genomics 42, 1–13 (2018).PubMed 
Article 

Google Scholar 
Pallarés, S., Arribas, P., Céspedes, V., Millán, A. & Velasco, J. Lethal and sublethal behavioural responses of saline water beetles to acute heat and osmotic stress. Ecol. Entomol. 37, 508–520 (2012).Article 

Google Scholar 
Qin, G. et al. Temperature-induced physiological stress and reproductive characteristics of the migratory seahorse Hippocampus erectus during a thermal stress simulation. Biol. Open 7, 1–7 (2018).CAS 

Google Scholar 
Zanuzzo, F. S., Bailey, J. A., Garber, A. F. & Gamperl, A. K. Comparative Biochemistry and Physiology, Part A The acute and incremental thermal tolerance of Atlantic cod (Gadus morhua) families under normoxia and mild hypoxia ☆. Comp. Biochem. Physiol. Part A 233, 30–38 (2019).CAS 
Article 

Google Scholar 
Cunning, R. et al. Census of heat tolerance among Florida ’ s threatened staghorn corals finds resilient individuals throughout existing nursery populations. (2021).Evensen, N. R., Fine, M., Perna, G., Voolstra, C. R. & Barshis, D. J. Remarkably high and consistent tolerance of a Red Sea coral to acute and chronic thermal stress exposures. Limnol. Oceanogr. https://doi.org/10.1002/lno.11715 (2021).Article 

Google Scholar 
Morikawa, M. K. & Palumbi, S. R. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc. Natl. Acad. Sci. U. S. A. 116, 10586–10591 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rose, N. H., Bay, R. A., Morikawa, M. K. & Palumbi, S. R. Polygenic evolution drives species divergence and climate adaptation in corals. Evolution (N. Y.) 72, 82–94 (2018).
Google Scholar 
Thomas, L. et al. Mechanisms of thermal tolerance in reef-building corals across a fine-grained environmental mosaic: lessons from Ofu, American Samoa. Front. Mar. Sci. 4, 1–14 (2018).CAS 
Article 

Google Scholar 
Voolstra, C. R. et al. Standardized short-term acute heat stress assays resolve historical differences in coral thermotolerance across microhabitat reef sites. Glob. Chang. Biol. 26, 4328–4343 (2020).ADS 
PubMed 
Article 

Google Scholar 
Klepac, C. N. & Barshis, D. J. High-resolution in situ thermal metrics coupled with acute heat stress experiments reveal differential coral bleaching susceptibility. Coral Reefs https://doi.org/10.1007/s00338-022-02276-1 (2022).Article 

Google Scholar 
Gardner, S. G. et al. A multi-trait systems approach reveals a response cascade to bleaching in corals. BMC Biol. 15, 117 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Madin, J. S. et al. A trait-based approach to advance coral reef science. Trends Ecol. Evol. 31, 419–428 (2016).PubMed 
Article 

Google Scholar 
Suggett, D. J. et al. Toward bio-optical phenotyping of reef-forming corals using light-induced fluorescence transient-fast repetition rate fluorometry. Limnol. Oceanogr. Methods https://doi.org/10.1002/lom3.10479 (2022).Article 

Google Scholar 
Krueger, T. et al. Differential coral bleaching-contrasting the activity and response of enzymatic antioxidants in symbiotic partners under thermal stress. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 190, 15–25 (2015).CAS 
Article 

Google Scholar 
Leggat, W. et al. Differential responses of the coral host and their algal symbiont to thermal stress. PLoS ONE 6, e26687 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nitschke, M. R. et al. Utility of photochemical traits as diagnostics of thermal tolerance amongst great barrier reef corals. Front. Mar. Sci. 5, 1–18 (2018).Article 

Google Scholar 
Warner, M. E., Fittt, W. K. & Schmidt, G. W. Damage to photosystem II in symbiotic dinoflagellates: a determinant of coral bleaching. Proc. Natl. Acad. Sci. 96, 8007–8012 (1999).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fitt, W. K., Brown, B. E., Warner, M. E. & Dunne, R. P. Coral bleaching: Interpretation of thermal tolerance limits and thermal thresholds in tropical corals. Coral Reefs 20, 51–65 (2001).Article 

Google Scholar 
Tolosa, I., Treignier, C., Grover, R. & Ferrier-Pagès, C. Impact of feeding and short-term temperature stress on the content and isotopic signature of fatty acids, sterols, and alcohols in the scleractinian coral Turbinaria reniformis. Coral Reefs 30, 763–774 (2011).ADS 
Article 

Google Scholar 
Grottoli, A. G. et al. Coral physiology and microbiome dynamics under combined warming and ocean acidification. PLoS ONE 13, e0191156 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Chow, M. H., Tsang, R. H. L., Lam, E. K. Y. & Ang, P. Quantifying the degree of coral bleaching using digital photographic technique. J. Exp. Mar. Bio. Ecol. 479, 60–68 (2016).Article 

Google Scholar 
Nielsen, J. J. V. et al. Physiological effects of heat and cold exposure in the common reef coral Acropora millepora. Coral Reefs 39, 259–269 (2020).Article 

Google Scholar 
McLachlan, R. H., Price, J. T., Solomon, S. L. & Grottoli, A. G. Thirty years of coral heat-stress experiments: a review of methods. Coral Reefs 39, 885–902 (2020).Article 

Google Scholar 
Edmunds, P. J. & Burgess, S. C. Correction: Size-dependent physiological responses of the branching coral Pocillopora verrucosa to elevated temperature and PCO2 (J. Exp. Biol. (2016) 219 (3896-3906) doi: 10.1242/jeb.146381). J. Exp. Biol. 221, 3896–3906 (2018).Article 

Google Scholar 
Madin, J. S., Baird, A. H., Dornelas, M. & Connolly, S. R. Mechanical vulnerability explains size-dependent mortality of reef corals. Ecol. Lett. 17, 1008–1015 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Pausch, R. E., Williams, D. E. & Miller, M. W. Impacts of fragment genotype, habitat, and size on outplanted elkhorn coral success under thermal stress. Mar. Ecol. Prog. Ser. 592, 109–117 (2018).ADS 
Article 

Google Scholar 
Shenkar, N., Fine, M. & Loya, Y. Size matters: bleaching dynamics of the coral Oculina patagonica. Mar. Ecol. Prog. Ser. 294, 181–188 (2005).ADS 
Article 

Google Scholar 
Middlebrook, R., Anthony, K. R. N., Hoegh-Guldberg, O. & Dove, S. Heating rate and symbiont productivity are key factors determining thermal stress in the reef-building coral Acropora formosa. J. Exp. Biol. 213, 1026–1034 (2010).CAS 
PubMed 
Article 

Google Scholar 
Hoey, A. et al. Recent advances in understanding the effects of climate change on coral reefs. Diversity 8, 12 (2016).Article 

Google Scholar 
Marhoefer, S. R. et al. Signatures of adaptation and acclimatization to reef flat and slope habitats in the coral pocillopora damicornis. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.704709 (2021).Article 

Google Scholar 
Cornwell, B. et al. Widespread variation in heat tolerance and symbiont load are associated with growth tradeoffs in the coral acropora hyacinthus in palau. Elife 10, 1–15 (2021).Article 

Google Scholar 
McClanahan, T. R. et al. Large geographic variability in the resistance of corals to thermal stress. Glob. Ecol. Biogeogr. 29, 2229–2247 (2020).Article 

Google Scholar 
Magozzi, S. & Calosi, P. Integrating metabolic performance, thermal tolerance, and plasticity enables for more accurate predictions on species vulnerability to acute and chronic effects of global warming. Glob. Chang. Biol. 21, 181–194 (2015).ADS 
PubMed 
Article 

Google Scholar 
Drury, C., Manzello, D. & Lirman, D. Genotype and local environment dynamically influence growth, disturbance response and survivorship in the threatened coral, Acropora cervicornis. PLoS ONE 12, 1–21 (2017).Article 

Google Scholar 
McLachlan, R. H., Dobson, K. L., Schmeltzer, E. R., Thurber, R. V. & Grottoli, A. G. A review of coral bleaching specimen collection, preservation, and laboratory processing methods. PeerJ 9, 1–21 (2021).Article 

Google Scholar 
Okubo, N., Motokawa, T. & Omori, M. When fragmented coral spawn? Effect of size and timing on survivorship and fecundity of fragmentation in Acropora formosa. Mar. Biol. 151, 353–363 (2007).Article 

Google Scholar 
Bruno, J. F. Fragmentation in Madracis mirabilis (Duchassaing and Michelotti): How common is size-specific fragment survivorship in corals?. J. Exp. Mar. Bio. Ecol. 230, 169–181 (1998).Article 

Google Scholar 
Suggett, D. J. et al. Optimizing return-on-effort for coral nursery and outplanting practices to aid restoration of the Great Barrier Reef. Restor. Ecol. 27, 683–693 (2019).Article 

Google Scholar 
Howlett, L., Camp, E. F., Edmondson, J., Henderson, N. & Suggett, D. J. Coral growth, survivorship and return-on-effort within nurseries at high-value sites on the Great Barrier Reef. PLoS ONE 16, 1–15 (2021).Article 

Google Scholar 
Veal, C. J., Carmi, M., Fine, M. & Hoegh-Guldberg, O. Increasing the accuracy of surface area estimation using single wax dipping of coral fragments. Coral Reefs 29, 893–897 (2010).ADS 
Article 

Google Scholar 
Voolstra, C. R. et al. Contrasting heat stress response patterns of coral holobionts across the Red Sea suggest distinct mechanisms of thermal tolerance. Mol. Ecol. 30, 4466–4480 (2021).CAS 
PubMed 
Article 

Google Scholar 
Dove, S. et al. Response of holosymbiont pigments from the scleractinian coral Montipora monasteriata to short-term heat stress. Limnol. Oceanogr. 51, 1149–1158 (2006).ADS 
Article 

Google Scholar 
Traylor-Knowles, N., Rose, N. H., Sheets, E. A. & Palumb, S. Early tracriptional responses during heat stress in the coral Acropora hyacinthus. Biol. Bull. 232, 91–100 (2017).CAS 
PubMed 
Article 

Google Scholar 
Schuback, N. et al. Single-turnover variable chlorophyll fluorescence as a tool for assessing phytoplankton photosynthesis and primary productivity: opportunities, caveats and recommendations. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.690607 (2021).Article 

Google Scholar 
Macadam, A., Nowell, C. J. & Quigley, K. Machine learning for the fast and accurate assessment of fitness in coral early life history. Remote Sens. 13, 1–17 (2021).Article 

Google Scholar 
Teague, J., Willans, J., Allen, M. J., Scott, T. B. & Day, J. C. C. Applied marine hyperspectral imaging; coral bleaching from a spectral viewpoint. Spectrosc. Eur. 31, 13–17 (2019).CAS 

Google Scholar 
Davies, S. W., Ries, J. B., Marchetti, A. & Castillo, K. D. Symbiodinium functional diversity in the Coral Siderastrea siderea Is influenced by thermal stress and reef environment, but not ocean acidification. Front. Mar. Sci. 5, 1–14 (2018).Article 

Google Scholar 
Tang, J. et al. Increased ammonium assimilation activity in the scleractinian coral pocillopora damicornis but not its symbiont after acute heat stress. Front. Mar. Sci. 7, 1–10 (2020).ADS 
Article 

Google Scholar 
Sweet, M. et al. Species-specific variations in the metabolomic profiles of Acropora hyacinthus and Acropora millepora mask acute temperature stress effects in adult coral colonies. Front. Mar. Sci. 8, 1–15 (2021).Article 

Google Scholar 
Newton, J. R., Smith-Keune, C. & Jerry, D. R. Thermal tolerance varies in tropical and sub-tropical populations of barramundi (Lates calcarifer) consistent with local adaptation. Aquaculture 308, S128–S132 (2010).Article 

Google Scholar 
Waltham, N. J. & Sheaves, M. Acute thermal tolerance of tropical estuarine fish occupying a man-made tidal lake, and increased exposure risk with climate change. Estuar. Coast. Shelf Sci. 196, 173–181 (2017).ADS 
Article 

Google Scholar 
Iwabuchi, B. L. & Gosselin, L. A. Implications of acute temperature and salinity tolerance thresholds for the persistence of intertidal invertebrate populations experiencing climate change. Ecol. Evol. 10, 7739–7754 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Cox, J., Schubert, A. M., Travisano, M. & Putonti, C. Adaptive evolution and inherent tolerance to extreme thermal environments. BMC Evol. Biol. https://doi.org/10.1186/1471-2148-10-75 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Quigley, K. M., Bay, L. K. & Willis, B. L. Temperature and water quality-related patterns in sediment-associated Symbiodinium communities impact symbiont uptake and fitness of juveniles in the genus Acropora. Front. Mar. Sci. 4, 1–17 (2017).Article 

Google Scholar 
Voolstra, C. R. et al. Extending the natural adaptive capacity of coral holobionts. Nat. Rev. Earth Environ. https://doi.org/10.1038/s43017-021-00214-3 (2021).Article 

Google Scholar 
Cocciardi, J. M. et al. Adjustable temperature array for characterizing ecological and evolutionary effects on thermal physiology. Methods Ecol. Evol. 2019, 1339–1346 (2019).Article 

Google Scholar 
Smith, G. & Spillman, C. New high-resolution sea surface temperature forecasts for coral reef management on the Great Barrier Reef. Coral Reefs 38, 1039–1056 (2019).ADS 
Article 

Google Scholar 
Bainbridge, S. J. Temperature and light patterns at four reefs along the Great Barrier Reef during the 2015–2016 austral summer: understanding patterns of observed coral bleaching. J. Oper. Oceanogr. 10, 16–29 (2017).
Google Scholar 
Siebeck, U. E., Marshall, N. J., Klüter, A. & Hoegh-Guldberg, O. Monitoring coral bleaching using a colour reference card. Coral Reefs 25, 453–460 (2006).ADS 
Article 

Google Scholar 
Palumbi, S. R., Barshis, D. J., Traylor-Knowles, N. & Bay, R. A. Mechanisms of reef coral resistance to future climate change. Science (80-.) 344, 895–899 (2014).ADS 
CAS 
Article 

Google Scholar 
Saxby, T., Dennison, W. C. & Hoegh-Guldberg, O. Photosynthetic responses of the coral Montipora digitata to cold temperature stress. Mar. Ecol. Prog. Ser. 248, 85–97 (2003).ADS 
Article 

Google Scholar 
Deschaseaux, E. S. M., Deseo, M. A., Shepherd, K. M., Jones, G. B. & Harrison, P. L. Air blasting as the optimal approach for the extraction of antioxidants in coral tissue. J. Exp. Mar. Bio. Ecol. 448, 146–148 (2013).CAS 
Article 

Google Scholar 
Holmes, G., Ortiz, J., Kaniewska, P. & Johnstone, R. Using three-dimensional surface area to compare the growth of two Pocilloporid coral species. Mar. Biol. 155, 421–427 (2008).Article 

Google Scholar 
Naumann, M. S., Niggl, W., Laforsch, C., Glaser, C. & Wild, C. Coral surface area quantification-evaluation of established techniques by comparison with computer tomography. Coral Reefs 28, 109–117 (2009).ADS 
Article 

Google Scholar 
Ritchie, R. J. Consistent sets of spectrophotometric chlorophyll equations for acetone, methanol and ethanol solvents. Photosynth. Res. 89, 27–41 (2006).CAS 
PubMed 
Article 

Google Scholar 
Licthenthaler, H. K. Chlorophylls and carotenoids – pigments of photosynthetic biomembranes. Methods Enzymol. 148, 350–382 (1987).Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing. (2020).Hartig, F. & Lohse, L. Package ‘DHARMa’ residual diangonstics for hierarchical (multi-level/mixed) regression models (2021).Brooks, M. E. et al. glmmTMB balances speed and flexibility among packaages for zero-inflated generalized linear mixed modelling. R Journal 9, 378–400 (2017).Article 

Google Scholar 
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 2018, 1–32 (2018).
Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Oksanen, J. et al. Vegan (2020).Sarkar, D. Lattice: Multivariate Data Visualization with R (Springer, 2008).MATH 
Book 

Google Scholar  More