Philippa Kaur

Darwin, C. R. On the Origin of Species, or the Preservation of Favoured Races in the Struggle for Life (John Murray, 1859).Wallace, A. R. Natural Selection and Tropical Nature: Essays on Descriptive and Theoretical Biology 2nd edn (Macmillan, 1895).Darwin, C. R. A Naturalist’s Voyage Round the World (John Murray, 1913).Wallace, A. R. Colour in nature. Nature 19, 580–581 (1879).
Google Scholar 
Dalrymple, R. L. et al. Abiotic and biotic predictors of macroecological patterns in bird and butterfly coloration. Ecol. Monogr. 88, 204–224 (2018).
Google Scholar 
Adams, J. M., Kang, C. & June-Wells, M. Are tropical butterflies more colorful? Ecol. Res. 29, 685–691 (2014).
Google Scholar 
Bailey, S. F. Latitudinal gradients in colors and patterns of passerine birds. Condor 80, 372–381 (1978).
Google Scholar 
Wilson, M. F. & Von Neaumann, R. A. Why are neotropical birds more colourful than North American birds? Avicultural Mag. 78, 141–147 (1972).
Google Scholar 
Dalrymple, R. L. et al. Birds, butterflies and flowers in the tropics are not more colourful than those at higher latitudes. Glob. Ecol. Biogeogr. 24, 1424–1432 (2015).
Google Scholar 
Friedman, N. R. & Remeš, V. Ecogeographical gradients in plumage coloration among Australasian songbird clades. Glob. Ecol. Biogeogr. 26, 261–274 (2017).
Google Scholar 
Dale, J., Dey, C. J., Delhey, K., Kempenaers, B. & Valcu, M. The effects of life history and sexual selection on male and female plumage colouration. Nature 527, 367–370 (2015).CAS 

Google Scholar 
Dunn, P. O., Armenta, J. K. & Whittingham, L. A. Natural and sexual selection act on different axes of variation in avian plumage color. Sci. Adv. 1, e1400155 (2015).PubMed 
PubMed Central 

Google Scholar 
Stoddard, M. C. & Prum, R. O. How colorful are birds? Evolution of the avian plumage color gamut. Behav. Ecol. 22, 1042–1052 (2011).
Google Scholar 
Renoult, J. P., Kelber, A. & Schaefer, H. M. Colour spaces in ecology and evolutionary biology. Biol. Rev. 92, 292–315 (2017).
Google Scholar 
Stoddard, M. C. & Prum, R. O. Evolution of avian plumage color in a tetrahedral color space: a phylogenetic analysis of New World buntings. Am. Nat. 171, 755–776 (2008).
Google Scholar 
Delhey, K. The colour of an avifauna: a quantitative analysis of the colour of Australian birds. Sci. Rep. 5, 18514 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).
Google Scholar 
Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 

Google Scholar 
Lynch, M. Methods for the analysis of comparative data in evolutionary biology. Evolution 45, 1065–1080 (1991).PubMed 
PubMed Central 

Google Scholar 
Delhey, K. A review of Gloger’s rule, an ecogeographical rule of colour: definitions, interpretations and evidence. Biol. Rev. Camb. Phil. Soc. 94, 1294–1316 (2019).
Google Scholar 
Marchetti, K. Dark habitats and bright birds illustrate the role of the environment in species divergence. Nature 362, 149–152 (1993).
Google Scholar 
Endler, J. A. The color of light in forests and its implications. Ecol. Monogr. 63, 1–27 (1993).
Google Scholar 
Schemske, D. W. in Speciation and Patterns of Diversity Vol. 12 (eds Butlin, R. et al.) 219–239 (Cambridge Univ. Press, 2009).Schemske, D. W., Mittelbach, G. G., Cornell, H. V., Sobel, J. M. & Roy, K. Is there a latitudinal gradient in the importance of biotic interactions? Annu. Rev. Ecol. Evol. Syst. 40, 245–269 (2009).
Google Scholar 
MacArthur, R. H. Patterns of communities in the tropics. Biol. J. Linn. Soc. 1, 19–30 (1969).
Google Scholar 
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).CAS 

Google Scholar 
Cooney, C. R. et al. Sexual selection predicts the rate and direction of colour divergence in a large avian radiation. Nat. Commun. 10, 1773 (2019).PubMed 
PubMed Central 

Google Scholar 
Cooney, C. R., MacGregor, H. E. A., Seddon, N. & Tobias, J. A. Multi-modal signal evolution in birds: re-assessing a standard proxy for sexual selection. Proc. R. Soc. B 285, 20181557 (2018).PubMed 
PubMed Central 

Google Scholar 
van der Bijl, W. et al. Butterfly dichromatism primarily evolved via Darwin’s, not Wallace’s, model. Evol. Lett. 4, 545–555 (2020).PubMed 
PubMed Central 

Google Scholar 
Darwin, C. R. The Descent of Man, and Selection in Relation to Sex (John Murray, 1871).Tobias, J. A., Montgomerie, R. & Lyon, B. E. The evolution of female ornaments and weaponry: social selection, sexual selection and ecological competition. Phil. Trans. R. Soc. B 367, 2274–2293 (2012).PubMed 
PubMed Central 

Google Scholar 
Galván, I., Negro, J. J., Rodríguez, A. & Carrascal, L. M. On showy dwarfs and sober giants: body size as a constraint for the evolution of bird plumage colouration. Acta Ornithol. 48, 65–80 (2013).
Google Scholar 
Kiltie, R. A. Scaling of visual acuity with body size in mammals and birds. Funct. Ecol. 14, 226–234 (2000).
Google Scholar 
Zahavi, A. & Zahavi, A. The Handicap Principle (Oxford Univ. Press, 1997).Badyaev, A. V. & Hill, G. E. Avian sexual dichromatism in relation to phylogeny and ecology. Annu. Rev. Ecol. Evol. Syst. 34, 27–49 (2003).
Google Scholar 
Simpson, R. K., Johnson, M. A. & Murphy, T. G. Migration and the evolution of sexual dichromatism: evolutionary loss of female coloration with migration among wood-warblers. Proc. R. Soc. B 282, 20150375 (2015).PubMed 
PubMed Central 

Google Scholar 
Helferich, G. Humboldt’s Cosmos (Tantor eBooks, 2011).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
He, Y. et al. Segmenting biological specimens from photos to understand the evolution of UV plumage in passerine birds. Preprint at bioRxiv https://doi.org/10.1101/2021.07.22.453339 (2021).Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F. & Adam, H. Encoder–decoder with atrous separable convolution for semantic image segmentation. Preprint at arXiv https://doi.org/10.48550/arXiv.1802.02611 (2018).Hussein, B. R., Malik, O. A., Ong, W.-H. & Slik, J. W. F. in Computational Science and Technology Lecture Notes in Electrical Engineering (eds Alfred, R. et al.) 321–330 (Springer Singapore, 2020).Troscianko, J. & Stevens, M. Image calibration and analysis toolbox—a free software suite for objectively measuring reflectance, colour and pattern. Methods Ecol. Evol. 6, 1320–1331 (2015).PubMed 
PubMed Central 

Google Scholar 
Hijmans, R. J. raster: Geographic Data Analysis and Modeling. R package version 3.5-15 https://CRAN.R-project.org/package=raster (2022).Maia, R., Gruson, H., Endler, J. A., White, T. E. & O’Hara, R. B. pavo 2: new tools for the spectral and spatial analysis of colour in R. Methods Ecol. Evol. 10, 1097–1107 (2019).
Google Scholar 
Stoddard, M. C. et al. Wild hummingbirds discriminate nonspectral colors. Proc. Natl Acad. Sci. USA 117, 15112–15122 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Gomez, D. & Théry, M. Simultaneous crypsis and conspicuousness in color patterns: comparative analysis of a neotropical rainforest bird community. Am. Nat. 169, S42–S61 (2007).
Google Scholar 
Blonder, B. Do hypervolumes have holes? Am. Nat. 187, E93–E105 (2016).
Google Scholar 
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 

Google Scholar 
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).
Google Scholar 
Beckmann, M. et al. glUV: a global UV-B radiation data set for macroecological studies. Methods Ecol. Evol. 5, 372–383 (2014).
Google Scholar 
Running, S. W. et al. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54, 547–560 (2004).
Google Scholar 
Tobias, J. A. & Pigot, A. L. Integrating behaviour and ecology into global biodiversity conservation strategies. Phil. Trans. R. Soc. B 374, 20190012 (2019).PubMed 
PubMed Central 

Google Scholar 
Dunn, P. O., Whittingham, L. A. & Pitcher, T. E. Mating systems, sperm competition, and the evolution of sexual dimorphism in birds. Evolution 55, 161–175 (2001).CAS 

Google Scholar 
Bivand, R. S. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).
Google Scholar 
Hawkins, B. A. et al. Structural bias in aggregated species-level variables driven by repeated species co-occurrences: a pervasive problem in community and assemblage data. J. Biogeogr. 44, 1199–1211 (2017).
Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalised linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar 
Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. B 281, 20140298 (2014).PubMed 
PubMed Central 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021); https://www.R-project.org/ More

Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science https://doi.org/10.1126/science.281.5374.237 (1998).Guidi, L. et al. Plankton networks driving carbon export in the oligotrophic ocean. Nature https://doi.org/10.1038/nature16942 (2016).Henson, S. A., Sanders, R. & Madsen, E. Global patterns in efficiency of particulate organic carbon export and transfer to the deep ocean. Glob. Biogeochem. Cycles https://doi.org/10.1029/2011GB004099 (2012).Azam, F. et al. The ecological role of water-column microbes in the sea. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps010257 (1983).Saab, M. A. Day-to-day variation in phytoplankton assemblages during spring blooming in a fixed station along the Lebanese coastline. J. Plankton Res. https://doi.org/10.1093/plankt/14.8.1099 (1992).Djurhuus, A. et al. Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat. Commun. https://doi.org/10.1038/s41467-019-14105-1 (2020).Kavanaugh, M. T. et al. Seascapes as a new vernacular for pelagic ocean monitoring, management and conservation. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsw086 (2016).Longhurst, A. R. Ecological Geography of the Sea (Elsevier, 2007).Fay, A. R. & McKinley, G. A. Global open-ocean biomes: mean and temporal variability. Earth Syst. Sci. Data https://doi.org/10.5194/essd-6-273-2014 (2014).Reygondeau, G. et al. Dynamic biogeochemical provinces in the global ocean. Glob. Biogeochem. Cycles https://doi.org/10.1002/gbc.20089 (2013).Richter, D. J. et al. Genomic evidence for global ocean plankton biogeography shaped by large-scale current systems. Preprint at bioRxiv https://doi.org/10.1101/867739 (2020).Dutkiewicz, S. et al. Dimensions of marine phytoplankton diversity. Biogeosciences https://doi.org/10.5194/bg-17-609-2020 (2020).Hellweger, F. L., Van Sebille, E. & Fredrick, N. D. Biogeographic patterns in ocean microbes emerge in a neutral agent-based model. Science https://doi.org/10.1126/science.1254421 (2014).Laso-Jadart, R. et al. Investigating population-scale allelic differential expression in wild populations of Oithona similis (Cyclopoida, Claus, 1866). Ecol. Evol. https://doi.org/10.1002/ece3.6588 (2020).Delmont, T. O. et al. Single-amino acid variants reveal evolutionary processes that shape the biogeography of a global SAR11 subclade. eLife https://doi.org/10.7554/eLife.46497 (2019).Carradec, Q. et al. A global ocean atlas of eukaryotic genes. Nat. Commun. https://doi.org/10.1038/s41467-017-02342-1 (2018).Salazar, G. et al. Gene expression changes and community turnover differentially shape the global ocean metatranscriptome. Cell https://doi.org/10.1016/j.cell.2019.10.014 (2019).Alberti, A. et al. Viral to metazoan marine plankton nucleotide sequences from the Tara Oceans expedition. Sci. Data https://doi.org/10.1038/sdata.2017.93 (2017).Pesant, S. et al. Open science resources for the discovery and analysis of Tara Oceans data. Sci. Data https://doi.org/10.1038/sdata.2015.23 (2015).Karsenti, E. et al. A holistic approach to marine eco-systems biology. PLoS Biol. https://doi.org/10.1371/journal.pbio.1001177 (2011).Duarte, C. M. Seafaring in the 21st century: the Malaspina 2010 circumnavigation expedition. Limnol. Oceanogr. Bull. https://doi.org/10.1002/lob.10008 (2015).Barton, A. D., Irwin, A. J., Finkel, Z. V. & Stock, C. A. Anthropogenic climate change drives shift and shuffle in North Atlantic phytoplankton communities. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1519080113 (2016).Benedetti, F., Guilhaumon, F., Adloff, F. & Ayata, S. D. Investigating uncertainties in zooplankton composition shifts under climate change scenarios in the Mediterranean Sea. Ecography https://doi.org/10.1111/ecog.02434 (2018).Beaugrand, G. et al. Prediction of unprecedented biological shifts in the global ocean. Nat. Clim. Change 9, 237–243 (2019).Article 

Google Scholar 
Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science https://doi.org/10.1126/science.1239352 (2013).Bopp, L. et al. Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences https://doi.org/10.5194/bg-10-6225-2013 (2013).Thomas, M. K., Kremer, C. T., Klausmeier, C. A. & Litchman, E. A global pattern of thermal adaptation in marine phytoplankton. Science https://doi.org/10.1126/science.1224836 (2012).Ibarbalz, F. M. et al. Global trends in marine plankton diversity across kingdoms of life. Cell https://doi.org/10.1016/j.cell.2019.10.008 (2019).Busseni, G. et al. Large scale patterns of marine diatom richness: drivers and trends in a changing ocean. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13161 (2020).Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

Google Scholar 
Delmont, T. O. et al. Functional repertoire convergence of distantly related eukaryotic plankton lineages revealed by genome-resolved metagenomics. Preprint at bioRxiv https://doi.org/10.1101/2020.10.15.341214 (2020).Delmont, T. O. et al. Heterotrophic bacterial diazotrophs are more abundant than their cyanobacterial counterparts in metagenomes covering most of the sunlit ocean. ISME J. https://doi.org/10.1038/s41396-021-01135-1 (2021).Boyer, et al. World Ocean Database 2013, NOAA Atlas NESDIS 72 (National Oceanic and Atmospheric Administration, 2013); https://doi.org/10.7289/V5NZ85MTSunagawa, S. et al. Tara Oceans: towards global ocean ecosystems biology. Nat. Rev. Microbiol. https://doi.org/10.1038/s41579-020-0364-5 (2020).Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0336-3 (2019).van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic Change https://doi.org/10.1007/s10584-011-0148-z (2011).Polovina, J. J., Dunne, J. P., Woodworth, P. A. & Howell, E. A. Projected expansion of the subtropical biome and contraction of the temperate and equatorial upwelling biomes in the North Pacific under global warming. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsq198 (2011).Flombaum, P., Wang, W. L., Primeau, F. W. & Martiny, A. C. Global picophytoplankton niche partitioning predicts overall positive response to ocean warming. Nat. Geosci. https://doi.org/10.1038/s41561-019-0524-2 (2020).Richardson, A. J. In hot water: zooplankton and climate change. ICES J. Mar. Sci. 65, 279–295 (2008).Article 

Google Scholar 
Wrightson, L. & Tagliabue, A. Quantifying the impact of climate change on marine diazotrophy: insights from Earth system models. Front. Mar. Sci. 7, 635 (2020).Article 

Google Scholar 
Zehr, J. P. & Capone, D. G. Changing perspectives in marine nitrogen fixation. Science 368, eaay9514 (2020).CAS 
Article 

Google Scholar 
Luo, Y.-W. et al. Database of diazotrophs in global ocean: abundance, biomass and nitrogen fixation rates. Earth Syst. Sci. Data 4, 47–73 (2012).Article 

Google Scholar 
Eppley, R. W. & Peterson, B. J. Particulate organic matter flux and planktonic new production in the deep ocean. Nature 282, 677–680 (1979).Article 

Google Scholar 
Laws, E. A., Falkowski, P. G., Smith, W. O., Ducklow, H. & McCarthy, J. J. Temperature effects on export production in the open ocean. Glob. Biogeochem. Cycles 14, 1231–1246 (2000).CAS 
Article 

Google Scholar 
Agrawal, R. & Srikant, R. in Proceedings of the 20th International Conference on Very Large Data Bases (eds Bocca, J. B. et al.) 487–499 (Morgan Kaufmann, 1994).Laufkötter, C. et al. Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem. Biogeosciences 13, 4023–4047 (2016).Article 

Google Scholar 
Iudicone, D. Some may like it hot. Nat. Geosci. https://doi.org/10.1038/s41561-020-0535-z (2020).Gorsky, G. et al. Expanding Tara Oceans protocols for underway, ecosystemic sampling of the ocean–atmosphere interface during Tara Pacific expedition (2016–2018). Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00750 (2019).Istace, B. et al. de novo assembly and population genomic survey of natural yeast isolates with the Oxford Nanopore MinION sequencer. Gigascience https://doi.org/10.1093/gigascience/giw018 (2017).Grand, M. M. et al. Developing autonomous observing systems for micronutrient trace metals. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00035 (2019).Becker, R. A., Wilks, A. R., Brownrigg, R., Minka, T. P. & Deckmyn, A. maps: Draw geographical maps. R version 3.5.0 https://cran.r-project.org/web/packages/maps/index.html (2021).Jaccard, P. Distribution comparée de la flore alpine dans quelques régions des Alpes occidentales et orientales. Bull. Murith. 31, 81–92 (1902).
Google Scholar 
Watson, R. A. A database of global marine commercial, small-scale, illegal and unreported fisheries catch 1950–2014. Sci. Data https://doi.org/10.1038/sdata.2017.39 (2017).Maritime Boundaries Geodatabase: Maritime Boundaries and Exclusive Economic Zones (200NM), version 11 (Flanders Marine Institute, 2019); https://doi.org/10.14284/386Aumont, O., Ethé, C., Tagliabue, A., Bopp, L. & Gehlen, M. PISCES-v2: an ocean biogeochemical model for carbon and ecosystem studies. Geosci. Model Dev. https://doi.org/10.5194/gmd-8-2465-2015 (2015).Bibby, T. S. & Moore, C. M. Silicate:nitrate ratios of upwelled waters control the phytoplankton community sustained by mesoscale eddies in sub-tropical North Atlantic and Pacific. Biogeosciences https://doi.org/10.5194/bg-8-657-2011 (2011).Brun, P., Kiørboe, T., Licandro, P. & Payne, M. R. The predictive skill of species distribution models for plankton in a changing climate. Glob. Change Biol. https://doi.org/10.1111/gcb.13274 (2016).Redfield, A. C. in James Johnstone Memorial Volume (ed. Daniel, R. J.) 176–192 (Liverpool Univ. Press, 1934).Michelangeli, P. A., Vrac, M. & Loukos, H. Probabilistic downscaling approaches: application to wind cumulative distribution functions. Geophys. Res. Lett. https://doi.org/10.1029/2009GL038401 (2009).Ridgeway, G. gbm: Generalized boosted regression models. R version 1.6–3.1 https://cran.r-project.org/web/packages/gbm/gbm.pdf (2010).Breiman, L. & Cutler, A. randomForest: Breiman and Cutler’s random forests for classification and regression. R package 4.1.0 https://www.stat.berkeley.edu/~breiman/RandomForests/ (2012).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S 4th edn (Springer, 2002).Wood, S. N. Stable and efficient multiple smoothing parameter estimation for generalized additive models. J. Am. Stat. Assoc. https://doi.org/10.1198/016214504000000980 (2004).Fawcett, T. An introduction to ROC analysis. Pattern Recognit. Lett. https://doi.org/10.1016/j.patrec.2005.10.010 (2006).Biecek, P. DALEX: explainers for complex predictive models. J. Mach. Learn. Res. 19, 1–5 (2018).
Google Scholar 
Jones, M. C. & Cheung, W. W. L. Multi-model ensemble projections of climate change effects on global marine biodiversity. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsu172 (2015).Vallejos, C. A. Exploring a world of a thousand dimensions. Nat. Biotechnol. https://doi.org/10.1038/s41587-019-0330-9 (2019).Kaufman, L. and Rousseeuw, P.J. in Statistical Data Analysis Based on the L1 Norm and Related Methods (ed. Dodge, Y.) 405–416 (North-Holland, 1987).Rousseeuw, P. J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. https://doi.org/10.1016/0377-0427(87)90125-7 (1987).Orsi, A. H., Whitworth, T. & Nowlin, W. D. On the meridional extent and fronts of the Antarctic Circumpolar Current. Deep Sea Res. Part I https://doi.org/10.1016/0967-0637(95)00021-W (1995).Hubert, L. & Arabie, P. Comparing partitions. J. Classif. https://doi.org/10.1007/BF01908075 (1985).Somerfield, P. J. Identification of the Bray–Curtis similarity index: comment on Yoshioka (2008). Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps07841 (2008).Bloom, S. Similarity indices in community studies: potential pitfalls. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps005125 (1981).Welch, B. L. The generalisation of student’s problems when several different population variances are involved. Biometrika 34, 28–35 (1947).CAS 

Google Scholar 
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
Google Scholar 
Mann, H. B. & Whitney, D. R. On a test of whether one of two random variables is stochastically larger than the other. Ann. Math. Stat. 18, 50–60 (1947).Article 

Google Scholar 
Sthle, L. & Wold, S. Analysis of variance (ANOVA). Chemom. Intell. Lab. Syst. 6, 259–272 (1989).Article 

Google Scholar 
Bozdogan, H. Model selection and Akaike’s Information Criterion (AIC): the general theory and its analytical extensions. Psychometrika 52, 345–370 (1987).Article 

Google Scholar 
Frémont, P. et al. Biogeographies of genomic provinces from ‘Restructuring of plankton genomic biogeography in the surface ocean under climate change’. figshare. https://figshare.com/articles/dataset/Biogeographies_genomic_provinces/19071620 (2022). More

Ferguson-Lees, J., Christie, D. A. Raptors of the World. Helm Identification Guides (Christopher Helm, London, 2001).
Google Scholar 
BirdLife International 2021 Species factsheet: Milvus migrans. Downloaded from http://www.birdlife.org on 10 May 2021.Sergio, F., Pedrini, P. & Marchesi, L. Adaptive selection of foraging and nesting habitat by black kites Milvus migrans and its implications for conservation: a multi-scale approach. Biol. Conserv. 112, 351–362 (2003).
Google Scholar 
Tanferna, A., López-Jiménez, L., Blas, J., Hiraldo, F. & Sergio, F. Habitat selection by Black kite breeders and floaters: implications for conservation management of raptor floaters. Biol. Conserv. 160, 1–9 (2013).
Google Scholar 
Cortés-Avizanda, A. et al. Spatial heterogeneity in resource distribution promotes facultative sociality in two Trans-Saharan migratory birds. PLoS ONE 6, e21016 (2011).ADS 
PubMed 
PubMed Central 

Google Scholar 
Panuccio, M., Agostini, N., Mellone. U. & Bogliani, G. Circannual variation in movement patterns of the Black Kite (Milvus migrans migrans): A review. Ethol. Ecol. Evol. 26, 1–18 (2013).Dickinson, E. C. & Remsen, J. V. The Howard and Moore Complete Checklist of the Birds of the World, 4th (Aves Press, 2013).
Google Scholar 
Clements, J. F. et al. The eBird/Clements Checklist of Birds of the World: v2019. (2019).Orta, J., Marks, J. S., Garcia, E. & Kirwan, G. M. Black Kite (Milvus migrans). In Birds of the World (eds. Billerman, S.M., Keeney, B.K., Rodewald, P.G. & Schulenberg T.S.) 168–172 (Cornell Lab of Ornithology, 2020).Gill, F., Donsker, D. & Rasmussen, P. IOC World Bird List – version 11.1 (worldbirdnames.org., 2021).Dementiev, G. P., Gladkov, N. A., Ptushenko, E. S., Spangenberg, E. P. & Sudilovskaya, A. M. Birds of the Soviet Union, Vol. 1 (Sovetskaya Nauka, Moscow, in Russian, 1951).
Google Scholar 
Stepanyan, L. S. Conspectus of the Ornithological Fauna of the USSR (Nauka, Moscow, in Russian, 1990).Karyakin, I. Problem of identification of Eurasian subspecies of the Black Kite and records of the Pariah Kite in Southern Siberia, Russia. Raptors Conserv. 34, 49–67 (2017).
Google Scholar 
Skyrpan, M. & Literák, I. A kite Milvus migrans migrans/lineatus in Ukraine. Biologia 74, 1669–1673 (2019).
Google Scholar 
Panter, C. T. et al. Kites (Milvus spp.) wintering on Crete. Eur. Zool. J. 87, 591–596 (2020).
Google Scholar 
Skyrpan, M. et al. Kites Milvus migrans lineatus (Milvus migrans migrans/lineatus) are spreading west across Europe. J. Ornithol. 162, 317–323 (2021).
Google Scholar 
Onrubia Baticón A. Patrones espacio-temporales de la migración de aves planeadoras en el Estrecho de Gibraltar (Spatial and temporal patterns of soaring birds migration through the straits of Gibraltar). Doctoral thesis (Universidad de León, 2015).Literák, I. et al. Weather-influenced water-crossing behaviour of black kites Milvus migrans during migration. Biologia 76, 1267–1273 (2021).
Google Scholar 
Ovčiariková, S. et al. Natal dispersal in Black Kites Milvus migrans migrans in Europe. J. Ornithol. 161, 935–951 (2020).
Google Scholar 
Sklyarenko, S., Gavrilov, E. & Gavrilov, A. Migratory flyways of raptors and owls in Kazakhstan according to ringing data. Vogelwarte 41, 263–268 (2002).
Google Scholar 
Probst, R. & Pavličev, M. Migration in the Novosibirsk region and the Kuznetsky Alatau, Russia. Sandgrouse 28, 114–118 (2006).
Google Scholar 
Harris, T. Migration Hotspots. The World’s Best Bird Migration Sites. (Bloomsbury, London, New Delhi, New York, Sydney, 2013).Hirano, T. & Ueda, M. Black Kite Milvus migrans in Japanese. Bird Res. News 810, 1–6 (2011).
Google Scholar 
Choudhuri, A. Migration of Black-eared or Large Indian Kite Milvus migrans lineatus Gray from Mongolia to North-Eastern India. J. Bombay Nat. Hist. Soc. 102, 229–230 (2005).
Google Scholar 
Davaasuren, B. Khurkh Bird Ringing Station Annual Report 2018. (Wildlife Science Conservation Center of Mongolia, Ulaanbaatar, 2019).Kumar, N. et al. GPS-telemetry unveils the regular high-elevation crossing of the Himalayan by a migratory raptor: Implications for definition of a “Central Asian Flyway”. Sci. Rep. 10, 15988 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Juhant, M. A. & Bildstein, K. L. Raptor migration across and around the Himalayas. In Bird Migration Across the Himalayas (eds. Prins, H. H. T. & Namgail, T.) 98–116 (Cambridge University Press, Cambridge, 2017).
Google Scholar 
Rotics, S. et al. The challenges of the first migration: Movement and behaviour of juvenile vs. adult white storks with insights regarding juvenile mortality. J. Anim. Ecol. 85, 938–947 (2016).PubMed 

Google Scholar 
Vidal-Mateo, J. et al. Wind effects on the migration routes of trans-Saharan soaring raptors: Geographical, seasonal and interspecific variation. Curr. Zool. 62, 89–97 (2016).PubMed 
PubMed Central 

Google Scholar 
Safi, K. et al. Flying with the wind: Scale dependency of speed and direction measurements in modelling wind support in avian flight. Mov. Ecol. 1, 4 (2013).PubMed 
PubMed Central 

Google Scholar 
Green, M., Alerstam, T., Clausen, P., Drent, R. & Ebbinge, B. S. Dark-bellied Brent Geese Branta bernicla bernicla, as recorded by satellite telemetry, do not minimize flight distance during spring migration. Ibis 144, 106–121 (2002).
Google Scholar 
Malmiga, G., Nilsson, C., Bäckman, J. & Alerstam, T. Interspecific comparison of the flight performance between sparrowhawks and common buzzards migrating at the Falsterbo peninsula: a radar study. Curr. Zool. 605, 670–679 (2014).
Google Scholar 
Vansteelant, W. M. G. et al. Regional and seasonal flight speeds of soaring migrants and the role of weather conditions at hourly and daily scales. J. Avian Biol. 46, 25–39 (2015).
Google Scholar 
Dodge, S., Bohrer, G. & Weinzierl, R. MoveBank track annotation project: linking animal movement data with the environment to discover the impact of environmental change in animal migration. In Workshop on GIScience in the Big Data Age in Conjunction with the Seventh International Conference on Geographic Information Science 2012 GIScience (eds. Janowicz, K., Kessler, C., Kauppinen, T. & Kolas, D.) 35–41 (Columbus, OH, 2012).Scott, G. R. et al. How bar-headed geese fly over the Himalayas. Physiology 30, 107–115 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Andreyenkova, N. G., Andreyenkov, O. V., Karyakin, I. V. & Zhimulev, I. F. New haplotypes of the mitochondrial gene cytB in the nesting population of the Siberian Black Kite Milvus migrans lineatus Gray, 1831 in the territory of the Republic of Tyva. Dokl. Biochem. Biophys. 482, 242–244 (2018).CAS 
PubMed 

Google Scholar 
Mellone, U. et al. Interspecific comparison of the performance of soaring migrants in relation to morphology, meteorological conditions and migration strategies. PLoS ONE 7, e39833 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kemp, M. U., Emiel van Loon, E., Shamoun-Baranes, J. & Bouten, W. RNCEP: global weather and climate data at your fingertips. Methods Ecol. Evol. 3, 65–70 (2012).
Google Scholar 
Team, R.C. R: A Language and Environment for Statistical Computing. R 739 (Foundation for Statistical Computing [Internet], Vienna, Austria, 2018). https://www.R-project.org/Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Google Scholar 
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
Andreyenkova, N. G. et al. Phylogeography and demographic history of the Black Kite Milvus migrans, raptor widespread in Eurasia, Australia and Africa. J. Avian Biol. 52, e02822 (2021).
Google Scholar 
Lindholm, A. & Forsten, A. Black Kites Milvus migrans in Russian Altai. Caluta 2, 1–6 (2011).
Google Scholar 
Vansteelant, W.M.G. An ontogenetic perspective on migration learning and critical life-history traits in raptors. In Abstracts of British Ornithologists’ Union 2019 Annual Conference Tracking Migration: Drivers, Challenges and Consequences of Seasonal Movements. 45–46. (University of Warwick, UK, 2019).Dixon, A., Rahman, L., Sokolov, A. & Sokolov, V. Peregrine Falcons crossing the „Roof of the World”. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.) 128–141 (Cambridge University Press, Cambridge, 2017).Parr, N. et al. High altitude flights by ruddy shelduck Tadorna ferruginea during trans-Himalayan migrations. J. Avian Biol. 48, 1310–1315 (2017).
Google Scholar 
Hawkes, L. A. et al. The paradox of extreme high-altitude migration in bar-headed geese Anser indicus. Proc. R. Soc. B 280, 1–8 (2013).
Google Scholar 
Bishop, C. M. et al. The roller coaster flight strategy of bar-headed geese conserves energy during Himalayan migrations. Science 347, 250–254 (2015).ADS 
CAS 
PubMed 

Google Scholar 
Agostini, N., Pannucio, M. & Pasquaretta, C. Morphology, flight performace, and water crossing tendencies of Afro-Palearctic raptors during migration. Curr. Zool. 61, 951–958 (2015).PubMed 
PubMed Central 

Google Scholar 
Altshuler, D. & Dudley, R. The physiology and biomechanics of avian flight at high altitude. Integr. Comp. Biol. 46, 62–71 (2006).PubMed 

Google Scholar 
Santos, C. D. et al. Match between soaring modes of black kites and the fine-scale distribution of updrafts. Sci. Rep. 7, 6421 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
Ohlmann, K. The wind system in the Himalayas: From a Bird’s-Eye View. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.), 9–28 (Cambridge University Press, Cambridge, 2017).Heise, R. Birds, gliders and uplift systems over the Himalayas. In Bird Migration Across the Himalayas, Wetland Functioning Amidst Mountains and Glaciers (eds. Prins, H.T. & Namgail, T.), 229–40 (Cambridge University Press, Cambridge, 2017).Harel, R. et al. Decision-making by a soaring bird: time, energy and risk considerations at different spatiotemporal scales. Philos. T. R. Soc. B 371, 20150397 (2016).
Google Scholar 
Vansteelant, W. M. G., Shamoun-Baranes, J., McLaren, J., van Diermen, J. & Bouten, W. Soaring across continents: Decision-making of a soaring migrant under changing atmospheric conditions along an entire flyway. J. Avian Biol. 48, 887–896 (2017).
Google Scholar 
Nilsson, C., Klaassen, R. H. G. & Alerstam, T. Differences in speed and duration of bird migration between spring and autumn. Am. Nat. 181, 837–845 (2013).PubMed 

Google Scholar 
Kokko, H. Competition for early arrival in migratory birds. J. Anim. Ecol. 68, 940–150 (1999).
Google Scholar 
Moore, F.R., Smith, R.J. & Sandberg, R. Stopover ecology of intercontinental migrants: en route problems and consequences for reproductive performance. In Birds of Two Worlds: the Ecology and Evolution of Migration (eds. Greenberg, R. & Marra, P.P.), 251–261 (Johns Hopkins University Press, Baltimore, 2005).McNamara, J. M., Welham, R. K. & Houston, A. I. The timing of migration within the context of an annual routine. J. Avian Biol. 29, 416–423 (1998).
Google Scholar 
Köppen, U. et al. Seasonal migrations of four individual bar-headed geese Anser indicus from Kyrgyzstan followed by satellite telemetry. J. Ornithol. 151, 703–712 (2010).
Google Scholar 
Kölzsch, A. et al. Towards a new understanding of migration timing: slower spring than autumn migration in geese reflects different decision rules for stopover use and departure. Oikos 125, 1496–1507 (2016).
Google Scholar 
Butler, R. W., Williams, T. D., Warnock, N. & Bishop, M. A. Wind assistance: a requirement for migration of shorebirds? Auk 114, 456–466 (1997).
Google Scholar 
Santos, C. D., Silva, J. P., Muñoz, A. R., Onrubia, A. & Wikelski, M. The gateway to Africa: What determines sea crossing performance of a migratory soaring birds at the Strait of Gibraltar. J. Anim. Ecol. 89, 1317–1328 (2020).PubMed 

Google Scholar 
Kumerloeve, H. V. Überwintern des Schwarzmilans im vorderen Orient. Falke 14, 274–227 (1967).
Google Scholar 
Baumgart, W., Kasparek, M. & Stephan, B. Die Vögel Syrien: eine Übersicht (Max Kasparek Verlag, 1995).
Google Scholar 
Tsvelykh, A. N. & Panyushkin, V. E. Wintering of the Black Kite Milvus migrans in Ukraine. Vestn. Zool. 36, 81–83 (2002).
Google Scholar 
Sarà, M. The colonisation of Sicily by the Black Kite Milvus migrans. J. Raptor Res. 37, 167–172 (2003).
Google Scholar 
Domashevskii, S. V. First record of the Black Kite in winter in the northern part of Ukraine. Berkut 18, 212–213 (2009).
Google Scholar 
Ciach, M. & Kruszyk, R. Foraging of White Storks Ciconia ciconia on rubbish dumps on nonbreeding grounds. Waterbirds 33, 101–104 (2010).
Google Scholar 
Biricik, M. & Karakaş. R. Black Kites Milvus migrans winter in Southeastern Anatolia, Turkey. J. Raptor Res. 45, 370–373 (2011).Literák, I., Horal, D., Alivizatos, H. & Matušík, H. Common wintering of black kites Milvus migrans migrans in Greece, and new data on their wintering elsewhere in Europe. Slovak Raptor J. 11, 91–102 (2017).
Google Scholar 
Shirihai, H., Yosef, R., Alon, D., Kirwan, G.M. & Spaar, R. Raptor Migration in Israel and the Middle East (International Birdwatching Centre Eilat IBRCE, IOC, Israel, 2000).Forsman, D. Identification of Black-eared Kite. Bird. World 16, 156–216 (2003).
Google Scholar 
Abuladze, A. Birds of Prey of Georgia, Materials towards a Fauna of Georgia, Issue VI (Ilia State University, Tbilisi, 2013).
Google Scholar 
Brooke, R. K. The migratory Black Kite Milvus migrans migrans Aves: Accipitridae of the Palearctic in southern Africa. Durb. Mus. Novit. 10, 53–66 (1974).
Google Scholar 
Forsman, D. Flight Identifcation of Raptors of Europe (North Africa and the Middle East (Christopher Helm, 2016).
Google Scholar 
Percie du Sert, N. et al. The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLoS Biol. 18, e3000410 (2020). More

Nathan, R. The challenges of studying dispersal. Trends. Ecol. Evol. 16, 481–483. https://doi.org/10.1016/S0169-5347(01)02272-8 (2001).CAS 
Article 

Google Scholar 
Bonte, D. et al. Costs of dispersal. Biol. Rev. Camb. Philos. Soc. 87, 290–312. https://doi.org/10.1111/j.1469-185X.2011.00201.x (2012).Article 
PubMed 

Google Scholar 
Matthysen, E. Multicausality of dispersal: A review. In Dispersal Ecology and Evolution (eds Clobert, J. et al.) 3–18 (Oxford University Press, 2012).Chapter 

Google Scholar 
Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209. https://doi.org/10.1111/j.1461-0248.2008.01267.x (2009).Article 
PubMed 

Google Scholar 
Poethke, H. J. & Hovestadt, T. Evolution of density- and patch-size-dependent dispersal rates. Proc. R. Soc. Lond. 269, 637–645. https://doi.org/10.1098/rspb.2001.1936 (2002).Article 

Google Scholar 
Benton, T. G. & Bowler, D. E. Dispersal in invertebrates: Influences on individual decisions. Ecol. Evol. 1, 41–49 (2012).
Google Scholar 
Legrand, D. et al. Ranking the ecological causes of dispersal in a butterfly. Ecography 38, 822–831. https://doi.org/10.1111/ecog.01283 (2015).Article 

Google Scholar 
Travis, J. M. J., Murrell, D. J. & Dytham, C. The evolution of density–dependent dispersal. Proc. R. Soc. Lond. B 266, 1837–1842. https://doi.org/10.1098/rspb.1999.0854 (1999).Article 

Google Scholar 
Matthysen, E. Density-dependent dispersal in birds and mammals. Ecography 28, 403–416. https://doi.org/10.1111/j.0906-7590.2005.04073.x (2005).Article 

Google Scholar 
de Meester, N., Derycke, S., Rigaux, A. & Moens, T. Active dispersal is differentially affected by inter- and intraspecific competition in closely related nematode species. Oikos 124, 561–570. https://doi.org/10.1111/oik.01779 (2015).Article 

Google Scholar 
Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225. https://doi.org/10.1017/S1464793104006645 (2005).Article 
PubMed 

Google Scholar 
Bengtsson, G., Hedlund, K. & Rundgren, S. Food- and density-dependent dispersal: Evidence from a soil collembolan. J. Anim. Ecol. 63, 513. https://doi.org/10.2307/5218 (1994).Article 

Google Scholar 
Fellous, S., Duncan, A., Coulon, A. & Kaltz, O. Quorum sensing and density-dependent dispersal in an aquatic model system. PLoS ONE 7, e48436. https://doi.org/10.1371/journal.pone.0048436 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Aguillon, S. M. & Duckworth, R. A. Kin aggression and resource availability influence phenotype-dependent dispersal in a passerine bird. Behav. Ecol. Sociobiol. 69, 625–633. https://doi.org/10.1007/s00265-015-1873-5 (2015).Article 

Google Scholar 
Byers, J. E. Effects of body size and resource availability on dispersal in a native and a non-native estuarine snail. J. Exp. Mar. Biol. Ecol. 248, 133–150. https://doi.org/10.1016/S0022-0981(00)00163-5 (2000).CAS 
Article 
PubMed 

Google Scholar 
de Meester, N., Derycke, S. & Moens, T. Differences in time until dispersal between cryptic species of a marine nematode species complex. PLoS ONE 7, e42674. https://doi.org/10.1371/journal.pone.0042674 (2012).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Sepulveda, A. J. & Marczak, L. B. Active dispersal of an aquatic invader determined by resource and flow conditions. Biol. Invasions 14, 1201–1209. https://doi.org/10.1007/s10530-011-0149-x (2012).Article 

Google Scholar 
Lobbia, P. A. & Mougabure-Cueto, G. Active dispersal in Triatoma infestans (Klug, 1834) (Hemiptera Reduviidae: Triatominae): Effects of nutritional status, the presence of a food source and the toxicological phenotype. Acta Trop. 204, 105345. https://doi.org/10.1016/j.actatropica.2020.105345 (2020).CAS 
Article 
PubMed 

Google Scholar 
Barbraud, C., Johnson, A. R. & Bertault, G. Phenotypic correlates of post-fledging dispersal in a population of greater flamingos: The importance of body condition. J. Anim. Ecol. 72, 246–257. https://doi.org/10.1046/j.1365-2656.2003.00695.x (2003).Article 

Google Scholar 
Bonte, D. & de La Peña, E. Evolution of body condition-dependent dispersal in metapopulations. J. Evol. Biol. 22, 1242–1251. https://doi.org/10.1111/j.1420-9101.2009.01737.x (2009).CAS 
Article 
PubMed 

Google Scholar 
Moran, N. P., Sánchez-Tójar, A., Schielzeth, H. & Reinhold, K. Poor nutritional condition promotes high-risk behaviours: A systematic review and meta-analysis. Biol. Rev. Camb. Philos. Soc. 96, 269–288. https://doi.org/10.1111/brv.12655 (2021).Article 
PubMed 

Google Scholar 
Altermatt, F. & Fronhofer, E. A. Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities. Freshw. Biol. 63, 22–32. https://doi.org/10.1111/fwb.12951 (2018).Article 

Google Scholar 
McCauley, S. J. & Rowe, L. Notonecta exhibit threat-sensitive, predator-induced dispersal. Biol. Lett. 6, 449–452. https://doi.org/10.1098/rsbl.2009.1082 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Baines, C. B., McCauley, S. J. & Rowe, L. Dispersal depends on body condition and predation risk in the semi-aquatic insect, Notonecta undulata. Ecol. Evol. 5, 2307–2316. https://doi.org/10.1002/ece3.1508 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Hammill, E., Fitzjohn, R. G. & Srivastava, D. S. Conspecific density modulates the effect of predation on dispersal rates. Oecologia 178, 1149–1158. https://doi.org/10.1007/s00442-015-3303-9 (2015).ADS 
Article 
PubMed 

Google Scholar 
Fronhofer, E. A. et al. Bottom-up and top-down control of dispersal across major organismal groups. Nat. Ecol. Evol. 2, 1859–1863. https://doi.org/10.1038/s41559-018-0686-0 (2018).Article 
PubMed 

Google Scholar 
Delm, M. Vigilance for predators: Detection and dilution effects. Behav. Ecol. Sociobiol. https://doi.org/10.1007/BF00171099 (1990).Article 

Google Scholar 
Matthysen, E. Multicausality of dispersal: A review. Ecol. Evol. 1, 3–18 (2012).
Google Scholar 
Bowler, D. E. & Benton, T. G. Variation in dispersal mortality and dispersal propensity among individuals: The effects of age, sex and resource availability. J. Anim. Ecol. 78, 1234–1241. https://doi.org/10.1111/j.1365-2656.2009.01580.x (2009).Article 
PubMed 

Google Scholar 
Giere, O. Meiobenthology. The microscopic motile fauna of aquatic sediments 2nd edn. (Springer, 2009).
Google Scholar 
Ptatscheck, C. & Traunspurger, W. The ability to get everywhere: Dispersal modes of free-living, aquatic nematodes. Hydrobiologia 22, 71. https://doi.org/10.1007/s10750-020-04373-0 (2020).Article 

Google Scholar 
Ptatscheck, C. & Gansfort, B. Dispersal of free-living nematodes. In Ecology of Freshwater Nematodes (ed. Traunspurger, W.) 151–184 (CABI, 2021).Chapter 

Google Scholar 
Traunspurger, W., Bergtold, M., Ettemeyer, A. & Goedkoop, W. Effects of copepods and chironomids on the abundance and vertical distribution of nematodes in a freshwater sediment. J. Freshw. Ecol. 21, 81–90. https://doi.org/10.1080/02705060.2006.9664100 (2006).Article 

Google Scholar 
Bargmann, C. I. Chemosensation in C. elegans. WormBook 1, 1–29. https://doi.org/10.1895/wormbook.1.123.1 (2006).Article 

Google Scholar 
Chasnov, J. R. & Chow, K. L. Why are there males in the hermaphroditic species Caenorhabditis elegans?. Genetics 160, 983–994 (2002).CAS 
Article 

Google Scholar 
Ramot, D., Johnson, B. E., Berry, T. L., Carnell, L. & Goodman, M. B. The Parallel Worm Tracker: A platform for measuring average speed and drug-induced paralysis in nematodes. PLoS ONE 3, e2208. https://doi.org/10.1371/journal.pone.0002208 (2008).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Muschiol, D. & Traunspurger, W. Life cycle and calculation of the intrinsic rate of natural increase of two bacterivorous nematodes, Panagrolaimus sp. and Poikilolaimus sp. from chemoautotrophic Movile Cave, Romania. Nematology 9, 271–284. https://doi.org/10.1163/156854107780739117 (2007).Article 

Google Scholar 
Beier, S., Bolley, M. & Traunspurger, W. Predator-prey interactions between Dugesia gonocephala and free-living nematodes. Freshw. Biol. 49, 77–86. https://doi.org/10.1046/j.1365-2426.2003.01168.x (2004).Article 

Google Scholar 
Powers, E. M. & Sayre, R. M. A predacious soil turbellarian that feeds on free-living and plant-parasitic nematodes. Nematology 12, 619–629. https://doi.org/10.1163/187529266X00482 (1966).Article 

Google Scholar 
Kreuzinger-Janik, B., Kruscha, S., Majdi, N. & Traunspurger, W. Flatworms like it round: Nematode consumption by Planaria torva (Müller 1774) and Polycelis tenuis (Ijima 1884). Hydrobiologia 819, 231–242. https://doi.org/10.1007/s10750-018-3642-8 (2018).Article 

Google Scholar 
Burnham, K. P. & Anderson, D. R. Practical use of the information-theoretic approach. In Model Selection and Inference (eds Burnham, K. P. & Anderson, D. R.) 75–117 (Springer, 1998).Chapter 

Google Scholar 
McCulloch, C. E., Searle, S. R. & Neuhaus, J. M. Generalized, Linear, and Mixed Models (Wiley, 2008).MATH 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/.Mazerolle, M. J. AICcmodavg: Model Selection and Multimodel Inference Based on (Q)AIC(c) (2020).Bonte, D., de Roissart, A., Wybouw, N. & van Leeuwen, T. Fitness maximization by dispersal: Evidence from an invasion experiment. Ecology 95, 3104–3111. https://doi.org/10.1890/13-2269.1 (2014).Article 

Google Scholar 
You, Y., Kim, J., Raizen, D. M. & Avery, L. Insulin, cGMP, and TGF-beta signals regulate food intake and quiescence in C. elegans: a model for satiety. Cell Metab. 7, 249–257. https://doi.org/10.1016/j.cmet.2008.01.005 (2008).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Shtonda, B. B. & Avery, L. Dietary choice behavior in Caenorhabditis elegans. J. Exp. Biol. 209, 89–102. https://doi.org/10.1242/jeb.01955 (2006).Article 
PubMed 

Google Scholar 
Mathieu, J. et al. Habitat quality, conspecific density, and habitat pre-use affect the dispersal behaviour of two earthworm species, Aporrectodea icterica and Dendrobaena veneta, in a mesocosm experiment. Soil Biol. Biochem. 42, 203–209. https://doi.org/10.1016/j.soilbio.2009.10.018 (2010).CAS 
Article 

Google Scholar 
Oro, D., Cam, E., Pradel, R. & Martínez-Abraín, A. Influence of food availability on demography and local population dynamics in a long-lived seabird. Proc. R. Soc. Lond. B 271, 387–396. https://doi.org/10.1098/rspb.2003.2609 (2004).Article 

Google Scholar 
Harvey, S. C. Non-dauer larval dispersal in Caenorhabditis elegans. J. Exp. Zool. B Mol. Dev. Evol. 312B, 224–230. https://doi.org/10.1002/jez.b.21287 (2009).Article 
PubMed 

Google Scholar 
Wilden, B., Majdi, N., Kuhlicke, U., Neu, T. R. & Traunspurger, W. Flatworm mucus as the base of a food web. BMC Ecol. 19, 15. https://doi.org/10.1186/s12898-019-0231-2 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Gloria-Soria, A. & Azevedo, R. B. R. npr-1 Regulates foraging and dispersal strategies in Caenorhabditis elegans. Curr. Biol. 18, 1694–1699. https://doi.org/10.1016/j.cub.2008.09.043 (2008).CAS 
Article 
PubMed 

Google Scholar 
Harrison, R. G. Dispersal Polymorphisms in Insects. Annu. Rev. Ecol. Syst. 11, 95–118. https://doi.org/10.1146/annurev.es.11.110180.000523 (1980).Article 

Google Scholar 
Denno, R. F. & Peterson, M. A. Density-dependent dispersal and its consequences for population dynamics. Popul Dyn 1, 113–130 (2021).
Google Scholar 
Srinivasan, J. et al. A modular library of small molecule signals regulates social behaviors in Caenorhabditis elegans. PLoS Biol. 10, e1001237. https://doi.org/10.1371/journal.pbio.1001237 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bretscher, A. J. et al. Temperature, oxygen, and salt-sensing neurons in C. elegans are carbon dioxide sensors that control avoidance behavior. Neuron 69, 1099–1113. https://doi.org/10.1016/j.neuron.2011.02.023 (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Freckman, D. W., Duncan, D. A. & Larson, J. R. Nematode density and biomass in an annual grassland ecosystem. J. Range Manag. 32, 418. https://doi.org/10.2307/3898550 (1979).Article 

Google Scholar 
Cote, J. et al. Evolution of dispersal strategies and dispersal syndromes in fragmented landscapes. Ecography 40, 56–73. https://doi.org/10.1111/ecog.02538 (2017).Article 

Google Scholar  More

FAO & UNEP. The state of the world’s forests 2020: Forests, biodiversity and people (2020).Brinck, K. et al. High resolution analysis of tropical forest fragmentation and its impact on the global carbon cycle. Nature Communications 8, 1–6 (2017).ADS 

Google Scholar 
Mitchard, E. T. The tropical forest carbon cycle and climate change. Nature 559, 527–534 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. science 342, 850–853 (2013).ADS 
CAS 
PubMed 

Google Scholar 
Tsujino, R., Yumoto, T., Kitamura, S., Djamaluddin, I. & Darnaedi, D. History of forest loss and degradation in Indonesia. Land use policy 57, 335–347 (2016).
Google Scholar 
Holl, K. D. & Brancalion, P. H. Tree planting is not a simple solution. Science 368, 580–581 (2020).ADS 
CAS 
PubMed 

Google Scholar 
Zhang, Y. et al. Multiple afforestation programs accelerate the greenness in the ‘Three North’ region of China from 1982 to 2013. Ecological Indicators 61, 404–412 (2016).
Google Scholar 
Soliño, M., Oviedo, J. L. & Caparrós, A. Are forest landowners ready for woody energy crops? Preferences for afforestation programs in Southern Spain. Energy Economics 73, 239–247 (2018).
Google Scholar 
Paquette, A. & Messier, C. The role of plantations in managing the world’s forests in the Anthropocene. Frontiers in Ecology and the Environment 8, 27–34 (2010).
Google Scholar 
Zulkefli, F., Syahlan, S. & Aziz, M. F. A. Negatives Impact Faced by Oil Palm Estate Management in managing Foreign Workers: A Case Study. International Journal of Academic Research in Business and Social Sciences 8 (2018).Fitzherbert, E. B. et al. How will oil palm expansion affect biodiversity? Trends in ecology & evolution 23, 538–545 (2008).
Google Scholar 
Vijay, V., Pimm, S. L., Jenkins, C. N. & Smith, S. J. The impacts of oil palm on recent deforestation and biodiversity loss. PloS one 11, e0159668 (2016).PubMed 
PubMed Central 

Google Scholar 
Koh, L. P., Miettinen, J., Liew, S. C. & Ghazoul, J. Remotely sensed evidence of tropical peatland conversion to oil palm. Proceedings of the National Academy of Sciences 108, 5127–5132 (2011).ADS 
CAS 

Google Scholar 
Guillaume, T. et al. Carbon costs and benefits of Indonesian rainforest conversion to plantations. Nature communications 9, 1–11 (2018).CAS 

Google Scholar 
Lucas-Borja, M. E., Hedo, J., Cerdá, A., Candel-Pérez, D. & Viñegla, B. Unravelling the importance of forest age stand and forest structure driving microbiological soil properties, enzymatic activities and soil nutrients content in Mediterranean Spanish black pine (Pinus nigra Ar. ssp. salzmannii) Forest. Science of the Total Environment 562, 145–154 (2016).ADS 
CAS 

Google Scholar 
Besnard, S. et al. Quantifying the effect of forest age in annual net forest carbon balance. Environmental Research Letters 13, 124018 (2018).ADS 

Google Scholar 
Dzikiti, S. et al. Estimating the water requirements of high yielding and young apple orchards in the winter rainfall areas of South Africa using a dual source evapotranspiration model. Agricultural water management 208, 152–162 (2018).
Google Scholar 
Zhang, Y., Yao, Y., Wang, X., Liu, Y. & Piao, S. Mapping spatial distribution of forest age in China. Earth and Space Science 4, 108–116 (2017).ADS 

Google Scholar 
Chen, B., Jin, Y. & Brown, P. Automatic mapping of planting year for tree crops with Landsat satellite time series stacks. ISPRS Journal of Photogrammetry and Remote Sensing 151, 176–188 (2019).ADS 

Google Scholar 
Danylo, O. et al. A map of the extent and year of detection of oil palm plantations in Indonesia, Malaysia and Thailand. Scientific data 8, 1–8 (2021).
Google Scholar 
O’Brien, S. T., Hubbell, S. P., Spiro, P., Condit, R. & Foster, R. B. Diameter, height, crown, and age relationship in eight neotropical tree species. Ecology 76, 1926–1939 (1995).
Google Scholar 
Fichtler, E., Clark, D. A. & Worbes, M. Age and long-term growth of trees in an old-growth tropical rain forest, based on analyses of tree rings and 14C1. Biotropica 35, 306–317 (2003).
Google Scholar 
Zhang, C. et al. Mapping forest stand age in China using remotely sensed forest height and observation data. Journal of Geophysical Research: Biogeosciences 119, 1163–1179 (2014).ADS 

Google Scholar 
Wang, B., Li, M., Fan, W., Yu, Y. & Chen, J. M. Relationship between net primary productivity and forest stand age under different site conditions and its implications for regional carbon cycle study. Forests 9, 5 (2018).
Google Scholar 
Wang, S. et al. Relationships between net primary productivity and stand age for several forest types and their influence on China’s carbon balance. Journal of environmental management 92, 1651–1662 (2011).PubMed 

Google Scholar 
Gupta, N., Kukal, S., Bawa, S. & Dhaliwal, G. Soil organic carbon and aggregation under poplar based agroforestry system in relation to tree age and soil type. Agroforestry Systems 76, 27–35 (2009).
Google Scholar 
Huang, C. et al. An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sensing of Environment 114, 183–198 (2010).ADS 

Google Scholar 
Thomas, N. E. et al. Validation of North American forest disturbance dynamics derived from Landsat time series stacks. Remote Sensing of Environment 115, 19–32 (2011).ADS 
MathSciNet 

Google Scholar 
Ye, S., Rogan, J., Zhu, Z. & Eastman, J. R. A near-real-time approach for monitoring forest disturbance using Landsat time series: Stochastic continuous change detection. Remote Sensing of Environment 252, 112167 (2021).ADS 

Google Scholar 
Verbesselt, J., Hyndman, R., Newnham, G. & Culvenor, D. Detecting trend and seasonal changes in satellite image time series. Remote sensing of Environment 114, 106–115 (2010).ADS 

Google Scholar 
Kennedy, R. E., Yang, Z. & Cohen, W. B. Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr—Temporal segmentation algorithms. Remote Sensing of Environment 114, 2897–2910 (2010).ADS 

Google Scholar 
Cohen, W. B., Yang, Z., Healey, S. P., Kennedy, R. E. & Gorelick, N. A LandTrendr multispectral ensemble for forest disturbance detection. Remote sensing of environment 205, 131–140 (2018).ADS 

Google Scholar 
Vogeler, J. C., Braaten, J. D., Slesak, R. A. & Falkowski, M. J. Extracting the full value of the Landsat archive: Inter-sensor harmonization for the mapping of Minnesota forest canopy cover (1973–2015). Remote sensing of environment 209, 363–374 (2018).ADS 

Google Scholar 
de Jong, S. M. et al. Mapping mangrove dynamics and colonization patterns at the Suriname coast using historic satellite data and the LandTrendr algorithm. International Journal of Applied Earth Observation and Geoinformation 97, 102293 (2021).
Google Scholar 
Harris, N., Goldman, E. D. & Gibbes, S. Spatial database of planted trees (SDPT VERSION 1.0). Technical Note. (2019).Descals, A. et al. High-resolution global map of smallholder and industrial closed-canopy oil palm plantations. Earth System Science Data 13, 1211–1231 (2021).ADS 

Google Scholar 
Li, C. et al. The first all-season sample set for mapping global land cover with landsat-8 data. Science Bulletin 62, 508–515 (2017).ADS 

Google Scholar 
Masek, J. G. et al. A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3, 68–72 (2006).ADS 

Google Scholar 
Vermote, E., Justice, C., Claverie, M. & Franch, B. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment 185, 46–56 (2016).ADS 
PubMed 

Google Scholar 
Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote sensing of environment 194, 379–390 (2017).ADS 

Google Scholar 
Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nature Geoscience 12, 632–637 (2019).CAS 

Google Scholar 
He, T. et al. Evaluating land surface albedo estimation from Landsat MSS, TM, ETM+, and OLI data based on the unified direct estimation approach. Remote Sensing of Environment 204, 181–196 (2018).ADS 

Google Scholar 
Flood, N. Continuity of reflectance data between Landsat-7 ETM+ and Landsat-8 OLI, for both top-of-atmosphere and surface reflectance: a study in the Australian landscape. Remote Sensing 6, 7952–7970 (2014).ADS 

Google Scholar 
Roy, D. P. et al. Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. Remote sensing of Environment 185, 57–70 (2016).ADS 
PubMed 

Google Scholar 
Key, C. & Benson, N. Landscape assessment: remote sensing of severity, the normalized burn ratio and ground measure of severity, the composite burn index. FIREMON: Fire effects monitoring and inventory system Ogden, Utah: USDA Forest Service, Rocky Mountain Res. Station (2005).Guo, J. & Gong, P. The potential of spectral indices in detecting various stages of afforestation over the Loess Plateau Region of China. Remote Sensing 10, 1492 (2018).ADS 

Google Scholar 
Kennedy, R. E. et al. Implementation of the LandTrendr algorithm on google earth engine. Remote Sensing 10, 691 (2018).ADS 

Google Scholar 
Yu, L. et al. A multi-resolution global land cover dataset through multisource data aggregation. Science China Earth Sciences 57, 2317–2329 (2014).ADS 

Google Scholar 
Du, Z. et al. A global map of planting years of plantations. figshare https://doi.org/10.6084/m9.figshare.19070084.v1 (2022).Gong, P. et al. Finer resolution observation and monitoring of global land cover: First mapping results with landsat tm and etm+ data. International Journal of Remote Sensing 34, 2607–2654 (2013).ADS 

Google Scholar 
Huang, H. et al. The migration of training samples towards dynamic global land cover mapping. ISPRS Journal of Photogrammetry and Remote Sensing 161, 27–36 (2020).ADS 

Google Scholar  More

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

Google Scholar 
Intergovernmental Panel on Climate Change. Climate Change 2013: 5th Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2013).
Google Scholar 
Torres, O., Kwiatkowski, L., Sutton, A. J., Dorey, N. & Orr, J. C. Characterizing mean and extreme diurnal variability of ocean CO2 system variables across marine environments. Geophys. Res. Lett. 48, 2 (2021).
Google Scholar 
Dorey, N., Lançon, P., Thorndyke, M. & Dupont, S. Assessing physiological tipping point of sea urchin larvae exposed to a broad range of pH. Glob. Change Biol. 19, 3355–3367 (2013).
Google Scholar 
Hauri, C. et al. Spatiotemporal variability and long-term trends of ocean acidification in the California current system. Biogeosci. Discuss. 9, 10371–10428 (2012).ADS 

Google Scholar 
Dupont, S. & Pörtner, H.-O. A snapshot into ocean acidification research. Mar. Biol. 160, 1765–1771 (2013).CAS 

Google Scholar 
Dupont, S. & Thorndyke, M. Chapter: Direct impacts of near-future ocean acidification on sea urchins. in Climate Change Perspective from the Atlantic: Past, Present and Future (eds. Fernández-Palacios, J. et al.) 461–485 (2013).Byrne, M. & Hernández, J. C. Chapter 16: Sea urchins in a high CO2 world: Impacts of climate warming and ocean acidification across life history stages. in Developments in Aquaculture and Fisheries Science vol. 43 281–297 (Elsevier, 2020).Kroeker, K. J., Kordas, R. L., Crim, R. N. & Singh, G. G. Meta-analysis reveals negative yet variable effects of ocean acidification on marine organisms. Ecol. Lett. 13, 1419–1434 (2010).PubMed 

Google Scholar 
L. Kelley, A., J. Lunden, J., 1 Ocean Acidification Research Center, College of Fisheries and Ocean Sciences, University of Alaska, Fairbanks, Fairbanks, AK, 99775, USA, & 2 Haverford College, Haverford, PA, 19041, USA. Meta-analysis identifies metabolic sensitivities to ocean acidification. AIMS Environ. Sci. 4, 709–729 (2017).Stumpp, M. et al. Acidified seawater impacts sea urchin larvae pH regulatory systems relevant for calcification. Proc. Natl. Acad. Sci. U. S. A. 109, 18192–18197 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Stumpp, M. et al. Digestion in sea urchin larvae impaired under ocean acidification. Nat. Clim. Change 3, 1044–1049 (2013).ADS 
CAS 

Google Scholar 
Runcie, D. E. et al. Genomic characterization of the evolutionary potential of the sea urchin Strongylocentrotus droebachiensis facing ocean acidification. Genome Biol. Evol. 8, 272 (2017).
Google Scholar 
Sewell, M. Utilization of lipids during early development of the sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 304, 133–142 (2005).ADS 
CAS 

Google Scholar 
Lucas, M. I., Walker, G., Holland, D. L. & Crisp, D. J. An energy budget for the free-swimming and metamorphosing larvae of Balanus balanoides (Crustacea: Cirripedia). Mar. Biol. 55, 221–229 (1979).
Google Scholar 
Shilling, F. M., Hoegh-Guldberg, O. & Manahan, D. T. Sources of energy for increased metabolic demand during metamorphosis of the abalone Haliotis rufescens (Mollusca). Biol. Bull. 191, 402–412 (1996).CAS 
PubMed 

Google Scholar 
Meidel, S. K. & Scheibling, R. E. Effects of food type and ration on reproductive maturation and growth of the sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 134, 155–166 (1999).
Google Scholar 
Pearce, C. M. & Scheibling, R. E. Induction of metamorphosis of larvae of the green sea urchin, Strongylocentrotus droebachiensis by coralline red algae. Biol. Bull. 179, 304–311 (1990).CAS 
PubMed 

Google Scholar 
Gosselin, P. & Jangoux, M. From competent larva to exotrophic juvenile: a morphofunctional study of the perimetamorphic period of Paracentrotus lividus (Echinodermata, Echinoida). Zoomorphology 118, 31–43 (1998).
Google Scholar 
Hinegardner, R. T. Growth and development of the laboratory cultured sea urchin. Biol. Bull. 137, 465–475 (1969).CAS 
PubMed 

Google Scholar 
Strathmann, R. R. Length of pelagic period in echinoderms with feeding larvae from the Northeast Pacific. J. Exp. Biol. Ecol. 34, 23–27 (1978).
Google Scholar 
Byrne, M. et al. Unshelled abalone and corrupted urchins: Development of marine calcifiers in a changing ocean. Proc. Biol. Sci. 278, 2376–2383 (2011).PubMed 

Google Scholar 
Dupont, S., Dorey, N., Stumpp, M., Melzner, F. & Thorndyke, M. Long-term and trans-life-cycle effects of exposure to ocean acidification in the green sea urchin Strongylocentrotus droebachiensis. Mar. Biol. 160, 1835–1843 (2013).CAS 

Google Scholar 
Uthicke, S. et al. Impacts of ocean acidification on early life-history stages and settlement of the coral-eating sea star Acanthaster planci. PLoS ONE 8, e82938 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
Dupont, S., Lundve, B. & Thorndyke, M. Near future ocean acidification increases growth rate of the lecithotrophic larvae and juveniles of the sea star Crossaster papposus. J. Exp. Zool. Mol. Dev. Evol. 314, 382–389 (2010).
Google Scholar 
Lim, Y.-K., Dang, X. & Thiyagarajan, V. Transgenerational responses to seawater pH in the edible oyster, with implications for the mariculture of the species under future ocean acidification. Sci. Total Environ. 782, 146704 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Hettinger, A. et al. Persistent carry-over effects of planktonic exposure to ocean acidification in the Olympia oyster. Ecology 93, 2758–2768 (2012).PubMed 

Google Scholar 
Hettinger, A. et al. Larval carry-over effects from ocean acidification persist in the natural environment. Glob. Change Biol. https://doi.org/10.1111/gcb.12307 (2013).Article 

Google Scholar 
Albright, R. & Langdon, C. Ocean acidification impacts multiple early life history processes of the Caribbean coral Porites astreoides. Glob. Change Biol. 17, 2478–2487 (2011).ADS 

Google Scholar 
Yuan, X. et al. Elevated CO2 delays the early development of scleractinian coral Acropora gemmifera. Sci. Rep. 8, 2787 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Maboloc, E. A. & Chan, K. Y. K. Parental whole life cycle exposure modulates progeny responses to ocean acidification in slipper limpets. Glob. Change Biol. 2, 15647. https://doi.org/10.1111/gcb.15647 (2021).Article 

Google Scholar 
Mos, B., Byrne, M. & Dworjanyn, S. A. Effects of low and high pH on sea urchin settlement, implications for the use of alkali to counter the impacts of acidification. Aquaculture 528, 735618 (2020).CAS 

Google Scholar 
Harianto, J., Aldridge, J., Torres Gabarda, S. A., Grainger, R. J. & Byrne, M. Impacts of acclimation in warm-low pH conditions on the physiology of the sea urchin Heliocidaris erythrogramma and carryover effects for juvenile offspring. Front. Mar. Sci. 7, 588938 (2021).
Google Scholar 
Houlihan, E. P., Espinel-Velasco, N., Cornwall, C. E., Pilditch, C. A. & Lamare, M. D. Diffusive boundary layers and ocean acidification: Implications for sea urchin settlement and growth. Front. Mar. Sci. 7, 577562 (2020).
Google Scholar 
Norderhaug, K. M. & Christie, H. C. Sea urchin grazing and kelp re-vegetation in the NE Atlantic. Mar. Biol. Res. 5, 515–528 (2009).
Google Scholar 
Dickson, A., Sabine, C. L. & Christian, J. R. Guide to best practices for ocean CO2 measurements. (PICES Special Publication 3;191 pp, 2007).Lavigne, H. & Gattuso, J.-P. seacarb: seawater carbonate chemistry with R. R package version 2.4. http://CRAN.R-project.org/package=seacarb. (2011).R Core Team. R: A language and environment for statistical computing. R: A language and environment for statistical computing (2017).Guillard, R. R. L. & Ryther, J. H. Studies of marine planktonic diatoms. I. Cyclotella nana Hustedt and Detonula confervacea (Cleve) Gran. Can. J. Microbiol. 8, 229–239 (1962).CAS 
PubMed 

Google Scholar 
Stumpp, M., Wren, J., Melzner, F., Thorndyke, M. & Dupont, S. CO2 induced seawater acidification impacts sea urchin larval development I: Elevated metabolic rates decrease scope for growth and induce developmental delay. Comp. Biochem. Physiol. Mol. Integr. Physiol. 160, 331–340 (2011).CAS 

Google Scholar 
His, E., Heyvang, I., Geffard, O. & De Montaudouin, X. A comparison between oyster (Crassostrea gigas) and sea urchin (Paracentrotus lividus) larval bioassays for toxicological studies. Water Res. 33, 1706–1718 (1999).CAS 

Google Scholar 
U. S. National Institutes of Health, Bethesda, Maryland, U. ImageJ, Rasband, W.S., http://imagej.nih.gov/ij/.Smith, M. M., Cruz Smith, L., Cameron, R. A. & Urry, L. The larval stages of the sea urchin, Strongylocentrotus purpuratus. J. Morphol. 269, 713–733 (2008).PubMed 

Google Scholar 
Kahm, M., Hasenbrink, G., Lichtenberg-Frate, H., Ludwig, J. & Kschischo, M. grofit: Fitting Biological Growth Curves with R. J. Stat. Softw., 33(7), 1–21. URL http://www.jstatsoft.org/v33/i07/. (2010).Pinheiro, J., Bates, D., & R-core. Package ‘nlme’: Linear and Nonlinear Mixed Effects Models. Cran-R (2018).Pan, T.-C.F., Applebaum, S. L. & Manahan, D. T. Experimental ocean acidification alters the allocation of metabolic energy. Proc. Natl. Acad. Sci. U. S. A. 112, 4696–4701 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jager, T., Ravagnan, E. & Dupont, S. Near-future ocean acidification impacts maintenance costs in sea-urchin larvae: Identification of stress factors and tipping points using a DEB modelling approach. J. Exp. Mar. Biol. Ecol. 474, 11–17 (2016).
Google Scholar 
Hoegh-Guldberg, O. & Emlet, R. B. Energy use during the development of a lecithotrophic and a planktotrophic echinoid. Biol. Bull. 192, 27–40 (1997).CAS 
PubMed 

Google Scholar 
Vaïtilingon, D. et al. Effects of delayed metamorphosis and food rations on the perimetamorphic events in the echinoid Paracentrotus lividus (Lamarck, 1816) (Echinodermata). J. Exp. Mar. Biol. Ecol. 262, 41–60 (2001).
Google Scholar 
García, E., Clemente, S. & Hernández, J. C. Ocean warming ameliorates the negative effects of ocean acidification on Paracentrotus lividus larval development and settlement. Mar. Environ. Res. 110, 61–68 (2015).PubMed 

Google Scholar 
Wangensteen, O. S., Dupont, S., Casties, I., Turon, X. & Palacín, C. Some like it hot: Temperature and pH modulate larval development and settlement of the sea urchin Arbacia lixula. J. Exp. Mar. Biol. Ecol. 449, 304–311 (2013).
Google Scholar 
García, E., Clemente, S. & Hernández, J. C. Effects of natural current pH variability on the sea urchin Paracentrotus lividus larvae development and settlement. Mar. Environ. Res. 139, 11–18 (2018).PubMed 

Google Scholar 
Marshall, D. J. & Keough, M. J. Variation in the dispersal potential of non-feeding invertebrate larvae: The desperate larva hypothesis and larval size. Mar. Ecol. Prog. Ser. 255, 145–153 (2003).ADS 

Google Scholar 
Huggett, M. J., Williamson, J. E., de Nys, R., Kjelleberg, S. & Steinberg, P. D. Larval settlement of the common Australian sea urchin Heliocidaris erythrogramma in response to bacteria from the surface of coralline algae. Oecologia 149, 604–619 (2006).ADS 
PubMed 

Google Scholar 
Espinel-Velasco, N., Agüera, A. & Lamare, M. Sea urchin larvae show resilience to ocean acidification at the time of settlement and metamorphosis. Mar. Environ. Res. 159, 104977 (2020).CAS 
PubMed 

Google Scholar 
Lamare, M. & Barker, M. Settlement and recruitment of the New Zealand sea urchin Evechinus chloroticus. Mar. Ecol. Prog. Ser. 218, 153–166 (2001).ADS 

Google Scholar 
Martin, S. et al. Early development and molecular plasticity in the Mediterranean sea urchin Paracentrotus lividus exposed to CO2-driven acidification. J. Exp. Biol. 214, 1357–1368 (2011).CAS 
PubMed 

Google Scholar 
Vargas, C. A. et al. Species-specific responses to ocean acidification should account for local adaptation and adaptive plasticity. Nat. Ecol. Evol. 1, 0084 (2017).
Google Scholar 
Espinel-Velasco, N. et al. Effects of ocean acidification on the settlement and metamorphosis of marine invertebrate and fish larvae: a review. Mar. Ecol. Prog. Ser. 606, 237–257 (2018).ADS 

Google Scholar 
Briffa, M., de la Haye, K. & Munday, P. L. High CO2 and marine animal behaviour: potential mechanisms and ecological consequences. Mar. Pollut. Bull. 64, 1519–1528 (2012).CAS 
PubMed 

Google Scholar 
Gaylord, B. et al. Ocean acidification through the lens of ecological theory. Ecology 96, 3–15 (2015).PubMed 

Google Scholar  More

Flores, C. O. et al. Proc. Natl Acad. Sci. USA 108, 288–297 (2011).Article 

Google Scholar 
Carlson, M. C. G. et al. Nat. Microbiol. https://doi.org/10.1038/s41564-022-01088-x (2022).Article 

Google Scholar 
Flombaum, P. et al. Proc. Natl Acad. Sci. USA 110, 9824–9829 (2013).CAS 
Article 

Google Scholar 
Coleman, M. L. & Chisholm, S. W. Trends Microbiol. 15, 398–407 (2007).CAS 
Article 

Google Scholar 
Johnson, Z. I. et al. Science 311, 1737–1740 (2006).CAS 
Article 

Google Scholar 
Martiny, A. C. et al. PLoS ONE 11, e0168291 (2016).Article 

Google Scholar 
Wilhelm, S. W. & Suttle, C. A. Bioscience 49, 781–788 (1999).Article 

Google Scholar 
Follett, C. L. et al. Proc. Natl Acad. Sci. USA 119, e2110993118 (2022).CAS 
Article 

Google Scholar 
Mojica, K. D. A. et al. ISME J. 10, 500–513 (2016).CAS 
Article 

Google Scholar 
Mruwat, N. et al. ISME J. 15, 41–54 (2021).CAS 
Article 

Google Scholar  More

Ebola datasetThe 2018–2020 DRC EVD outbreak lasted over 24 months and spread over 3 distinct spatial and temporal waves. Between the emergency declaration of the EVD outbreak in northern DRC on August 1, 2018 and the outbreak’s official end on June 25, 2020, the DRC Ministry of Health has reported a total of 3481 cases (including confirmed and probable), 1162 recoveries, and 2299 deaths16 in the provinces of Northern Kivu, Southern Kivu, and Ituri. The dataset considered here is a large subset of the entire EVD database compiled by the University of Kinshasa School of Public Health, which comprises 3117 total case records (confirmed and probable) recorded between May 3, 2018, and September 12, 2019. The data included partially de-identified but still detailed patient information, such as each person’s location, date of symptom onset and hospitalization, as well as discharge due to recovery or death. These individual records came from the Ebola treatment centers in 24 different health zones, spread out among the three DRC provinces of Northern Kivu, Southern Kivu, and Ituri.Of the 24 health zones, 77.1% of all cases were from only 6: Beni, Butembo, Katwa, Kalunguta, Mabalako, and Mandima. Only 9.7% of cases were under the age of 18. There is also a slightly larger proportion of females contracting the disease, comprising 57.0% of the cases. Approximately 5% of the cases were health care workers. About one-third of the EVD fatalities were not identified until patient’s death and thus not effectively isolated from the time of infection. Although over 170,000 contacts of confirmed and probable Ebola cases had been monitored across all affected health zones for 21 days after their last known exposure by the end of the epidemic, some of the contact tracing was incomplete due to insecurity that prevented public health response teams from entering some communities. The overall case density map is presented in panel (A) of Fig. 1 with the animated version of the map presented in the online appendix in Fig. A.1. Notice that the high-density areas, particularly Butembo, Katwa, and Beni, are all spatially small health zones corresponding to cities or towns with larger populations.Figure 1DRC Ebola dataset. (A) The spatial distribution of 3481 EVD cases across the northern DRC health zones during Ebola 2018–2020 outbreak. (B) The flowchart of personal records available up to September 12, 2019 available for the current analysis. The total number of available individual disease records was 3080. Map created using open software R17 with geospatial data obtained from18.Full size imageFigure 2Daily incidence and removal rates. Daily incidence (grey bars) and removal counts (red dots) during DRC Ebola 2018–2020 outbreak between August 15, 2018 and September 12, 2020 along with their respective trendlines (loess smoothers). The blue trendline above the plot represents daily effective reproduction number (mathcal{R}_t) defined as the ratio of daily number of new infections to new removals. The vertical lines indicate cut-off dates for data collection in each wave as listed in Table 1.Full size imageTable 1 Observed cases by EVD wave.Full size tableCase alerts and definitionsSince early August, 2018, the DRC Ministry of Health has been collaborating with several international partners to support and enhance EVD response activities through its emergency operations center in Goma. To the extent possible given regional security considerations19, the response teams were deployed to interview patients and their suspected contacts using a standardized case investigation form classifying cases as suspected, probable, or confirmed. A suspected case (whether surviving or not) was defined as one with the acute onset of fever (over 100(^{circ })F) and at least three Ebola-compatible clinical signs or symptoms (headache, vomiting, anorexia, diarrhea, lethargy, stomach pain, muscle or joint aches, difficulty swallowing or breathing, hiccups, unexplained bleeding, or any sudden, unexplained death) in a North Kivu, South Kivu, or Ituri resident or any person who had traveled to these provinces during this period and reported the signs or symptoms defined above. A patient who met the suspected case definition and died but from whom no specimens were available was considered a probable case. A confirmed Ebola case was defined as a suspected case with at least one positive test for Ebola virus using reverse transcription polymerase chain reaction (RT-PCR)20 testing. Patients with suspected Ebola were isolated and transported to an Ebola treatment center for confirmatory testing and treatment2.Onset and removalIn our analysis of the DRC dataset, we focused on dates of symptom onset and removal, with removal defined as either a death/recovery at home or transfer to an Ebola treatment center (ETC). It was assumed that, once in the treatment center, the probability of further infection spread by an isolated individual was very small due to the strict safety protocols—and later due also to vaccination of healthcare personnel and family members who were in contact with the suspected Ebola case. As summarized in panel (B) of Fig. 1, we were able to access 3117 out of 3481 individual records of confirmed and probable Ebola cases. Of these 3117 records, 37 were missing both the onset and recovery dates and were removed from further analysis. In about 30% of the remaining records, either their dates of onset or removal were missing. A detailed flow diagram summarizing the amount of missing data and data processing leading to the final dataset is presented in panel (B) of Fig. 1. The distribution of the original and the partially imputed records across the three waves of infection is provided for further reference in Table 1.Spatial and temporal patternsThroughout the pandemic, the incidence rates exhibited strong spatial and temporal patterns that can be summarized as three distinct waves of infections with approximate boundaries marked by vertical lines in Fig. 1. The distribution of weekly reported cases across the most affected health zones listed in Table 1 is provided in the bar plot and in the corresponding animation in the appendix (see Figure A.1). As seen from the bar chart and the animated plot, the epidemic was initially driven largely by infections in the health zones of Beni, Mandima and Mabalako. After several months, the incidence of new cases in these zones subsided, but the epidemic moved south to the health zones of Katwa and Butembo, where the majority of new infections was registered between weeks 22 to 45 of the epidemic (see Panel (A) in Figure A.1 in the online Appendix). In the final spatial shift, around week 49, the epidemic returned to the health zones of Beni, Mandima, and Mabalako, where it was mostly extinguished around week 60 (September 2019). Isolated Ebola incidences occurred sporadically across northern DRC until end of the outbreak was officially declared in June 2020.The empirical patterns of incidence and removal for EVD cases are summarized in Fig. 2 with the bar and the dot plots representing the daily numbers of new infections and removals, respectively. As seen from the plot, these daily counts closely follow a three-wave temporal pattern in Table 1. This is further evident from the black and red trendlines representing the loess smoothers (see21). The daily ratio of new cases and removals may be interpreted as a crude estimate of the effective reproduction number (mathcal{R}_t) defined more formally in (2) in Model for Data Analysis below. In particular, the blue trendline for (mathcal{R}_t) indicates that towards the end of the observed time period, the number of removals outpaced the number of new infections ((mathcal{R}_t 0) and (r_t = 0) where (beta > 0) is the rate of infection, (gamma > 0) is the rate of recovery and (rho > 0) is the initial amount of infection. In particular, the model implies the existence of the basic reproduction number (mathcal{R}_0) (R-naught), which determines the average speed of disease spread11 and is given by the formula$$mathcal{R}_0=beta /gamma .$$If (mathcal{R}_0 > 1), the proportion of infected initially rises and then subsides, with the final proposition of surviving susceptibles given by (s_infty = 1 – tau > 0) where (tau) is know as the epidemic’s final size. In typical statistical analysis, an estimate of (mathcal{R}_0) is obtained by separately estimating the parameters (beta) and (gamma). Another important quantity related to (1) is the effective reproduction number, which is typically defined as$$begin{aligned} mathcal{R}_t= mathcal{R}_0 s_t. end{aligned}$$
(2)
Although equation (1) is typically considered in the context of an average behavior of a large population, for our purposes we interpret it as defining the individual histories of infection and recovery, according to the idea of the dynamic survival analysis (DSA) discussed recently in10 and24 and also briefly summarized in the Appendix. With the DSA approach, we interpret equation (1) as the so-called stochastic master equation25 describing the change in probability of a randomly selected individual being at time t either susceptible, infected, or removed. These respective probabilities are represented by the scaled proportions (s_t/(1+rho )), (iota _t/(1+rho )), and (r_t/(1+rho )) and evolve according to (1). As outlined in10, the DSA-based interpretation of the classical SIR equations has a number of advantages that make it particularly convenient for analyzing epidemic data consisting of individual histories of infection onsets and removals, which is exactly the type of data available in the DRC Ebola dataset. The fact that the model is individual-based implies also that we can vary the parameters (theta =(beta ,gamma ,rho )) to account for individual covariates and changes in the parameter values over time, as different waves of infection sweep through the population. Finally, for the purpose of our analysis, it is also important to note that the DSA model does not require any knowledge of the size of the susceptible population subjected to the epidemic pressure. For the DRC dataset, that assumption would be difficult to justify due to spatial and temporal heterogeneity of the epidemic and the frequent movements of local populations driven by political conflicts and insecurity. Another element complicating the determination of the size of susceptible population was the ring vaccination campaign that has been conducted since 2019 wherever possible in the northern DRC during periods of relative stability, despite local mistrust and supply issues. This campaign ultimately resulted in over 250,000 vaccinations.Note that, because (s_0 = 1), the values of (mathcal{R}_0) and (mathcal{R}_t) coincide for (t = 0). Moreover, (s_t = exp left( -mathcal{R}_0 int _0^t r_u mathrm {d}u right)) is a decreasing function of time and therefore, so is (mathcal{R}_t). However, in practice, this implication is problematic. Rewriting (mathcal{R}_t = – {dot{s}}_t/ {dot{r}}_t) suggests that a crude but sensible way to estimate (mathcal{R}_t) empirically is to take the ratio of daily number of new infections to new removals. The empirical (mathcal{R}_t) thus estimated will not be necessarily monotonically decreasing. In the light of possibly changing parameters and the effective population size, we have adopted this approach to estimating the daily effective reproduction number (mathcal{R}_t) in Fig. 2.Parameter estimationWe assume that, for each of the three waves of the epidemic, we have a separate and independent set of parameters (theta) and that, in each wave, we observe (n_T) histories (records) of infection. The i-th individual history may be represented either by the times of disease onset and removal ((t_i,T_i)) or by (t_i) or (T_i) times alone ((t_i,circ )) or ((circ ,T_i)) ((circ) denoting missing value). We assume that among the available (n_T) histories we have n complete records ((t_i,T_i)), (n_1) incomplete ones ((t_i,circ )) and (n_2) incomplete ones ((circ ,T_i )). The wave-specific DSA likelihood function for n complete data records is (see Appendix)$$begin{aligned} begin{aligned} {mathcal {L}}_C(theta vert t_1ldots ,t_n,T_1,ldots ,T_n,T)=(s_T-1)^{-n}prod _{i=1}^n {dot{s}}_{t_i}gamma ^{w_i}e^{-gamma (T_i wedge T -t_i)} end{aligned} end{aligned}$$
(3)
where T is the available time horizon and (w_i) is the binary variable indicating whether (T_i) is right-censored (that is, (T_iwedge T =T)) in which case (w_i = 0) and otherwise (w_i = 1). For the remaining (n_1+n_2) records that are partially incomplete, the wave-specific DSA likelihood function is$$begin{aligned} begin{aligned} {mathcal {L}}_I(theta vert t_1ldots ,t_{n_1},T_1,ldots ,T_{n_2},T)= (s_T-1)^{-(n_1+n_2)} gamma ^{n_2}prod _{i=1}^{n_1} {dot{s}}_{t_i} prod _{i=1}^{n_2} (rho e^{-gamma T_i }-iota _{T_i}) end{aligned} end{aligned}$$
(4)
where we assume that (T_i1). Given the wave-specific time horizons (T’s), the set of parameters for each epidemic wave was estimated independently using 2 independent chains of 3000 iterations, with a burn-in period of 1000 iterations. The chains’ convergence assessed using Rubin’s R statistic28. The analysis resulted in approximate samples from the posterior distribution of (theta) for each of the three waves of the epidemic (see e.g., Fig. 4).Ethics statement on human subjects and methodsThe research was conducted in accordance with the relevant guidelines and regulations of the US law and OSU Institutional Review Board. The research activities involving human subjects discussed in the paper meet the US federal exemption criteria under 45 CFR 46 and 21 CFR 56. More