Philippa Kaur

Globally, agriculture represents a substantial contributor to net greenhouse gas (GHG) emissions (c. 25%)1, and accounts for at least 10% of all GHG emissions in the United States2. To address the current climate emergency, agriculture remains a key player, with substantial potential to contribute to the solution. Reduced tillage as part of a ‘conservation agriculture’ approach is considered an important way of achieving this and is gaining popularity globally. Leaving the soil uncultivated, also referred to as zero or no tillage (that is, not ploughing), has been shown to offer considerable benefits for the ‘health’ of soil, including improved soil structure, a thriving soil faunal community (for example, earthworms) and, potentially, sequestration of carbon3. It has recently been shown, for temperate arable systems, that there is potential for a substantial (up to 30%) reduction in GHG emissions by simply moving to direct drilling, as the resulting changes in the soil structure help reduce GHG emissions4. Minimizing tillage also dramatically cuts the diesel consumption linked to crop production. However, there are negatives associated with this reductionist approach, most notably the proliferation of weed plant species that have traditionally been controlled via the implementation of tillage. More

Minasny, B. et al. Soil carbon 4 per mille. Geoderma 292, 59–86 (2017).ADS 
Article 

Google Scholar 
Lal, R. Soil carbon sequestration impacts on global climate change and food security. Science 304, 1623–1627 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Lehmann, J., Bossio, D. A., Kögel-Knabner, I. & Rillig, M. C. The concept and future prospects of soil health. Nat. Rev. Earth Environ. 1, 544–553 (2020).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534 (2020).ADS 
CAS 
Article 

Google Scholar 
Lavallee, J. M., Soong, J. L. & Cotrufo, M. F. Conceptualizing soil organic matter into particulate and mineral-associated forms to address global change in the 21st century. Glob. Change Biol. 26, 261–273 (2020).ADS 
Article 

Google Scholar 
Kravchenko, A. N. et al. Microbial spatial footprint as a driver of soil carbon stabilization. Nat. Commun. 10, 3121 (2019).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Witzgall, K. et al. Particulate organic matter as a functional soil component for persistent soil organic carbon. Nat. Commun. 12, 4115 (2021).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Dungait, J. A. J., Hopkins, D. W., Gregory, A. S. & Whitmore, A. P. Soil organic matter turnover is governed by accessibility not recalcitrance. Glob. Change Biol. 18, 1781–1796 (2012).ADS 
Article 

Google Scholar 
Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60 (2015).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Keiluweit, M., Nico, P. S., Kleber, M. & Fendorf, S. Are oxygen limitations under recognized regulators of organic carbon turnover in upland soils? Biogeochemistry 127, 157–171 (2016).CAS 
Article 

Google Scholar 
Rohe, L. et al. Denitrification in soil as a function of oxygen availability at the microscale. Biogeosciences 18, 1185–1201 (2021).ADS 
CAS 
Article 

Google Scholar 
Hall, S. J. & Silver, W. L. Reducing conditions, reactive metals, and their interactions can explain spatial patterns of surface soil carbon in a humid tropical forest. Biogeochemistry 125, 149–165 (2015).CAS 
Article 

Google Scholar 
Hagedorn, F., Bruderhofer, N., Ferrari, A. & Niklaus, P. A. Tracking litter-derived dissolved organic matter along a soil chronosequence using 14C imaging: Biodegradation, physico-chemical retention or preferential flow? Soil Biol. Biochem. 88, 333–343 (2015).CAS 
Article 

Google Scholar 
Védère, C., Vieublé Gonod, L., Pouteau, V., Girardin, C. & Chenu, C. Spatial and temporal evolution of detritusphere hotspots at different soil moistures. Soil Biol. Biochem. 150, 107975 (2020).Article 
CAS 

Google Scholar 
Silver, W. L., Lugo, A. E. & Keller, M. Soil oxygen availability and biogeochemistry along rainfall and topographic gradients in upland wet tropical forest soils. Biogeochemistry 44, 301–328 (1999).
Google Scholar 
Schuur, E. A. G., Chadwick, O. A. & Matson, P. A. Carbon cycling and soil carbon storage in mesic to wet hawaiian montane forests. Ecology 82, 3182–3196 (2001).Article 

Google Scholar 
Tiemeyer, B. et al. High emissions of greenhouse gases from grasslands on peat and other organic soils. Glob. Change Biol. 22, 4134–4149 (2016).ADS 
Article 

Google Scholar 
Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands. Biogeosciences 9, 1053–1071 (2012).ADS 
CAS 
Article 

Google Scholar 
Cleveland, C. C., Wieder, W. R., Reed, S. C. & Townsend, A. R. Experimental drought in a tropical rain forest increases soil carbon dioxide losses to the atmosphere. Ecology 91, 2313–2323 (2010).PubMed 
Article 

Google Scholar 
Moyano, F. E., Manzoni, S. & Chenu, C. Responses of soil heterotrophic respiration to moisture availability: an exploration of processes and models. Soil Biol. Biochem. 59, 72–85 (2013).CAS 
Article 

Google Scholar 
Franzluebbers, A. J. Microbial activity in response to water-filled pore space of variably eroded southern Piedmont soils. Appl. Soil Ecol. 11, 91–101 (1999).Article 

Google Scholar 
Thomsen, I. K., Schjønning, P., Jensen, B., Kristensen, K. & Christensen, B. T. Turnover of organic matter in differently textured soils: II. Microbial activity as influenced by soil water regimes. Geoderma 89, 199–218 (1999).ADS 
Article 

Google Scholar 
Nunan, N., Leloup, J., Ruamps, L. S., Pouteau, V. & Chenu, C. Effects of habitat constraints on soil microbial community function. Sci. Rep. 7, 4280 (2017).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Ruamps, L. S., Nunan, N. & Chenu, C. Microbial biogeography at the soil pore scale. Soil Biol. Biochem. 43, 280–286 (2011).CAS 
Article 

Google Scholar 
Strong, D. T., Wever, H. D., Merckx, R. & Recous, S. Spatial location of carbon decomposition in the soil pore system. Eur. J. Soil Sci. 55, 739–750 (2004).Article 

Google Scholar 
Vogel, H.-J. et al. A holistic perspective on soil architecture is needed as a key to soil functions. Eur. J. Soil Sci. 73, e13152 (2022).Article 

Google Scholar 
Lehmann, J. et al. Spatial complexity of soil organic matter forms at nanometre scales. Nat. Geosci. 1, 238–242 (2008).ADS 
CAS 
Article 

Google Scholar 
Steffens, M. et al. Identification of distinct functional microstructural domains controlling C storage in soil. Environ. Sci. Technol. 51, 12182–12189 (2017).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Elyeznasni, N. et al. Exploration of soil micromorphology to identify coarse-sized OM assemblages in X-ray CT images of undisturbed cultivated soil cores. Geoderma 179-180, 38–45 (2012).ADS 
Article 

Google Scholar 
Hayes, T. L., Lindgren, F. T. & Gofman, J. W. A quantitative determination of the Osmium tetroxide-lipoprotein interaction. J. Cell Biol. 19, 251–255 (1963).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
Belazi, D., Solé-Domènech, S., Johansson, B., Schalling, M. & Sjövall, P. Chemical analysis of osmium tetroxide staining in adipose tissue using imaging ToF-SIMS. Histochem. Cell Biol. 132, 105–115 (2009).CAS 
PubMed 
Article 

Google Scholar 
Schulz M., et al. Structured heterogeneity in a marine terrace chronosequence: upland mottling. Vadose Zone J. 15, vzj2015.07.0102 (2016).Fimmen et al. Fe–C redox cycling: a hypothetical biogeochemical mechanism that drives crustal weathering in upland soils. Biogeochemistry 87, 127–141 (2008).CAS 
Article 

Google Scholar 
Zheng, H., Kim, K., Kravchenko, A., Rivers, M. & Guber, A. Testing Os staining approach for visualizing soil organic matter patterns in intact samples via X-ray dual-energy tomography scanning. Environ. Sci. Technol. 54, 8980–8989 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Périé, C. & Ouimet, R. Organic carbon, organic matter and bulk density relationships in boreal forest soils. Can. J. Soil Sci. 88, 315–325 (2008).Article 

Google Scholar 
Rawls, W. J., Pachepsky, Y. A., Ritchie, J. C., Sobecki, T. M. & Bloodworth, H. Effect of soil organic carbon on soil water retention. Geoderma 116, 61–76 (2003).ADS 
CAS 
Article 

Google Scholar 
Quigley M. Y., Rivers M. L. & Kravchenko A. N. Patterns and sources of spatial heterogeneity in soil matrix from contrasting long term management practices. Front. Environ. Sci. 6 (2018).Arai, M. et al. An improved method to identify osmium-stained organic matter within soil aggregate structure by electron microscopy and synchrotron X-ray micro-computed tomography. Soil Tillage Res. 191, 275–281 (2019).Article 

Google Scholar 
Peth, S. et al. Localization of soil organic matter in soil aggregates using synchrotron-based X-ray microtomography. Soil Biol. Biochem. 78, 189–194 (2014).CAS 
Article 

Google Scholar 
Rawlins, B. G. et al. Three-dimensional soil organic matter distribution, accessibility and microbial respiration in macroaggregates using osmium staining and synchrotron X-ray computed tomography. Soil 2, 659–671 (2016).ADS 
CAS 
Article 

Google Scholar 
Plattner H. & Zingsheim H. P. Electron Microscopic Methods in Cellular and Molecular Biology. In: Subcellular Biochemistry (ed. Roodyn D. B.). (Plenum Press, 1983).Litman, R. B. & Barrnett, R. J. The mechanism of the fixation of tissue components by osmium tetroxide via hydrogen bonding. J. Ultrastruct. Res. 38, 63–86 (1972).CAS 
PubMed 
Article 

Google Scholar 
Vepraskas M. & Lindbo D. Redoximorphic features as related to soil hydrology and hydric soils. In: Hydropedology: Synergistic Integration of Soil Science and Hydrology (ed. Lin H.). Academic Press (2012).See C. R., et al. Hyphae move matter and microbes to mineral microsites: integrating the hyphosphere into conceptual models of soil organic matter stabilization. Glob. Change Biol. 28, 2527–2540 (2022).Vidal, A. et al. Visualizing the transfer of organic matter from decaying plant residues to soil mineral surfaces controlled by microorganisms. Soil Biol. Biochem. 160, 108347 (2021).CAS 
Article 

Google Scholar 
Hagedorn, F., Kaiser, K., Feyen, H. & Schleppi, P. Effects of redox conditions and flow processes on the mobility of dissolved organic carbon and nitrogen in a forest soil. J. Environ. Qual. 29, 288–297 (2000).CAS 
Article 

Google Scholar 
Grybos, M., Davranche, M., Gruau, G., Petitjean, P. & Pédrot, M. Increasing pH drives organic matter solubilization from wetland soils under reducing conditions. Geoderma 154, 13–19 (2009).ADS 
CAS 
Article 

Google Scholar 
Keiluweit, M., Wanzek, T., Kleber, M., Nico, P. & Fendorf, S. Anaerobic microsites have an unaccounted role in soil carbon stabilization. Nat. Commun. 8, 1771 (2017).ADS 
PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
Eusterhues, K., Rumpel, C. & Kögel-Knabner, I. Stabilization of soil organic matter isolated via oxidative degradation. Org. Geochem. 36, 1567–1575 (2005).CAS 
Article 

Google Scholar 
Torn, M. S., Trumbore, S. E., Chadwick, O. A., Vitousek, P. M. & Hendricks, D. M. Mineral control of soil organic carbon storage and turnover. Nature 389, 170–173 (1997).ADS 
CAS 
Article 

Google Scholar 
Lucas, M., Schlüter, S., Vogel, H.-J. & Vetterlein, D. Soil structure formation along an agricultural chronosequence. Geoderma 350, 61–72 (2019).ADS 
Article 

Google Scholar 
Sokol, N. W., Sanderman, J. & Bradford, M. A. Pathways of mineral-associated soil organic matter formation: Integrating the role of plant carbon source, chemistry, and point of entry. Glob. Change Biol. 25, 12–24 (2019).ADS 
Article 

Google Scholar 
Marschner, B. & Kalbitz, K. Controls of bioavailability and biodegradability of dissolved organic matter in soils. Geoderma 113, 211–235 (2003).ADS 
CAS 
Article 

Google Scholar 
Stirling, E., Smernik, R. J., Macdonald, L. M. & Cavagnaro, T. R. The effect of fire affected Pinus radiata litter and char addition on soil nitrogen cycling. Sci. Total Environ. 664, 276–282 (2019).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kravchenko, A. N. et al. Hotspots of soil N2O emission enhanced through water absorption by plant residue. Nat. Geosci. 10, 496 (2017).ADS 
CAS 
Article 

Google Scholar 
Kim, K., Guber, A., Rivers, M. & Kravchenko, A. Contribution of decomposing plant roots to N2O emissions by water absorption. Geoderma 375, 114506 (2020).ADS 
CAS 
Article 

Google Scholar 
Goebel, M. O., Bachmann, J., Reichstein, M., Janssens, I. A. & Guggenberger, G. Soil water repellency and its implications for organic matter decomposition – is there a link to extreme climatic events? Glob. Change Biol. 17, 2640–2656 (2011).ADS 
Article 

Google Scholar 
Brodowski, S., Amelung, W., Haumaier, L., Abetz, C. & Zech, W. Morphological and chemical properties of black carbon in physical soil fractions as revealed by scanning electron microscopy and energy-dispersive X-ray spectroscopy. Geoderma 128, 116–129 (2005).ADS 
CAS 
Article 

Google Scholar 
Diel, J., Vogel, H.-J. & Schlüter, S. Impact of wetting and drying cycles on soil structure dynamics. Geoderma 345, 63–71 (2019).ADS 
CAS 
Article 

Google Scholar 
Surey R., et al. Contribution of particulate and mineral-associated organic matter to potential denitrification of agricultural soils. Front. Environ. Sci. 9 (2021).Kaiser, M., Ellerbrock, R. H. & Sommer, M. Separation of coarse organic particles from bulk surface soil samples by electrostatic attraction. Soil Sci. Soc. Am. J. 73, 2118–2130 (2009).ADS 
CAS 
Article 

Google Scholar 
Atkinson, R., Posner, A. & Quirk, J. P. Adsorption of potential-determining ions at the ferric oxide-aqueous electrolyte interface. J. Phys. Chem. 71, 550–558 (1967).CAS 
Article 

Google Scholar 
Mueller, C. W. et al. Submicron scale imaging of soil organic matter dynamics using NanoSIMS – from single particles to intact aggregates. Org. Geochem. 42, 1476–1488 (2012).Article 
CAS 

Google Scholar 
Herrmann, A. M. et al. Nano-scale secondary ion mass spectrometry—a new analytical tool in biogeochemistry and soil ecology: A review article. Soil Biol. Biochem. 39, 1835–1850 (2007).CAS 
Article 

Google Scholar 
Schlüter, S., Eickhorst, T. & Mueller, C. W. Correlative imaging reveals holistic view of soil microenvironments. Environ. Sci. Technol. 53, 829–837 (2019).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Klein, S., Staring, M., Murphy, K., Viergever, M. A. & Pluim, J. P. W. elastix: a toolbox for intensity-based medical image registration. Med. Imaging, IEEE Trans. 29, 196–205 (2010).Article 

Google Scholar 
Otsu, N. A threshold selection method from gray-level histograms. Automatica 11, 23–27 (1975).
Google Scholar 
Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. methods 9, 676–682 (2012).CAS 
PubMed 
Article 

Google Scholar 
Schlüter, S., Leuther, F., Vogler, S. & Vogel, H.-J. X-ray microtomography analysis of soil structure deformation caused by centrifugation. Solid Earth 7, 129–140 (2016).ADS 
Article 

Google Scholar 
Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).CAS 
PubMed 
Article 

Google Scholar 
Schlüter, S., Sheppard, A., Brown, K. & Wildenschild, D. Image processing of multiphase images obtained via X-ray microtomography: a review. Water Resour. Res. 50, 3615–3639 (2014).ADS 
Article 

Google Scholar 
Legland, D., Arganda-Carreras, I. & Andrey, P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics 32, 3532–3534 (2016).CAS 
PubMed 

Google Scholar 
Liaw, A. & Wiener, M. Classification and regression by randomForest. R. N. 2, 18–22 (2002).
Google Scholar 
Surey, R. et al. Differences in labile soil organic matter explain potential denitrification and denitrifying communities in a long-term fertilization experiment. Appl. Soil Ecol. 153, 103630 (2020).Article 

Google Scholar 
R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing (2020). More

Data collection of flowering and fruiting phenologyMonthly reproductive phenology data recorded over 35 years (from April 1976 to September 2010) were collected from the Bulletin Fenologi Biji Benih dan Anak Benih (Bulletin of Seed and Seedling Phenology), which was deposited at the FRIM library. The bulletin reported seed and seedling availabilities and the flowering and fruiting phenology of trees at several research stations in Malaysia. The present study collected flowering and fruiting records of trees grown in FRIM arboretums located approximately 12 km northwest of Kuala Lumpur, Malaysia (latitude 3°24 ‘N, longitude 101°63 ‘E, elevation 80 m). There are both dipterocarp and non-dipterocarp arboretums in FRIM, both of which were founded in 1929. These arboretums preserve and maintain living trees for research and other purposes. Each month, three research staff members of FRIM with sufficient phenology monitoring training made observations with binoculars to record the presence of flowers and fruits on trees of each species on the forest floor from April 1976 to September 2010. The phenological status of the trees was recorded as flowering during the developmental stages from flower budding to blooming and as fruiting during the developmental stages from the occurrence of immature fruit to fruit ripening. Because only one or two individuals per species are grown at the FRIM arboretums, the flowering and fruiting phenology were monitored using these individuals. The resultant flowering and fruiting phenology data included a time series of binary data (1 for presence and 0 for absence) with a length of 417 months.The original data included 112 dipterocarp and 240 non-dipterocarp species. We excluded 17 dipterocarps and 125 non-dipterocarp species based on the following five criteria for data accuracy.

1.

Percentage of missing values is ≤50%: If the monthly flowering or fruiting phenology data of a species included a substantially large number of missing values ( >50%), the species was excluded.

2.

Stable flowering period: We considered an observation to be unreliable if the flowering period was significantly different among flowering events (if the coefficient of variation in the flowering period was larger or equal to 1.0).

3.

Flowering period is shorter than or equal to 12 months: we considered an observation to be unreliable if the flowering period was longer than 12 months because it was unlikely that the same tree would flower continuously for longer than 1 year.

4.

The flowering and fruiting frequencies were not significantly different between the first and second half of the census period: when the flowering frequency was zero for the first half of the observation period but was larger than 0.1 for the second half of the observation period, or when the flowering frequency was zero for the second half of the observation period but was larger than 0.1 for the first half of the observation period, we removed these species because data are not reliable (e.g., physiological conditions may have changed significantly). We adopted the same criteria for the fruiting phenology data.

5.

We removed overlapping species, herb species, and specimens with unknown species names.

After removing unreliable species based on the five criteria explained above, we obtained 95 dipterocarp and 115 non-dipterocarp species (Supplementary Data 1). We used these species for further analyses. It is unlikely that our final data includes trees that were replaced by young trees during the census period because newly planted seedlings do not flower over 20–30 years until they are fully grown to the reproductive stage ( >20–30 cm DBH)45.Detection of seasonality in reproductive phenologyTo compare the flowering and fruiting phenology seasonality among different families, nine families that included at least five species were used. The number of flowering or fruiting events was counted for each month from January to December during a census, and then the frequency distribution was drawn as a histogram. Similarly, we also generated a histogram for the seed dispersal month, which was calculated as the month when fruiting ended (i.e., when the binary fruiting phenology data changed from one to zero).Classification of phenological patternsTo classify the phenological patterns, we performed time-series clustering using the R package TSclust46 with the hierarchical clustering method based on the Dynamic Time Warping distance of the flowering phenology data of each species. For this analysis, time points at which there were missing values for at least one species were excluded. Because of the large number of missing values in non-Dipterocarpaceae species, we performed time-series clustering only for the Dipterocarpaceae species based on 394 time points in total. The number of phenological clusters was estimated based on AIC, as explained below.Climate dataDaily minimum, mean, and maximum temperatures and precipitation data monitored at the FRIM KEPONG (3° 14’ N, 101° 42’ E, elevation 97 m) weather station were provided by the Malaysian Meteorological Department. We used the daily minimum temperature for our analysis because there were fewer missing values compared to the numbers of missing daily mean and daily maximum temperature values. The periods in which climate data were available were from 1 March 1973 to 31 March 1996, and from 23 July 1997 to 20 April 2005. We removed periods in which there were missing values spanning longer than 5 days. When the range of missing values spanned a period shorter than 3 days, we approximated these missing values using the mean minimum temperatures recorded on the adjacent three days. Although solar radiation data were not available for our study, the use of precipitation is sufficient for model fitting because there is a significant negative correlation between solar radiation and precipitation in Southeast Asia47.Climate data generated by GCMsAs the future climate inputs, we used bias-corrected climate input data from 1 January 2050 to 31 December 2099, with a daily temporal resolution and a 0.5° spatial resolution, provided by the ISI-MIP project48; these data are based on the Coupled Model Intercomparison Project Phase 5 outputs from three GCMs: GFDL–ESM2M, IPSL–CM5A-LR, and MIROC5. To compare the flowering phenology between 1976–1996 and 2050–2099, bias-corrected GCM data from 1 May 1976 to 31 March 1996, were also used. This period (1 May 1976–31 March 1996) is consistent with the period used for model fitting. We selected daily minimum temperature and precipitation time series from the 0.5° grid cells corresponding to the study site for phenology monitoring at FRIM. To compare flowering phenology among regions, we also used the same set of data from three other regions in Southeast Asia: Trang Province in Thailand (7° 4’ N, 99° 47’ E), Lambir Hills National Park in Malaysia (4° 2’ N, 113° 50’ E), and central Kalimantan in Indonesia (0° 06’ S, 114° 0’ E). Because the study site in FRIM was not in the center of a 0.5° grid cell, we interpolated the data using four grid cells in the vicinity of the observation site. We used the weighted average according to the distance between each observation site and the center of each corresponding grid cell.Although the climate input data provided by ISI-MIP were already bias-corrected, we conducted additional bias correction at FRIM using a historical scenario for each GCM data set and the observed weather data from 1 January 1976 to 31 December 2004 based on previously presented protocol49. We did not implement any bias correction for the frequency of dry days or precipitation intensity of wet days49 because we only focused on the average precipitation.The variances in the annual fluctuation of the monthly mean precipitation were not the same between the observation data and historical GCM runs at FRIM. For all three GCMs (GFDL–ESM2M, IPSL–CM5A-LR, and MIROC5), the variances in the yearly fluctuation output by the GCMs tended to be larger than that of the observed data at the FRIM KEPONG weather station during winter and spring. On the other hand, during summer and fall, the variances output by the GCMs tended to be smaller than that of the observed data. These biases could not be corrected using the previous method49. Therefore, we conducted the following bias correction for these data:$${p}_{i,m,y}^{{{{{{rm{GCM}}}}}}* }={r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}cdot left[{F}_{Gamma }^{-1}left({F}_{Gamma }left({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}|{k}_{m,y},{theta }_{m,y}right)|{k}_{m,y}^{* },{theta }_{m,y}^{* }right)cdot {rho }_{m,y}^{{{{{{rm{GCM}}}}}}}right],$$
(1)
where ({p}_{i,m,y}^{{{{{{rm{GCM}}}}}}* }) is the bias-corrected precipitation value of the target GCM at year y, month m, and date i. In the equation, ({r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}) is the ratio of the precipitation value of the GCM relative to the monthly mean value. Then, the following equation is used:$${r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}=frac{{p}_{i,m,y}^{{{{{{rm{GCM}}}}}}}}{{bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}},$$
(2)
where ({p}_{i,m,y}^{{{{{{rm{GCM}}}}}}}) is the precipitation value (not bias-corrected) of the GCM at year (y), month (m), and date i and ({bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}) is the monthly mean precipitation value of the GCM at year (y) and month (m). In Eq. 1, ({F}_{Gamma }) represents the cumulative distribution function of a gamma distribution, ({F}_{Gamma }^{-1}) represents the inverse function of the cumulative distribution function of the gamma distribution, and ({k}_{m,y}) and ({theta }_{m,y}) are the shape parameters. In Eq. 1, ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) indicates the deviation of the monthly mean from the normal climate value of the corresponding period, and this value is calculated as follows:$${delta }_{m,y}^{{{{{{rm{GCM}}}}}}}=frac{{bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}}{{rho }_{m,y}^{{{{{{rm{GCM}}}}}}}},$$
(3)
where ({rho }_{m,y}^{{{{{{rm{GCM}}}}}}}) is the normal climate value during the target period. In this method, we defined the normal climate value as the mean of the monthly mean precipitation values over 31 years.$${rho }_{m,y}^{{{{{{rm{GCM}}}}}}}=frac{1}{31}mathop{sum }limits_{j=y-15}^{y+15}{bar{p}}_{m,j}^{{{{{{rm{GCM}}}}}}}.$$
(4)
When the mean of a gamma distribution is fixed at one, the shape parameters are represented as follows:$${k}_{m,y}=frac{1}{Vleft({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}right)},$$
(5)
$${theta }_{m,y}=frac{1}{{k}_{m,y}},$$
(6)
where (Vleft({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}right)) indicates the variance in ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) at month (m) over 31 years.In this method, we assumed that the ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) value follows a gamma distribution and that the ratio of the variance of ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) to the variance of ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) is maintained even in the future scenario. Here, ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) represents the deviation of the monthly mean in the observation data from the normal climate value.$${delta }_{m,y}^{{{{{{rm{obs}}}}}}}=frac{{bar{p}}_{m,y}^{{{{{{rm{obs}}}}}}}}{{rho }_{m}^{{{{{{rm{obs}}}}}}}},$$
(7)
$${rho }_{m}^{{{{{{rm{obs}}}}}}}=frac{1}{28}mathop{sum }limits_{j=1976}^{2004}{bar{p}}_{m,y}^{{{{{{rm{obs}}}}}}}.$$
(8)
In the above equations, ({bar{p}}_{m,y}^{{{{{{rm{obs}}}}}}}) indicates the monthly mean precipitation value in the observed data. As mentioned above, because we assume that the ratio of the variance in ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) to the variance in ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) is maintained, ({k}_{m,y}^{* }) and ({theta }_{m,y}^{* }) are calculated as follows:$${k}_{m,y}^{* }=frac{{k}_{m,y}}{alpha },$$
(9)
$${theta }_{m,y}^{* }=frac{1}{{k}_{m,y}^{* }},$$
(10)
where$$alpha =frac{Vleft({delta }_{m,y}^{{{{{{{rm{GCM}}}}}}}^{{{{{{rm{h}}}}}}}}right)}{Vleft({delta }_{m,y}^{{{{{{rm{obs}}}}}}}right)}.$$
(11)
In Eq. 11, ({delta }_{m,y}^{{{{{{{rm{GCM}}}}}}}^{{{{{{rm{h}}}}}}}}) is the deviation of the monthly mean of the historical GCM precipitation data from the normal climate value. Here, we defined the normal climate value as the average monthly mean during 1976–2004.The method proposed here is an original bias correction method, but the above equations are easily derived if we assume that the ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) value follows a gamma distribution and that the ratio of the variance in ({delta }_{m,y}^{{{{{{rm{GCM}}}}}}}) to the variance in ({delta }_{m,y}^{{{{{{rm{obs}}}}}}}) is maintained even in the future scenario. Notably, because we combined this method with the bias correction method described previously49, Eq. 2 should be expressed as follows:$${r}_{i,m,y}^{{{{{{rm{GCM}}}}}}}=frac{{widetilde{p}}_{l,m,y}^{{{{{{rm{GCM}}}}}}}}{{bar{p}}_{m,y}^{{{{{{rm{GCM}}}}}}}},$$
(12)
where ({widetilde{p}}_{l,m,y}^{{{{{{rm{GCM}}}}}}}) is the precipitation data that are bias-corrected using the method described previously49. Bias-corrected data were compared with the data without bias correction (Supplementary Figs. 8–11).Statistical analyses and reproducibilityWe adopted previously presented models in which environmental triggers for floral induction accumulate for n1 days prior to the onset of floral induction21 (Supplementary Fig. 2). Flowers then develop for n2 days before opening (Supplementary Fig. 2). The model assumption of the time lag between floral induction and anthesis, which is denoted as n2, was validated by a previous finding in which the expression peaks of flowering-time genes, which are used as molecular markers of floral induction, were shown to occur at least one month before anthesis in Shorea curtisii19. S. curtissi is included in our data set. The CU at time t, ({{{{{rm{CU}}}}}}left(t|{theta }^{C}right)), is calculated as follows:$${{{{{rm{CU}}}}}}left(t|{theta }^{C}right)=mathop{sum }limits_{n={n}_{2}}^{{n}_{2}+{n}_{1}-1}{{{{{rm{max }}}}}}{bar{C}-xleft(t-nright),0},$$
(13)
where ({theta }^{C}=left{{n}_{1},{n}_{2},bar{C}right}) is the set of parameters and x(t) is the temperature at time t. Here, (bar{C}) indicates the threshold temperature. The term max{x1, x2} is a function that returns a larger value for the two arguments. Similarly, given ({theta }^{D}={{n}_{1},{n}_{2},bar{D}},) the DU at time t, ({{{{{rm{DU}}}}}}left(t|{theta }^{D}right)), is defined as the difference between the mean daily accumulation of rainfall over n1 days and a threshold rainfall level ((bar{D})):$${{{{{rm{DU}}}}}}left(t|{theta }^{D}right)={{{{{rm{max }}}}}}left{bar{D}-mathop{sum }limits_{n={n}_{2}}^{{n}_{2}+{n}_{1}-1}yleft(t-nright)/{n}_{1},0right},$$
(14)
where y(t) is the rainfall value at time t. The term max{x1, x2} is defined similarly as in Eq. 13.Logistic regression was performed using only the DU and using the product of CU and DU (CU × DU) as the explanatory variables and using the presence or absence of a first flowering event as the dependent variable for each phenological cluster. Because the number of phenological clusters is unknown, we performed forward selection on the cluster number based on the AIC. Let m be the number of phenological clusters based on the dendrogram drawn from the time-series clustering explained above (Supplementary Fig. 5). Given m phenological clusters, let ({G}_{k}^{m}) be the kth set of clusters in which the DU model is adopted for model fitting. Here, ({G}_{k}^{m}) indicates the set of cluster IDs, and k ranges from 0 to m(m+1)/2. For example, when m = 2 (i.e., there are two clusters, clusters 1 and 2), there are four cluster sets, calculated as follows:$${G}_{0}^{m=2}={},{G}_{1}^{m=2}={1},{G}_{2}^{m=2}={2},{G}_{3}^{m=2}={1,2},$$
(15)
where the element in the bracket indicates the ID of the cluster in which the DU model is adopted for model fitting. When k = 0, the DU model is not used; instead, the CU × DU model is adopted for model fitting for both clusters 1 and 2. Let i be the ith element of the vector E, which is defined as follows:$${{{{{bf{E}}}}}}={{t}_{1}^{1},,{t}_{2}^{1},…,,,{t}_{n}^{1},,…,,,{t}_{1}^{m},,{t}_{2}^{m},…,,{t}_{n}^{m}},$$
(16)
where n is the length of the time-series data for each cluster. Notably, n = 223 is the same for all species and clusters. The term ({t}_{1}^{m}) in the above equation denotes the first time point of the time series of length n for the species included in cluster m. Given m and k, let ({p}^{(m,k)}(i)) be the flowering probability of element i of vector E. The term ({p}^{(m,k)}(i)) is expressed as follows:$${{log }}left[frac{{p}^{left(m,kright)}left(iright)}{1-{p}^{left(m,kright)}left(iright)}right]= mathop{sum }limits_{j=1}^{m}{alpha }_{m,j}cdot {Z}_{m,j}left(iright)+mathop{sum }limits_{jin {G}_{k}^{left(mright)}}^{m}{beta }_{m,j}cdot {Z}_{m,j}left(iright)cdot {{{{{{rm{DU}}}}}}}_{m,j}left(i|{theta }_{j}^{D}right)\ +mathop{sum }limits_{jnotin {G}_{k}^{left(mright)}}^{m}{beta }_{m,j}cdot {Z}_{m,j}left(iright)cdot {{{{{rm{CU}}}}}}left(i|{theta }_{j}^{C}right)times {{{{{{rm{DU}}}}}}}_{m,j}left(i|{theta }_{j}^{D}right),$$
(17)
where ({Z}_{m,j}(i)) is the dummy variable indicating a cluster for i; ({Z}_{m,j}(i)) equals 1 if the ith element of E belongs to the jth cluster, otherwise it is zero, and ({alpha }_{m,j}) and ({beta }_{m,j}) in Eq. (5) are regression coefficients for the jth cluster when the species are grouped into m clusters. We estimate the parameters and the number of clusters based on a finite number of observations. Given the number of clusters m, for each of m clusters, the parameters were estimated by maximizing the loglikelihood value calculated for all combinations of potential parameter values for ({n}_{1},{n}_{2},bar{C},) and (bar{D}) within the ranges of [1 (min), 50 (max)] for n1, [1,50] for n2, [19,25] for (bar{C}), and [1,9] for (bar{D}). We varied the days (n1 and n2) by integers, temperature ((bar{C})) by tenths of a degree C, and daily precipitation ((bar{D})) by tenths of a mm. Regression coefficients (({alpha }_{m,j}), ({beta }_{m,j})) for all j values under a given m value and associated likelihoods were determined using generalized linear models with binomial error structures.With the results of the parameter estimations, we determined the number of clusters in two steps. For the first step, for a given m, we obtained (hat{k}(m)) according to the following equation:$$hat{k}(m)={arg }mathop{{min }}limits_{k}{{{{{{rm{AIC}}}}}}{m,k(m)},,k(m),=,0,,…,{2}^{m}}.$$
(18)
For the second step, with the results of (hat{k}) obtained from the first step, we obtained the estimate of the number of clusters according to forward selection by searching for the (hat{m}) value that satisfies the following inequalities:$${{{{{rm{AIC}}}}}}(hat{m},,hat{k}(hat{m})), < ,{{{{{rm{AIC}}}}}}(hat{m}+1,,hat{k}(hat{m}+1))cap {{{{{rm{AIC}}}}}}(hat{m},,hat{k}(hat{m})), < ,{{{{{rm{AIC}}}}}}(hat{m}-1,,hat{k}(hat{m}-1)).$$ (19) For model fitting, the first flowering month was extracted from the flowering phenology data. When flowering lasted more than 1 month, the month after the first flowering month was replaced by a value of zero (absence of flowering). If the month before the first flowering month was a missing value, the first flowering month was treated as a missing value and was not used for further analyses. We assumed that phenology monitoring was performed on the first date of each month.Projections of 21st-century changes in flowering phenologyWe used two scenarios (RCP2.6 and RCP8.5) to forecast future reproductive phenology in dipterocarp species for each of the three GCMs (GFDL–ESM2M, IPSL–CM5A-LR, and MIROC5). We predicted the flowering probability per month for each phenological cluster during the periods from 1 May 1976–31 March 1996 and from 1 January 2050–31 December 2099 based on the best model (Supplementary Table 2). The predicted flowering probability during the 2050–2099 period was normalized to that during the 1976–1996 period for each climate scenario and for each of three GCMs. To compare the seasonal patterns between 1976–1996 and 2050–2099, the predicted flowering probability was averaged for each month from January to December and plotted for each month in Fig. 6. R version 3.6.3 was used for all analyses.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. & West, G. B. Toward a metabolic theory of ecology. Ecology 85, 1771–1789 (2004).Article 

Google Scholar 
Price, S. A. & Hopkins, S. S. B. The macroevolutionary relationship between diet and body mass across mammals. Biol. J. Linn. Soc. Lond. 115, 173–184 (2015).Article 

Google Scholar 
Hiiemae, K. M. in Feeding: Form, Function, and Evolution in Tetrapod Vertebrates (ed. Schwenk, K.) 411–448 (Academic Press, 2000).Pineda-Munoz, S., Evans, A. R. & Alroy, J. The relationship between diet and body mass in terrestrial mammals. Paleobiology 42, 659–669 (2016).Article 

Google Scholar 
Clauss, M., Steuer, P., Müller, D. W. H., Codron, D. & Hummel, J. Herbivory and body size: allometries of diet quality and gastrointestinal physiology, and implications for herbivore ecology and dinosaur gigantism. PLoS ONE 8, e68714 (2013).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Jarman, P. J. The Effect of the Creation of Lake Kariba upon the Terrestrial Ecology of the Middle Zambezi Valley, with Particular References to the Large Mammals. PhD thesis, Univ. of Manchester (1968).Bell, R. H. V. A grazing ecosystem in the Serengeti. Sci. Am. 225, 86–93 (1971).Article 

Google Scholar 
Belovsky, G. E. Optimal foraging and community structure: the allometry of herbivore food selection and competition. Evol. Ecol. 11, 641–672 (1997).Article 

Google Scholar 
Carbone, C., Mace, G. M., Roberts, S. C. & Macdonald, D. W. Energetic constraints on the diet of terrestrial carnivores. Nature 402, 286–288 (1999).CAS 
Article 
PubMed 

Google Scholar 
Carbone, C., Teacher, A. & Rowcliffe, J. M. The costs of carnivory. PLoS Biol. 5, e22 (2007).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Peters, R. H. The Ecological Implications of Body Size (Cambridge Univ. Press, 1983).Burness, G. P., Diamond, J. & Flannery, T. Dinosaurs, dragons, and dwarfs: the evolution of maximal body size. Proc. Natl Acad. Sci. USA 98, 14518–14523 (2001).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Bergmann, C. Über die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse (Vandenhoeck & Ruprecht Verlage, 1848).Gearty, W., McClain, C. R. & Payne, J. L. Energetic tradeoffs control the size distribution of aquatic mammals. Proc. Natl Acad. Sci. USA 115, 4194–4199 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Gearty, W. & Payne, J. L. Physiological constraints on body size distributions in Crocodyliformes. Evolution 74, 245–255 (2020).Article 
PubMed 

Google Scholar 
Tucker, M. A. & Rogers, T. L. Examining predator–prey body size, trophic level and body mass across marine and terrestrial mammals. Proc. Biol. Sci. 281, 20142103 (2014).PubMed 
PubMed Central 

Google Scholar 
Archibald, J. D. Extinction and Radiation: How the Fall of the Dinosaurs Led to the Rise of Mammals (The Johns Hopkins Univ. Press, 2011).Ripple, W. J. et al. Extinction risk is most acute for the world’s largest and smallest vertebrates. Proc. Natl Acad. Sci. USA 114, 10678–10683 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Alroy, J. Cope’s rule and the dynamics of body mass evolution in North American fossil mammals. Science 280, 731–734 (1998).CAS 
Article 
PubMed 

Google Scholar 
Smith, F. A. et al. The evolution of maximum body size of terrestrial mammals. Science 330, 1216–1219 (2010).CAS 
Article 
PubMed 

Google Scholar 
Smith, F. A., Smith, R. E. E., Lyons, S. K. & Payne, J. L. Body size downgrading of mammals over the Late Quaternary. Science 360, 310–313 (2018).CAS 
Article 
PubMed 

Google Scholar 
Alroy, J. The fossil record of North American mammals: evidence for a Paleocene evolutionary radiation. Syst. Biol. 48, 107–118 (1999).CAS 
Article 
PubMed 

Google Scholar 
Slater, G. J. Phylogenetic evidence for a shift in the mode of mammalian body size evolution at the Cretaceous-Palaeogene boundary. Methods Ecol. Evol. 4, 734–744 (2013).Article 

Google Scholar 
Tucker, M. A., Ord, T. J. & Rogers, T. L. Evolutionary predictors of mammalian home range size: body mass, diet and the environment. Glob. Ecol. Biogeogr. 23, 1105–1114 (2014).Article 

Google Scholar 
Slater, G. J., Goldbogen, J. A. & Pyenson, N. D. Independent evolution of baleen whale gigantism linked to Plio-Pleistocene ocean dynamics. Proc. Biol. Sci. 284, 20170546 (2017).PubMed 
PubMed Central 

Google Scholar 
Bojarska, K. & Selva, N. Spatial patterns in brown bear Ursus arctos diet: the role of geographical and environmental factors. Mamm. Rev. 42, 120–143 (2012).Article 

Google Scholar 
Virgós, E. et al. Body size clines in the European badger and the abundant centre hypothesis. J. Biogeogr. 38, 1546–1556 (2011).Article 

Google Scholar 
Lyons, S. K., Smith, F. A. & Brown, J. H. Of mice, mastodons and men: human-mediated extinctions on four continents. Evol. Ecol. Res. 6, 339–358 (2004).
Google Scholar 
Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L. & Shabel, A. B. Assessing the causes of late Pleistocene extinctions on the continents. Science 306, 70–75 (2004).CAS 
Article 
PubMed 

Google Scholar 
Blois, J. L. & Hadly, E. A. Mammalian response to Cenozoic climatic change. Annu. Rev. Earth Planet. Sci. 37, 181–208 (2009).CAS 
Article 

Google Scholar 
Tomašových, A. & Kidwell, S. M. Fidelity of variation in species composition and diversity partitioning by death assemblages: time-averaging transfers diversity from beta to alpha levels. Paleobiology 35, 94–118 (2009).Article 

Google Scholar 
Bakker, E. S. et al. Combining paleo-data and modern exclosure experiments to assess the impact of megafauna extinctions on woody vegetation. Proc. Natl Acad. Sci. USA 113, 847–855 (2016).Malhi, Y. et al. Megafauna and ecosystem function from the Pleistocene to the Anthropocene. Proc. Natl Acad. Sci. USA 113, 838–846 (2016).Pires, M. M., Guimarães, P. R., Galetti, M. & Jordano, P. Pleistocene megafaunal extinctions and the functional loss of long-distance seed-dispersal services. Ecography 41, 153–163 (2018).Article 

Google Scholar 
Doughty, C. E. et al. Global nutrient transport in a world of giants. Proc. Natl Acad. Sci. USA 113, 868–873 (2016).CAS 
Article 
PubMed 

Google Scholar 
Enquist, B. J., Abraham, A. J., Harfoot, M. B. J., Malhi, Y. & Doughty, C. E. The megabiota are disproportionately important for biosphere functioning. Nat. Commun. 11, 699 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Estes, J. A., Heithaus, M., McCauley, D. J., Rasher, D. B. & Worm, B. Megafaunal impacts on structure and function of ocean ecosystems. Annu. Rev. Environ. Resour. 41, 83–116 (2016).Article 

Google Scholar 
Bellwood, D. R., Hoey, A. S. & Choat, J. H. Limited functional redundancy in high diversity systems: resilience and ecosystem function on coral reefs. Ecol. Lett. 6, 281–285 (2003).Article 

Google Scholar 
Leip, A. et al. Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004 (2015).Article 
CAS 

Google Scholar 
Smith, D., King, R. & Allen, B. L. Impacts of exclusion fencing on target and non-target fauna: a global review. Biol. Rev. Camb. Philos. Soc. 95, 1590–1606 (2020).Article 
PubMed 

Google Scholar 
Galetti, M. et al. Ecological and evolutionary legacy of megafauna extinctions. Biol. Rev. 93, 845–862 (2018).Article 
PubMed 

Google Scholar 
Sandom, C. J. et al. Learning from the past to prepare for the future: felids face continued threat from declining prey. Ecography 41, 140–152 (2018).Article 

Google Scholar 
Zavaleta, E. et al. Ecosystem responses to community disassembly. Ann. N. Y. Acad. Sci. 1162, 311–333 (2009).Article 
PubMed 

Google Scholar 
Hoy, S. R., Peterson, R. O. & Vucetich, J. A. Climate warming is associated with smaller body size and shorter lifespans in moose near their southern range limit. Glob. Change Biol. 24, 2488–2497 (2018).Article 

Google Scholar 
Peralta-Maraver, I. & Rezende, E. L. Heat tolerance in ectotherms scales predictably with body size. Nat. Clim. Change 11, 58–63 (2020).Article 

Google Scholar 
Smith, F. A. et al. Unraveling the consequences of the terminal Pleistocene megafauna extinction on mammal community assembly. Ecography 39, 223–239 (2016).Article 

Google Scholar 
Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Projected losses of global mammal and bird ecological strategies. Nat. Commun. 10, 2279 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Smith, F. A., Elliott Smith, R. E., Lyons, S. K., Payne, J. L. & Villaseñor, A. The accelerating influence of humans on mammalian macroecological patterns over the Late Quaternary. Quat. Sci. Rev. 211, 1–16 (2019).Article 

Google Scholar 
Middleton, O. S., Scharlemann, J. P. W. & Sandom, C. J. Homogenization of carnivorous mammal ensembles caused by global range reductions of large-bodied hypercarnivores during the Late Quaternary. Proc. Biol. Sci. 287, 20200804 (2020).PubMed 
PubMed Central 

Google Scholar 
Pimiento, C. et al. Functional diversity of marine megafauna in the Anthropocene. Sci. Adv. 6, eaay7650 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).CAS 
Article 
PubMed 

Google Scholar 
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Schreiber, E. A. & Burger, J. Biology of Marine Birds (CRC Press, 2001).Cooke, R. S. C., Bates, A. E. & Eigenbrod, F. Global trade-offs of functional redundancy and functional dispersion for birds and mammals. Glob. Ecol. Biogeogr. 28, 484–495 (2019).Article 

Google Scholar 
Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648 (2009).Article 

Google Scholar 
Pacifici, M. et al. Generation length for mammals. Nat. Conserv. 5, 89–94 (2013).Article 

Google Scholar 
Wilman, H. et al. EltonTraits 1.0: species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027 (2014).Article 

Google Scholar 
Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109 (2015).Article 

Google Scholar 
Atwood, T. B. et al. Herbivores at the highest risk of extinction among mammals, birds, and reptiles. Sci. Adv. 6, eabb8458 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).Article 
PubMed 
PubMed Central 

Google Scholar 
Pineda-Munoz, S. & Alroy, J. Dietary characterization of terrestrial mammals. Proc. Biol. Sci. 281, 20141173 (2014).PubMed 
PubMed Central 

Google Scholar 
Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).
Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience 51, 933–938 (2001).Article 

Google Scholar 
Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. Bioscience 57, 573–583 (2007).Article 

Google Scholar 
Kidwell, S. M. & Flessa, K. W. The quality of the fossil record: populations, species, and communities. Annu. Rev. Earth Planet. Sci. 24, 433–464 (1996).CAS 
Article 

Google Scholar 
Miller, J. H. et al. Ecological fidelity of functional traits based on species presence–absence in a modern mammalian bone assemblage (Amboseli, Kenya). Paleobiology 40, 560–583 (2014).Article 

Google Scholar 
Smith, F. A. et al. Similarity of mammalian body size across the taxonomic hierarchy and across space and time. Am. Nat. 163, 672–691 (2004).Article 
PubMed 

Google Scholar 
Andermann, T., Faurby, S., Cooke, R., Silvestro, D. & Antonelli, A. iucn_sim: a new program to simulate future extinctions based on IUCN threat status. Ecography 44, 162–176 (2021).Article 

Google Scholar 
Mooers, A., Faith, D. P. & Maddison, W. P. Converting endangered species categories to probabilities of extinction for phylogenetic conservation prioritization. PLoS ONE 3, e3700 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Koch, P. L. & Barnosky, A. D. Late Quaternary extinctions: state of the debate. Annu. Rev. Ecol. Evol. Syst. 37, 215–250 (2006).Article 

Google Scholar 
Clauset, A. & Erwin, D. H. The evolution and distribution of species body size. Science 321, 399–401 (2008).CAS 
Article 
PubMed 

Google Scholar 
Clauss, M. et al. The maximum attainable body size of herbivorous mammals: morphophysiological constraints on foregut, and adaptations of hindgut fermenters. Oecologia 136, 14–27 (2003).CAS 
Article 
PubMed 

Google Scholar 
Alexander, R. M. All-time giants: the largest animals and their problems. Palaeontology 41, 1231–1245 (1998).
Google Scholar 
Dobson, G. P. On being the right size: heart design, mitochondrial efficiency and lifespan potential. Clin. Exp. Pharmacol. Physiol. 30, 590–597 (2003).CAS 
Article 
PubMed 

Google Scholar 
Blackburn, T. M., Gaston, K. J. & Loder, N. Geographic gradients in body size: a clarification of Bergmann’s rule. Divers. Distrib. 5, 165–174 (1999).Article 

Google Scholar  More

Sagan, L. On the origin of mitosing cells. J. Theor. Biol. 14, 255–274 (1967).CAS 
PubMed 
Article 

Google Scholar 
Taylor, F. J. R. Implications and extensions of the serial endosymbiosis theory of the origin of eukaryotes. Taxon 23, 229–258 (1974).Article 

Google Scholar 
Margulis, L. Serial endosymbiotic theory (SET) and composite individuality. Microbiol. Today 31, 172–175 (2004).
Google Scholar 
Mereschkowsky, C. Über Natur und Ursprung der Chromatophoren im Pflanzenreiche. Biol. Centralbl. 25, 593–604 (1905).
Google Scholar 
Wallin, I. E. On the nature of mitochondria. IX. Demonstration of the bacterial nature of mitochondria. Am. J. Anat. 36, 131–149 (1925).Article 

Google Scholar 
Martin, W. F. Physiology, anaerobes, and the origin of mitosing cells 50 years on. J. Theor. Biol. 434, 2–10 (2017).CAS 
PubMed 
Article 

Google Scholar 
Müller, M. et al. Biochemistry and evolution of anaerobic energy metabolism in eukaryotes. Microbiol. Mol. Biol. Rev. 76, 444–495 (2012).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Spang, A. et al. Complex archaea that bridge the gap between prokaryotes and eukaryotes. Nature 521, 173–179 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Imachi, H. et al. Isolation of an archaeon at the prokaryote–eukaryote interface. Nature 577, 519–525 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morris, B. E. L., Henneberger, R., Huber, H. & Moissl-Eichinger, C. Microbial syntrophy: interaction for the common good. FEMS Microbiol. Rev. 37, 384–406 (2013).CAS 
PubMed 
Article 

Google Scholar 
Martin, W. & Müller, M. The hydrogen hypothesis for the first eukaryote. Nature 392, 37–41 (1998).CAS 
PubMed 
Article 

Google Scholar 
Moreira, D. & Lopez-Garcia, P. Symbiosis between methanogenic archaea and delta-proteobacteria as the origin of eukaryotes: the syntrophic hypothesis. J. Mol. Evol. 47, 517–530 (1998).CAS 
PubMed 
Article 

Google Scholar 
Sousa, F. L., Neukirchen, S., Allen, J. F., Lane, N. & Martin, W. F. Lokiarchaeon is hydrogen dependent. Nat. Microbiol. 1, 16034 (2016).CAS 
PubMed 
Article 

Google Scholar 
Spang, A. et al. Proposal of the reverse flow model for the origin of the eukaryotic cell based on comparative analyses of Asgard archaeal metabolism. Nat. Microbiol. 4, 1138–1148 (2019).CAS 
PubMed 
Article 

Google Scholar 
López-García, P. & Moreira, D. The syntrophy hypothesis for the origin of eukaryotes revisited. Nat. Microbiol. 5, 655–667 (2020).PubMed 
Article 
CAS 

Google Scholar 
Eme, L., Sharpe, S. C., Brown, M. W. & Roger, A. J. in The Origin and Evolution of Eukaryotes (eds. Keeling, P. J. & Koonin, E. V.) 165–180 (Cold Spring Harbor Perspectives in Biology, 2014).Betts, H. C. et al. Integrated genomic and fossil evidence illuminates life’s early evolution and eukaryote origin. Nat. Ecol. Evol. 2, 1556–1562 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Porter, S. M. Insights into eukaryogenesis from the fossil record. Interface Focus 10, 20190105 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Agić, H. in Prebiotic Chemistry and the Origin of Life (eds. Neubeck, A. & McMahon, S.) 255–289 (Springer International, 2021).Lyons, T. W., Reinhard, C. T. & Planavsky, N. J. The rise of oxygen in Earth’s early ocean and atmosphere. Nature 506, 307–315 (2014).CAS 
PubMed 
Article 

Google Scholar 
Lenton, T. M. & Daines, S. J. Biogeochemical transformations in the history of the ocean. Ann. Rev. Mar. Sci. 9, 31–58 (2017).PubMed 
Article 

Google Scholar 
Lenton, T. M. On the use of models in understanding the rise of complex life. Interface Focus 10, 20200018 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Liu, P. et al. Triple oxygen isotope constraints on atmospheric O2 and biological productivity during the mid-Proterozoic. Proc. Natl Acad. Sci. USA 118, e2105074118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mentel, M. & Martin, W. Energy metabolism among eukaryotic anaerobes in light of Proterozoic ocean chemistry. Philos. Trans. R. Soc. Lond. B 363, 2717–2729 (2008).Article 

Google Scholar 
Zimorski, V., Mentel, M., Tielens, A. G. M. & Martin, W. F. Energy metabolism in anaerobic eukaryotes and Earth’s late oxygenation. Free Radic. Biol. Med. 140, 279–294 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, W. F., Tielens, A. G. M. & Mentel, M. Mitochondria and Anaerobic Energy Metabolism in Eukaryotes: Biochemistry and Evolution (Walter de Gruyter, 2020).Hall, J. B. The nature of the host in the origin of the eukaryote cell. J. Theor. Biol. 38, 413–418 (1973).CAS 
PubMed 
Article 

Google Scholar 
Stanier, R. Y. in Organization and Control in Prokaryotic and Eukaryotic Cells (eds. Charles, H. P. & Knight, B. C. J. G.) vol. 20, 1–38 (Cambridge Univ. Press, 1970).De Duve, C. Origin of mitochondria. Science 182, 85 (1973).PubMed 
Article 

Google Scholar 
Andersson, S. G. & Kurland, C. G. Origins of mitochondria and hydrogenosomes. Curr. Opin. Microbiol. 2, 535–541 (1999).CAS 
PubMed 
Article 

Google Scholar 
Cavalier-Smith, T. The phagotrophic origin of eukaryotes and phylogenetic classification of Protozoa. Int. J. Syst. Evol. Microbiol. 52, 297–354 (2002).CAS 
PubMed 
Article 

Google Scholar 
de Duve, C. The origin of eukaryotes: a reappraisal. Nat. Rev. Genet. 8, 395–403 (2007).PubMed 
Article 
CAS 

Google Scholar 
Knoll, A. H. & Nowak, M. A. The timetable of evolution. Sci. Adv. 3, e1603076 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Martin, W. F. & Müller, M. Origin of Mitochondria and Hydrogenosomes (Springer, 2007).Lindmark, D. G. & Müller, M. Hydrogenosome, a cytoplasmic organelle of the anaerobic flagellate Tritrichomonas foetus, and its role in pyruvate metabolism. J. Biol. Chem. 248, 7724–7728 (1973).CAS 
PubMed 
Article 

Google Scholar 
Müller, M. in Origin of Mitochondria and Hydrogenosomes (eds. Martin, W. F. & Müller, M.) 1–10 (Springer, 2007).Zillig, W. et al. Did eukaryotes originate by a fusion event? Endocytobiosis Cell Res. 6, 1–25 (1989).
Google Scholar 
Embley, T. M. & Martin, W. Eukaryotic evolution, changes and challenges. Nature 440, 623–630 (2006).CAS 
PubMed 
Article 

Google Scholar 
Stairs, C. W., Leger, M. M. & Roger, A. J. Diversity and origins of anaerobic metabolism in mitochondria and related organelles. Philos. Trans. R. Soc. Lond. B 370, 20140326 (2015).Article 
CAS 

Google Scholar 
Roger, A. J., Muñoz-Gómez, S. A. & Kamikawa, R. The origin and diversification of mitochondria. Curr. Biol. 27, R1177–R1192 (2017).CAS 
PubMed 
Article 

Google Scholar 
Zachar, I. & Szathmáry, E. Breath-giving cooperation: critical review of origin of mitochondria hypotheses. Biol. Direct 12, 19 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Eme, L., Spang, A., Lombard, J., Stairs, C. W. & Ettema, T. J. G. Archaea and the origin of eukaryotes. Nat. Rev. Microbiol. 15, 711–723 (2018).Article 
CAS 

Google Scholar 
Stairs, C. W. et al. Microbial eukaryotes have adapted to hypoxia by horizontal acquisitions of a gene involved in rhodoquinone biosynthesis. eLife 7, e34292 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Martin, W. F. Too much eukaryote LGT. Bioessays 39, 1700115 (2017).Article 

Google Scholar 
Leger, M. M., Eme, L., Stairs, C. W. & Roger, A. J. Demystifying eukaryote lateral gene transfer (response to Martin 2017 https://doi.org/10.1002/bies.201700115). Bioessays 40, e1700242 (2018).Martin, W. Mosaic bacterial chromosomes: a challenge en route to a tree of genomes. Bioessays 21, 99–104 (1999).CAS 
PubMed 
Article 

Google Scholar 
Nagies, F. S. P., Brueckner, J., Tria, F. D. K. & Martin, W. F. A spectrum of verticality across genes. PLoS Genet. 16, e1009200 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guy, L. & Ettema, T. J. G. The archaeal ‘TACK’ superphylum and the origin of eukaryotes. Trends Microbiol. 19, 580–587 (2011).CAS 
PubMed 
Article 

Google Scholar 
Williams, T. A., Foster, P. G., Cox, C. J. & Embley, T. M. An archaeal origin of eukaryotes supports only two primary domains of life. Nature 504, 231–236 (2013).CAS 
PubMed 
Article 

Google Scholar 
McInerney, J. O., O’Connell, M. J. & Pisani, D. The hybrid nature of the Eukaryota and a consilient view of life on Earth. Nat. Rev. Microbiol. 12, 449–455 (2014).CAS 
PubMed 
Article 

Google Scholar 
Raymann, K., Brochier-Armanet, C. & Gribaldo, S. The two-domain tree of life is linked to a new root for the Archaea. Proc. Natl Acad. Sci. USA 112, 6670–6675 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Williams, T. A., Cox, C. J., Foster, P. G., Szöllősi, G. J. & Embley, T. M. Phylogenomics provides robust support for a two-domains tree of life. Nat. Ecol. Evol. 4, 138–147 (2020).PubMed 
Article 

Google Scholar 
Zaremba-Niedzwiedzka, K. et al. Asgard archaea illuminate the origin of eukaryotic cellular complexity. Nature 541, 353–358 (2017).CAS 
PubMed 
Article 

Google Scholar 
López-García, P. & Moreira, D. Cultured Asgard archaea shed light on eukaryogenesis. Cell 181, 232–235 (2020).PubMed 
Article 
CAS 

Google Scholar 
Martin, W. F., Tielens, A. G. M., Mentel, M., Garg, S. G. & Gould, S. B. The physiology of phagocytosis in the context of mitochondrial origin. Microbiol. Mol. Biol. Rev. 81, e00008–17 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Berkner, L. V. & Marshall, L. C. History of major atmospheric components. Proc. Natl Acad. Sci. USA 53, 1215–1226 (1965).CAS 
PubMed Central 
Article 

Google Scholar 
Stolper, D. A., Revsbech, N. P. & Canfield, D. E. Aerobic growth at nanomolar oxygen concentrations. Proc. Natl Acad. Sci. USA 107, 18755–18760 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Degli Esposti, M., Mentel, M., Martin, W. & Sousa, F. L. Oxygen reductases in alphaproteobacterial genomes: physiological evolution from low to high oxygen environments. Front. Microbiol. 10, 499 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Berg, J. et al. How low can they go? Aerobic respiration by microorganisms under apparent anoxia. FEMS Microbiol. Rev. https://doi.org/10.1093/femsre/fuac006 (2022).Cloud, P. Cosmos, Earth, and Man: A Short History of the Universe (Yale Univ. Press, 1978).Pichler, H. & Riezman, H. Where sterols are required for endocytosis. Biochim. Biophys. Acta 1666, 51–61 (2004).CAS 
PubMed 
Article 

Google Scholar 
Hoshino, Y. & Gaucher, E. A. Evolution of bacterial steroid biosynthesis and its impact on eukaryogenesis. Proc. Natl Acad. Sci. USA 118, e2101276118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Waldbauer, J. R., Newman, D. K. & Summons, R. E. Microaerobic steroid biosynthesis and the molecular fossil record of Archean life. Proc. Natl Acad. Sci. USA 108, 13409–13414 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Valentine, D. L. in Symbiosis: Mechanisms and Model Systems (ed. Seckbach, J.) 147–161 (Springer, 2002).Canfield, D. E. & Thamdrup, B. Towards a consistent classification scheme for geochemical environments, or, why we wish the term ‘suboxic’ would go away. Geobiology 7, 385–392 (2009).CAS 
PubMed 
Article 

Google Scholar 
McInerney, M. J., Sieber, J. R. & Gunsalus, R. P. Syntrophy in anaerobic global carbon cycles. Curr. Opin. Biotechnol. 20, 623–632 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schink, B. Synergistic interactions in the microbial world. Antonie Van Leeuwenhoek 81, 257–261 (2002).CAS 
PubMed 
Article 

Google Scholar 
Stams, A. J. M. & Plugge, C. M. Electron transfer in syntrophic communities of anaerobic bacteria and archaea. Nat. Rev. Microbiol. 7, 568–577 (2009).CAS 
PubMed 
Article 

Google Scholar 
Embley, T. M., van der Giezen, M., Horner, D. S., Dyal, P. L. & Foster, P. Mitochondria and hydrogenosomes are two forms of the same fundamental organelle. Philos. Trans. R. Soc. Lond. B 358, 191–201 (2003). discussion 201–2.CAS 
Article 

Google Scholar 
Donoghue, P. C. J. & Purnell, M. A. Distinguishing heat from light in debate over controversial fossils. Bioessays 31, 178–189 (2009).PubMed 
Article 

Google Scholar 
Brocks, J. J., Logan, G. A., Buick, R. & Summons, R. E. Archean molecular fossils and the early rise of eukaryotes. Science 285, 1033–1036 (1999).CAS 
PubMed 
Article 

Google Scholar 
Rasmussen, B., Fletcher, I. R., Brocks, J. J. & Kilburn, M. R. Reassessing the first appearance of eukaryotes and cyanobacteria. Nature 455, 1101–1104 (2008).CAS 
PubMed 
Article 

Google Scholar 
French, K. L. et al. Reappraisal of hydrocarbon biomarkers in Archean rocks. Proc. Natl Acad. Sci. USA 112, 5915–5920 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brocks, J. J. et al. The rise of algae in Cryogenian oceans and the emergence of animals. Nature 548, 578–581 (2017).CAS 
PubMed 
Article 

Google Scholar 
Hoshino, Y. et al. Cryogenian evolution of stigmasteroid biosynthesis. Sci. Adv. 3, e1700887 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bengtson, S. et al. Fungus-like mycelial fossils in 2.4-billion-year-old vesicular basalt. Nat. Ecol. Evol. 1, 141 (2017).PubMed 
Article 

Google Scholar 
Butterfield, N. J. Probable Proterozoic fungi. Paleobiology 31, 165–182 (2005).Article 

Google Scholar 
Butterfield, N. J. Early evolution of the Eukaryota. Palaeontology 58, 5–17 (2015).Article 

Google Scholar 
Berbee, M. L. et al. Genomic and fossil windows into the secret lives of the most ancient fungi. Nat. Rev. Microbiol. 18, 717–730 (2020).CAS 
PubMed 
Article 

Google Scholar 
Lamb, D. M., Awramik, S. M., Chapman, D. J. & Zhu, S. Evidence for eukaryotic diversification in the 1800 million-year-old Changzhougou Formation, North China. Precambrian Res. 173, 93–104 (2009).CAS 
Article 

Google Scholar 
Javaux, E. J., Knoll, A. H. & Walter, M. R. Morphological and ecological complexity in early eukaryotic ecosystems. Nature 412, 66–69 (2001).CAS 
PubMed 
Article 

Google Scholar 
Butterfield, N. J. Modes of pre-Ediacaran multicellularity. Precambrian Res. 173, 201–211 (2009).CAS 
Article 

Google Scholar 
Peng, Y., Bao, H. & Yuan, X. New morphological observations for Paleoproterozoic acritarchs from the Chuanlinggou Formation, North China. Precambrian Res. 168, 223–232 (2009).CAS 
Article 

Google Scholar 
Javaux, E. J. in Origins and Evolution of Life: An Astrobiological Perspective (eds Gargaud, M., López-García, P. & Martin, H.) 414–449 (Cambridge Univ. Press, 2011).Stairs, C. W. & Ettema, T. J. G. The archaeal roots of the eukaryotic dynamic actin cytoskeleton. Curr. Biol. 30, R521–R526 (2020).CAS 
PubMed 
Article 

Google Scholar 
Carlisle, E. M., Jobbins, M., Pankhania, V., Cunningham, J. A. & Donoghue, P. C. J. Experimental taphonomy of organelles and the fossil record of early eukaryote evolution. Sci. Adv. 7, eabe9487 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Han, T. M. & Runnegar, B. Megascopic eukaryotic algae from the 2.1-billion-year-old negaunee iron-formation, Michigan. Science 257, 232–235 (1992).CAS 
PubMed 
Article 

Google Scholar 
Javaux, E. J. & Lepot, K. The Paleoproterozoic fossil record: implications for the evolution of the biosphere during Earth’s middle-age. Earth-Sci. Rev. 176, 68–86 (2018).CAS 
Article 

Google Scholar 
Agić, H., Moczydłowska, M. & Yin, L. Diversity of organic-walled microfossils from the early Mesoproterozoic Ruyang Group, North China Craton – A window into the early eukaryote evolution. Precambrian Res. 297, 101–130 (2017).Article 
CAS 

Google Scholar 
Pang, K. et al. The nature and origin of nucleus-like intracellular inclusions in Paleoproterozoic eukaryote microfossils. Geobiology 11, 499–510 (2013).CAS 
PubMed 

Google Scholar 
Bengtson, S., Belivanova, V., Rasmussen, B. & Whitehouse, M. The controversial ‘Cambrian’ fossils of the Vindhyan are real but more than a billion years older. Proc. Natl Acad. Sci. USA 106, 7729–7734 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bengtson, S., Sallstedt, T., Belivanova, V. & Whitehouse, M. Three-dimensional preservation of cellular and subcellular structures suggests 1.6 billion-year-old crown-group red algae. PLoS Biol. 15, e2000735 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Tang, Q., Pang, K., Yuan, X. & Xiao, S. A one-billion-year-old multicellular chlorophyte. Nat. Ecol. Evol. 4, 543–549 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Bykova, N. et al. Seaweeds through time: morphological and ecological analysis of Proterozoic and early Paleozoic benthic macroalgae. Precambrian Res. 350, 105875 (2020).CAS 
Article 

Google Scholar 
Maloney, K. M. et al. New multicellular marine macroalgae from the early Tonian of northwestern Canada. Geology 49, 743–747 (2021).CAS 
Article 

Google Scholar 
Tang, Q. et al. The Proterozoic macrofossil Tawuia as a coenocytic eukaryote and a possible macroalga. Palaeogeogr. Palaeoclimatol. Palaeoecol. 576, 110485 (2021).Article 

Google Scholar 
Sforna, M. C. et al. Intracellular bound chlorophyll residues identify 1 Gyr-old fossils as eukaryotic algae. Nat. Commun. 13, 146 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Strother, P. K. et al. A possible billion-year-old holozoan with differentiated multicellularity. Curr. Biol. 31, 2658–2665.e2 (2021).CAS 
PubMed 
Article 

Google Scholar 
Loron, C. C. et al. Early fungi from the Proterozoic era in Arctic Canada. Nature 570, 232–235 (2019).CAS 
PubMed 
Article 

Google Scholar 
Bonneville, S. et al. Molecular identification of fungi microfossils in a Neoproterozoic shale rock. Sci. Adv. 6, eaax7599 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gibson, T. M. et al. Precise age of Bangiomorpha pubescens dates the origin of eukaryotic photosynthesis. Geology 46, 135–138 (2018).CAS 
Article 

Google Scholar 
Butterfield, N. J. Bangiomorpha pubescens n. gen., n. sp.: implications for the evolution of sex, multicellularity, and the Mesoproterozoic/Neoproterozoic radiation of eukaryotes. Paleobiology 26, 386–404 (2000).Article 

Google Scholar 
Husson, J. M. & Peters, S. E. Nature of the sedimentary rock record and its implications for Earth system evolution. Emerg. Top. Life Sci. 2, 125–136 (2018).CAS 
PubMed 
Article 

Google Scholar 
Donoghue, P. C. J. & Yang, Z. The evolution of methods for establishing evolutionary timescales. Philos. Trans. R. Soc. Lond. B 371, 20160020 (2016).Article 

Google Scholar 
Berney, C. & Pawlowski, J. A molecular time-scale for eukaryote evolution recalibrated with the continuous microfossil record. Proc. Biol. Sci. 273, 1867–1872 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chernikova, D., Motamedi, S., Csürös, M., Koonin, E. V. & Rogozin, I. B. A late origin of the extant eukaryotic diversity: divergence time estimates using rare genomic changes. Biol. Direct 6, 26 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parfrey, L. W., Lahr, D. J. G., Knoll, A. H. & Katz, L. A. Estimating the timing of early eukaryotic diversification with multigene molecular clocks. Proc. Natl Acad. Sci. USA 108, 13624–13629 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shih, P. M. & Matzke, N. J. Primary endosymbiosis events date to the later Proterozoic with cross-calibrated phylogenetic dating of duplicated ATPase proteins. Proc. Natl Acad. Sci. USA 110, 12355–12360 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Canfield, D. E. The early history of atmospheric oxygen: homage to Robert M. Garrels. Annu. Rev. Earth Planet. Sci. 33, 1–36 (2005).CAS 
Article 

Google Scholar 
Kump, L. R. The rise of atmospheric oxygen. Nature 451, 277–278 (2008).CAS 
PubMed 
Article 

Google Scholar 
Holland, H. D. When did the Earth’s atmosphere become oxic? A reply. Geochem. N. 100, 20–22 (1999).
Google Scholar 
Holland, H. D. Volcanic gases, black smokers, and the great oxidation event. Geochim. Cosmochim. Acta 66, 3811–3826 (2002).CAS 
Article 

Google Scholar 
Farquhar, J., Bao, H. & Thiemens, M. Atmospheric influence of Earth’s earliest sulfur cycle. Science 289, 756–759 (2000).CAS 
PubMed 
Article 

Google Scholar 
Poulton, S. W. et al. A 200-million-year delay in permanent atmospheric oxygenation. Nature 592, 232–236 (2021).CAS 
PubMed 
Article 

Google Scholar 
Hodgskiss, M. S. W. & Sperling, E. A. A prolonged, two-step oxygenation of Earth’s early atmosphere: support from confidence intervals. Geology https://doi.org/10.1130/g49385.1 (2021).Article 

Google Scholar 
Fischer, W. W., Hemp, J. & Johnson, J. E. Evolution of oxygenic photosynthesis. Annu. Rev. Earth Planet. Sci. 44, 647–683 (2016).CAS 
Article 

Google Scholar 
Sánchez-Baracaldo, P. & Cardona, T. On the origin of oxygenic photosynthesis and Cyanobacteria. N. Phytol. 225, 1440–1446 (2020).Article 

Google Scholar 
Fournier, G. P. et al. The Archean origin of oxygenic photosynthesis and extant cyanobacterial lineages. Proc. Biol. Sci. 288, 20210675 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Cardona, T., Sánchez-Baracaldo, P., Rutherford, A. W. & Larkum, A. W. Early Archean origin of Photosystem II. Geobiology 17, 127–150 (2019).CAS 
PubMed 
Article 

Google Scholar 
Eigenbrode, J. L. & Freeman, K. H. Late Archean rise of aerobic microbial ecosystems. Proc. Natl Acad. Sci. USA 103, 15759–15764 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Daines, S. J. & Lenton, T. M. The effect of widespread early aerobic marine ecosystems on methane cycling and the Great Oxidation. Earth Planet. Sci. Lett. 434, 42–51 (2016).CAS 
Article 

Google Scholar 
Crowe, S. A. et al. Atmospheric oxygenation three billion years ago. Nature 501, 535–538 (2013).CAS 
PubMed 
Article 

Google Scholar 
Planavsky, N. J. et al. Evidence for oxygenic photosynthesis half a billion years before the Great Oxidation Event. Nat. Geosci. 7, 283–286 (2014).CAS 
Article 

Google Scholar 
Daye, M. et al. Light-driven anaerobic microbial oxidation of manganese. Nature 576, 311–314 (2019).PubMed 
Article 
CAS 

Google Scholar 
Slotznick, S. P. et al. Reexamination of 2.5-Ga ‘whiff’ of oxygen interval points to anoxic ocean before GOE. Sci. Adv. 8, eabj7190 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soo, R. M., Hemp, J., Parks, D. H., Fischer, W. W. & Hugenholtz, P. On the origins of oxygenic photosynthesis and aerobic respiration in Cyanobacteria. Science 355, 1436–1440 (2017).CAS 
PubMed 
Article 

Google Scholar 
Jabłońska, J. & Tawfik, D. S. The evolution of oxygen-utilizing enzymes suggests early biosphere oxygenation. Nat. Ecol. Evol. 5, 442–448 (2021).PubMed 
Article 

Google Scholar 
Mentel, M., Röttger, M., Leys, S., Tielens, A. G. M. & Martin, W. F. Of early animals, anaerobic mitochondria, and a modern sponge. Bioessays 36, 924–932 (2014).PubMed 
Article 

Google Scholar 
Lenton, T. M. et al. Earliest land plants created modern levels of atmospheric oxygen. Proc. Natl Acad. Sci. USA 113, 9704–9709 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krause, A. J. et al. Stepwise oxygenation of the Paleozoic atmosphere. Nat. Commun. 9, 4081 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Daines, S. J., Mills, B. J. W. & Lenton, T. M. Atmospheric oxygen regulation at low Proterozoic levels by incomplete oxidative weathering of sedimentary organic carbon. Nat. Commun. 8, 14379 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Canfield, D. E. A new model for Proterozoic ocean chemistry. Nature 396, 450–453 (1998).CAS 
Article 

Google Scholar 
Sperling, E. A. et al. Statistical analysis of iron geochemical data suggests limited late Proterozoic oxygenation. Nature 523, 451–454 (2015).CAS 
PubMed 
Article 

Google Scholar 
Planavsky, N. J. et al. Low mid-Proterozoic atmospheric oxygen levels and the delayed rise of animals. Science 346, 635–638 (2014).CAS 
PubMed 
Article 

Google Scholar 
Cole, D. B. et al. A shale-hosted Cr isotope record of low atmospheric oxygen during the Proterozoic. Geology 44, 555–558 (2016).CAS 
Article 

Google Scholar 
Wang, C. et al. Strong evidence for a weakly oxygenated ocean-atmosphere system during the Proterozoic. Proc. Natl Acad. Sci. USA 119, e2116101119 (2022).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reinhard, C. T., Planavsky, N. J., Olson, S. L., Lyons, T. W. & Erwin, D. H. Earth’s oxygen cycle and the evolution of animal life. Proc. Natl Acad. Sci. USA 113, 8933–8938 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Poulton, S. W. & Canfield, D. E. Ferruginous conditions: a dominant feature of the ocean through Earth’s history. Elements 7, 107–112 (2011).CAS 
Article 

Google Scholar 
Gilleaudeau, G. J. et al. Uranium isotope evidence for limited euxinia in mid-Proterozoic oceans. Earth Planet. Sci. Lett. 521, 150–157 (2019).CAS 
Article 

Google Scholar 
Cole, D. B. et al. On the co-evolution of surface oxygen levels and animals. Geobiology 319, 55 (2020).
Google Scholar 
Friese, A. et al. Organic matter mineralization in modern and ancient ferruginous sediments. Nat. Commun. 12, 2216 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sperling, E. A., Knoll, A. H. & Girguis, P. R. The ecological physiology of Earth’s second oxygen revolution. Annu. Rev. Ecol. Evol. Syst. 46, 215–235 (2015).Article 

Google Scholar 
Knoll, A. H. Paleobiological perspectives on early eukaryotic evolution. Cold Spring Harb. Perspect. Biol. 6, a016121 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cohen, P. A. & Kodner, R. B. The earliest history of eukaryotic life: uncovering an evolutionary story through the integration of biological and geological data. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2021.11.005 (2021).Szathmáry, E. & Smith, J. M. The major evolutionary transitions. Nature 374, 227–232 (1995).PubMed 
Article 

Google Scholar 
Lane, N. & Martin, W. The energetics of genome complexity. Nature 467, 929–934 (2010).CAS 
PubMed 
Article 

Google Scholar 
Theissen, U., Hoffmeister, M., Grieshaber, M. & Martin, W. Single eubacterial origin of eukaryotic sulfide: quinone oxidoreductase, a mitochondrial enzyme conserved from the early evolution of eukaryotes during anoxic and sulfidic times. Mol. Biol. Evol. 20, 1564–1574 (2003).CAS 
PubMed 
Article 

Google Scholar 
Martin, W. et al. Early cell evolution, eukaryotes, anoxia, sulfide, oxygen, fungi first (?), and a tree of genomes revisited. IUBMB Life 55, 193–204 (2003).Gould, S. B. et al. Adaptation to life on land at high O2 via transition from ferredoxin-to NADH-dependent redox balance. Proc. Biol. Sci. 286, 20191491 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mills, D. B. The origin of phagocytosis in Earth history. Interface Focus 10, 20200019 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Nguyen, K. et al. Absence of biomarker evidence for early eukaryotic life from the Mesoproterozoic Roper Group: searching across a marine redox gradient in mid-Proterozoic habitability. Geobiology 17, 247–260 (2019).CAS 
PubMed 
Article 

Google Scholar 
Lyons, T. W., Diamond, C. W., Planavsky, N. J., Reinhard, C. T. & Li, C. Oxygenation, life, and the planetary system during Earth’s middle history: an overview. Astrobiology 21, 906–923 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Gray, M. W. & Doolittle, W. F. Has the endosymbiont hypothesis been proven? Microbiol. Rev. 46, 1–42 (1982).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gray, M. W., Burger, G. & Lang, B. F. Mitochondrial evolution. Science 283, 1476–1481 (1999).CAS 
PubMed 
Article 

Google Scholar 
Yang, D., Oyaizu, Y., Oyaizu, H., Olsen, G. J. & Woese, C. R. Mitochondrial origins. Proc. Natl Acad. Sci. USA 82, 4443–4447 (1985).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Woese, C. R. Bacterial evolution. Microbiol. Rev. 51, 221–271 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Martijn, J., Vosseberg, J., Guy, L., Offre, P. & Ettema, T. J. G. Deep mitochondrial origin outside the sampled alphaproteobacteria. Nature 557, 101–105 (2018).CAS 
PubMed 
Article 

Google Scholar 
Muñoz-Gómez, S. A. et al. Site-and-branch-heterogeneous analyses of an expanded dataset favour mitochondria as sister to known Alphaproteobacteria. Nat. Ecol. Evol. 6, 253–262 (2022).Fan, L. et al. Phylogenetic analyses with systematic taxon sampling show that mitochondria branch within Alphaproteobacteria. Nat. Ecol. Evol. 4, 1213–1219 (2020).PubMed 
Article 

Google Scholar 
Richards, T. A. & van der Giezen, M. Evolution of the Isd11–IscS complex reveals a single α-proteobacterial endosymbiosis for all eukaryotes. Mol. Biol. Evol. 23, 1341–1344 (2006).CAS 
PubMed 
Article 

Google Scholar 
Sapp, J. in Origin of Mitochondria and Hydrogenosomes (eds. Martin, W. F. & Müller, M.) 57–83 (Springer, 2007).Poole, A. M. & Gribaldo, S. Eukaryotic origins: how and when was the mitochondrion acquired? Cold Spring Harb. Perspect. Biol. 6, a015990 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Cavalier-Smith, T. in Endocytobiology II (eds Schenk, H. E. A. & Schwemmler, W. S.) 1027–1034 (de Gruyter, 1983).Martijn, J. & Ettema, T. J. G. From archaeon to eukaryote: the evolutionary dark ages of the eukaryotic cell. Biochem. Soc. Trans. 41, 451–457 (2013).CAS 
PubMed 
Article 

Google Scholar 
Canfield, D. E. Oxygen: a Four Billion Year History (Princeton Univ. Press, 2014).Holland, H. D. in Petrologic Studies: a Volume in Honor of A. F. Buddington (eds Engel, A. E. J., James, H. L. & Leonard, B. F.) 447–477 (Geological Society of America, 1962).Cloud, P. E. Jr. Significance of the Gunflint (Precambrian) microflora: photosynthetic oxygen may have had important local effects before becoming a major atmospheric gas. Science 148, 27–35 (1965).PubMed 
Article 

Google Scholar 
Rivera, M. C. & Lake, J. A. The ring of life provides evidence for a genome fusion origin of eukaryotes. Nature 431, 152–155 (2004).CAS 
PubMed 
Article 

Google Scholar 
Pisani, D., Cotton, J. A. & McInerney, J. O. Supertrees disentangle the chimerical origin of eukaryotic genomes. Mol. Biol. Evol. 24, 1752–1760 (2007).CAS 
PubMed 
Article 

Google Scholar 
Esser, C., Martin, W. & Dagan, T. The origin of mitochondria in light of a fluid prokaryotic chromosome model. Biol. Lett. 3, 180–184 (2007).CAS 
PubMed 
Article 

Google Scholar  More

Bach, L. T. et al. Testing the climate intervention potential of ocean afforestation using the Great Atlantic Sargassum Belt. Nat. Commun. 12, 2556 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
N‘Yeurt, A. D. R., Chynoweth, D. P., Capron, M. E., Stewart, J. R. & Hasan, M. A. Negative carbon via ocean afforestation. Process Saf. Environ. Prot. 90, 467–474 (2012).Article 
CAS 

Google Scholar 
Duarte, C. M., Bruhn, A. & Krause-Jensen, D. A seaweed aquaculture imperative to meet global sustainability targets. Nat. Sustain. 5, 185–193 (2022).Article 

Google Scholar 
Woody, T. Seaweed ‘forests’ can help fight climate change. National Geographic https://www.nationalgeographic.co.uk/environment-and-conservation/2019/08/seaweed-forests-can-help-fight-climate-change (2019).Godin, M. The ocean farmers trying to save the world with seaweed. Time https://time.com/5848994/seaweed-climate-change-solution/ (2020).Marshall, M. Kelp is coming: how seaweed could prevent catastrophic climate change. New Scientist https://www.newscientist.com/article/mg24632821-100-kelp-is-coming-how-seaweed-could-prevent-catastrophic-climate-change/ (2020).Bever, F. ‘Run the oil industry in reverse’: fighting climate change by farming kelp. NPR https://www.npr.org/2021/03/01/970670565/run-the-oil-industry-in-reverse-fighting-climate-change-by-farming-kelp (2021).Running Tide. https://www.runningtide.com/ (2022).IPCC: Summary for Policymakers. In Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).IPCC: Summary for Policymakers. In Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press) (in the press).GESAMP. High Level Review of a Wide Range of Proposed Marine Geoengineering Techniques (eds Boyd, P. W. & Vivian, C. M. G.) GESAMP Working Group 41 (International Maritime Organization, 2019).Boyd, P. & Vivian, C. Should we fertilize oceans or seed clouds? No one knows. Nature 570, 155–157 (2019).CAS 
PubMed 
Article 

Google Scholar 
Law, C. S. Predicting and monitoring the impact of large-scale iron fertilisation on marine trace gas emissions. Mar. Ecol. Prog. Ser. 364, 283–288 (2008).CAS 
Article 

Google Scholar 
Russell, L. M. et al. Ecosystem impacts of geoengineering: a review for developing a science plan. Ambio 41, 350–369 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Costello, C., Fries, L. & Gaines, S. Transformational opportunities in ocean-based food & nutrition. Zenodo https://zenodo.org/record/4646319#.YkBFxhPMLAw (2021).Jouffray, J.-B., Blasiak, R., Norström, A. V., Österblom, H. & Nyström, M. The blue acceleration: the trajectory of human expansion into the ocean. One Earth 2, 43–54 (2020).Article 

Google Scholar 
Cullen, J. J. & Boyd, P. W. Predicting and verifying the intended and uninterested consequence of large-scale iron fertilization. Mar. Ecol. Prog. Ser. 364, 295–301 (2008).CAS 
Article 

Google Scholar 
Bach, L. T., Gill, S. J., Rickaby, R. E. M., Gore, S. & Renforth, P. CO2 removal with enhanced weathering and ocean alkalinity enhancement: potential risks and co-benefits for marine pelagic ecosystems. Front. Clim. https://doi.org/10.3389/fclim.2019.00007 (2019).Moore, C. M. et al. Processes and patterns of oceanic nutrient limitation. Nat. Geosci. 6, 701–710 (2013).CAS 
Article 

Google Scholar 
Suchet, P. A., Probst, J.-L. & Ludwig, L. Worldwide distribution of continental rock lithology: implications for the atmospheric/soil CO2 uptake by continental weathering and alkalinity river transport to the oceans. Glob. Biogeochem. Cycles 17, 1038 (2003).
Google Scholar 
Macreadie, P. I. et al. The future of blue carbon science. Nat. Commun. 10, 3998 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fraser, C. I., Nikula, R. & Waters, J. M. Oceanic rafting by a coastal community. Proc. Biol. Sci. 278, 649–655 (2011).PubMed 

Google Scholar 
Fraser, C. I., Davies, I. D., Bryant, D. & Waters, J. M. How disturbance and dispersal influence intraspecific structure. J. Ecol. 106, 1298–1306 (2018).Article 

Google Scholar 
Fraser, C. I. et al. Antarctica’s ecological isolation will be broken by storm-driven dispersal and warming. Nat. Clim. Change 8, 704–708 (2018).Article 

Google Scholar 
Chung, I. K., Beardall, J., Mehta, S., Sahoo, D. & Stojkovic, S. Using marine macroalgae for carbon sequestration: a critical appraisal. J. Appl. Phycol. 23, 877–886 (2011).CAS 
Article 

Google Scholar 
Krause-Jensen, D. & Duarte, C. M. Substantial role of macroalgae in marine carbon sequestration. Nat. Geosci. 9, 737–742 (2016).CAS 
Article 

Google Scholar 
Hurd, C. L. et al. Forensic carbon accounting: assessing the role of seaweeds for carbon sequestration. J. Phycol., https://doi.org/10.1111/jpy.13249 (2022).Stripe commits $8M to six new carbon removal companies. Stripe https://stripe.com/newsroom/news/spring-21-carbon-removal-purchases (2021).General application. Stripe https://github.com/stripe/carbon-removal-source-materials/blob/master/Project%20Applications/Spring2021/Running%20Tide%20-%20Stripe%20Spring21%20CDR%20Purchase%20Application.pdf (2021).Coston-Clements, L. Utilization of the Sargassum Habitat by Marine Invertebrates and Vertebrates: a Review. NOAA Technical Memorandum NMFS-SEFSC, 296 (U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southeast Fisheries Science Center & Beaufort Laboratory, 1991).Egan, S. et al. The seaweed holobiont: understanding seaweed–bacteria interactions. FEMS Microbiol. Rev. 37, 462–476 (2013).CAS 
PubMed 
Article 

Google Scholar 
Califano, G., Kwantes, M., Abreu, M. H., Costa, R. & Wichard, T. Cultivating the macroalgal holobiont: effects of integrated multi-trophic aquaculture on the microbiome of Ulva rigida (Chlorophyta)Front. Mar. Sci. 7, 52 (2020).Article 

Google Scholar 
Selvarajan, R. et al. Distribution, interaction and functional profiles of epiphytic bacterial communities from the rocky intertidal seaweeds, South Africa. Sci. Rep. 9, 19835 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bonthond, G. et al. The role of host promiscuity in the invasion process of a seaweed holobiont. ISME J. 15, 1668–1679 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, M. et al. The great Atlantic Sargassum belt. Science 365, 83–87 (2019).CAS 
PubMed 
Article 

Google Scholar 
Johns, E. M. et al. The establishment of a pelagic Sargassum population in the tropical Atlantic: biological consequences of a basin-scale long distance dispersal event. Prog. Oceanogr. 182, 102269 (2020).Article 

Google Scholar 
Martiny, A. C. et al. Biogeochemical controls of surface ocean phosphate. Sci. Adv. 5, eaax0341 (2019).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Harrison, P. J., Druehl, L. D., Lloyd, K. E. & Thompson, P. A. Nitrogen uptake kinetics in three year-classes of Laminaria groenlandica (Laminariales: Phaeophyta). Mar. Biol. 93, 29–35 (1986).CAS 
Article 

Google Scholar 
Hurd, C. L. & Dring, M. L. Phosphate uptake by intertidal algae in relation to zonation and season. Mar. Biol. 107, 281–289 (1990).Article 

Google Scholar 
Ohtake, M. et al. Growth and nutrient uptake characteristics of Sargassum macrocarpum cultivated with phosphorus-replete wastewater. Aquat. Bot. 163, 103208 (2020).Article 

Google Scholar 
MacFarlane, J. J. & Raven, J. A. C, N and P nutrition of Lemanea mamillosa Kütz. (Batrachospermales, Rhodophyta) in the Dighty Burn, Angus, U.K. Plant Cell Environ. 13, 1–13 (1990).CAS 
Article 

Google Scholar 
Wu, J., Keller, D. P. & Oschlies, A. Carbon dioxide removal via macroalgae open-ocean mariculture and sinking: an Earth system modeling study. Preprint at Earth System Dynamics Discuss https://doi.org/10.5194/esd-2021-104 (2022).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).CAS 
Article 

Google Scholar 
Chapman, A. R. O. & Craigie, J. S. Seasonal growth in Laminaria longicruris: relations with dissolved inorganic nutrients and internal reserves of nitrogen. Mar. Biol. 40, 197–205 (1977).CAS 
Article 

Google Scholar 
Dutkiewicz, S., Scott, J. R. & Follows, M. J. Winners and losers: ecological and biogeochemical changes in a warming ocean. Glob. Biogeochem. Cycles 27, 463–477 (2013).CAS 
Article 

Google Scholar 
Thomas, M. K. et al. Temperature–nutrient interactions exacerbate sensitivity to warming in phytoplankton. Glob. Change Biol. 2, 3269–3280 (2017).Article 

Google Scholar 
Lapointe, B. E. et al. Nutrient content and stoichiometry of pelagic Sargassum reflects increasing nitrogen availability in the Atlantic Basin. Nat. Commun. 12, 3060 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fan, W. et al. A sea trial of enhancing carbon removal from Chinese coastal waters by stimulating seaweed cultivation through artificial upwelling. Appl. Ocean Res. 101, 102260 (2020).Article 

Google Scholar 
Karl, D. M. & Letelier, R. M. Nitrogen fixation-enhanced carbon sequestration in low nitrate, low chlorophyll seascapes. Mar. Ecol. Prog. Ser. 364, 257–268 (2008).CAS 
Article 

Google Scholar 
Oschlies, A. S., Pahlow, M., Yool, A. & Matear, R. Climate engineering by artificial ocean upwelling: channelling the sorcerer’s apprentice. Geophys. Res. Lett. 37, L04701 (2010).Article 
CAS 

Google Scholar 
Thornton, D. C. O. Dissolved organic matter (DOM) release by phytoplankton in the contemporary and future ocean. Eur. J. Phycol. 49, 20–46 (2014).CAS 
Article 

Google Scholar 
Morán, X. A. G., Sebastián, M., Pedrós-Alió, C. & Estrada, M. Response of Southern Ocean phytoplankton and bacterioplankton production to short-term experimental warming. Limnol. Oceanogr. 51, 1791–1800 (2006).Article 

Google Scholar 
Marañón, E., Cermeño, P., Fernández, E., Rodríguez, J. & Zabala, L. Significance and mechanisms of photosynthetic production of dissolved organic carbon in a coastal eutrophic ecosystem. Limnol. Oceanogr. 49, 1652–1666 (2004).Article 

Google Scholar 
Paine, E. R., Schmid, M., Boyd, P. W., Diaz-Pulido, G. & Hurd, C. L. Rate and fate of dissolved organic carbon release by seaweeds: a missing link in the coastal ocean carbon cycle. J. Phycol. 57, 1375–1391 (2021).CAS 
PubMed 
Article 

Google Scholar 
Brylinsky, M. Release of dissolved organic matter by some marine macrophytes. Mar. Biol. 39, 213–220 (1977).Article 

Google Scholar 
Sieburth, J. M. Studies on algal substances in the sea. III. The production of extracellular organic matter by littoral marine algae. J. Exp. Mar. Biol. Ecol. 3, 290–309 (1969).CAS 
Article 

Google Scholar 
Hanson, R. B. Pelagic Sargassum community metabolism: carbon and nitrogen. J. Exp. Mar. Biol. Ecol. 29, 107–118 (1977).CAS 
Article 

Google Scholar 
Zark, M., Riebesell, U. & Dittmar, T. Effects of ocean acidification on marine dissolved organic matter are not detectable over the succession of phytoplankton blooms. Sci. Adv. 1, e1500531 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Zhang, Y., Liu, X., Wang, M. & Qin, B. Compositional differences of chromophoric dissolved organic matter derived from phytoplankton and macrophytes. Org. Geochem. 55, 26–37 (2013).Article 
CAS 

Google Scholar 
Hulatt, C. J., Thomas, D. N., Bowers, D. G., Norman, L. & Zhang, C. Exudation and decomposition of chromophoric dissolved organic matter (CDOM) from some temperate macroalgae. Estuar. Coast. Shelf Sci. 84, 147–153 (2009).CAS 
Article 

Google Scholar 
Liu, S., Trevathan-Tackett, S. M., Ewers Lewis, C. J., Huang, X. & Macreadie, P. I. Macroalgal blooms trigger the breakdown of seagrass blue carbon. Environ. Sci. Technol. 54, 14750–14760 (2020).CAS 
PubMed 
Article 

Google Scholar 
Vieira, H. C. et al. Ocean warming may enhance biochemical alterations induced by an invasive seaweed exudate in the mussel Mytilus galloprovincialis. Toxics 9, 121 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brooks, S. D. & Thornton, D. C. O. Marine aerosols and clouds. Ann. Rev. Mar. Sci. 10, 289–313 (2018).PubMed 
Article 

Google Scholar 
Lewis, M. R., Carr, M.-E., Feldman, G. C., Esaias, W. & McClain, C. Influence of penetrating solar radiation on the heat budget of the equatorial Pacific Ocean. Nature 347, 543–545 (1990).Article 

Google Scholar 
Morel, A. Optical modeling of the upper ocean in relation to its biogenous matter content (case-I waters). J. Geophys. Res. 93, 10749–10768 (1988).Article 

Google Scholar 
Park, J.-Y., Kug, J.-S., Bader, J., Rolph, R. & Kwon, M. Amplified Arctic warming by phytoplankton under greenhouse warming. Proc. Natl Acad. Sci. USA 112, 5921–5926 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Denaro, G. et al. Dynamics of two picophytoplankton groups in Mediterranean Sea: analysis of the deep chlorophyll maximum by a stochastic advection-reaction-diffusion model. PLoS ONE 8, e66765 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kavanaugh, M. T. et al. Experimental assessment of the effects of shade on an intertidal kelp: do phytoplankton blooms inhibit growth of open-coast macroalgae? Limnol. Oceanogr. 54, 276–288 (2009).Article 

Google Scholar 
Omand, M. M., Steinberg, D. K. & Stamies, K. Cloud shadows drive vertical migrations of deep-dwelling marine life. Proc. Natl Acad. Sci. USA 118, e2022977118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bach, L. T. & Boyd, P. W. Seeking natural analogs to fast-forward the assessment of marine CO2 removal. Proc. Natl Acad. Sci. USA 118, e2106147118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Donk, E. & van de Bund, W. J. Impact of submerged macrophytes including charophytes on phyto- and zooplankton communities: allelopathy versus other mechanisms. Aquat. Bot. 72, 261–274 (2002).Article 

Google Scholar 
Jin, Q., Dong, S. & Wang, C. Allelopathic growth inhibition of Prorocentrum micans (Dinophyta) by Ulva pertusa and Ulva linza (Chlorophyta) in laboratory cultures. Eur. J. Phycol. 40, 31–37 (2005).Article 

Google Scholar 
Wallace, R. B. & Gobler, C. J.Factors controlling blooms of microalgae and macroalgae (Ulva rigida) in a eutrophic, urban estuary: Jamaica Bay, NY, USA. Estuaries Coast 38, 519–533 (2015).CAS 
Article 

Google Scholar 
Tang, Y. Z. & Gobler, C. J. The green macroalga, Ulva lactuca, inhibits the growth of seven common harmful algal bloom species via allelopathy. Harmful Algae 10, 480–488 (2011).Article 

Google Scholar 
Cagle, S. E., Roelke, D. L. & Muhl, R. W. Allelopathy and micropredation paradigms reconcile with system stoichiometry. Ecosphere 12, e03372 (2021).Article 

Google Scholar 
Hein, M., Pedersen, M. F. & Sand-Jensen, K. Size-dependent nitrogen uptake in micro- and macroalgae. Mar. Ecol. Prog. Ser. 118, 247–253 (1995).Article 

Google Scholar 
Stevens, C. L., Hurd, C. L. & Smith, M. J. Water motion relative to subtidal kelp fronds. Limnol. Oceanogr. 46, 668–678 (2001).Article 

Google Scholar 
Raut, Y., Morando, M. & Capone, D. G. Diazotrophic macroalgal associations with living and decomposing Sargassum. Front. Microbiol. 9, 3127 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Villareal, T. A., Woods, S., Moore, J. K. & CulverRymsza, K. Vertical migration of Rhizosolenia mats and their significance to NO3− fluxes in the central North Pacific gyre. J. Plankton Res. 18, 1103–1121 (1996).Article 

Google Scholar 
Gachon, C. M. M., Sime-Ngando, T., Strittmatter, M., Chambouvet, A. & Kim, G. H. Algal diseases: spotlight on a black box. Trends Plant Sci. 15, 633–640 (2010).CAS 
PubMed 
Article 

Google Scholar 
Sánchez-Baracaldo, P., Bianchini, G., Wilson, J. D. & Knoll, A. H. Cyanobacteria and biogeochemical cycles through Earth history. Trends Microbiol. 30, 143–157 (2022).PubMed 
Article 
CAS 

Google Scholar 
Thiel, M. & Gutow, L. in Oceanography and Marine Biology: an Annual Review Vol. 43 (eds Gibson, R. et al.) 279–418 (Taylor & Francis, 2005).Rech, S., Borrell Pichs, Y. J. & García-Vazquez, E. Anthropogenic marine litter composition in coastal areas may be a predictor of potentially invasive rafting fauna. PLoS ONE 13, e0191859 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Food and Agriculture Organization (FAO) of the United Nations. The State of World Fisheries and Aquaculture 2020: Sustainability in Action (FAO, 2020).Schell, J. M., Goodwin, D. S. & Siuda, A. N. S. Recent Sargassum inundation events in the Caribbean: shipboard observations reveal dominance of a previously rare form. Oceanography 28, 8–10 (2015).Article 

Google Scholar 
Rodríguez-Martínez, R. E. et al. Element concentrations in pelagic Sargassum along the Mexican Caribbean coast in 2018–2019. Peer J. 8, e8667 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Flannery, T. How farming giant seaweed can feed fish and fix the climate. The Conversation Trust https://theconversation.com/how-farming-giant-seaweed-can-feed-fish-and-fix-the-climate-81761 (2017).GESAMP. Methodology for the Evaluation of Ballast Water Management Systems Using Active Substances. GESAMP No. 101 (eds Linders, J. & Dock, A.) (International Maritime Organization, 2019).Lenton, A., Boyd, P. W., Thatcher, M. & Emmerson, K. M. Foresight must guide geoengineering research and development. Nat. Clim. Change 9, 342 (2019).Article 

Google Scholar 
Sumaila, U. R. Financing a sustainable ocean economy. Nat. Commun. 12, 3259 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rockström, J. et al. Planetary boundaries: exploring the safe operating space for humanity. Ecol. Soc. 14, 32 (2009).Article 

Google Scholar 
Rech, S., Salmina, S., Borrell Pichs, Y. J. & García-Vazquez, E. Dispersal of alien invasive species on anthropogenic litter from European mariculture areas. Mar. Pollut. Bull. 131, 10–16 (2018).CAS 
PubMed 
Article 

Google Scholar 
Therriault, T. W. et al. The invasion risk of species associated with Japanese tsunami marine debris in Pacific North America and Hawaii. Mar. Pollut. Bull. 132, 82–89 (2018).CAS 
PubMed 
Article 

Google Scholar 
Miller, J. A., Carlton, J. T., Chapman, J. W., Geller, J. B. & Ruiz, G. M. Transoceanic dispersal of the mussel Mytilus galloprovincialis on Japanese tsunami marine debris: an approach for evaluating rafting of a coastal species at sea. Mar. Pollut. Bull. 132, 60–69 (2018).CAS 
PubMed 
Article 

Google Scholar 
Carlton, J. T. et al. Tsunami-driven rafting: transoceanic species dispersal and implications for marine biogeography. Science 357, 1402–1406 (2017).CAS 
PubMed 
Article 

Google Scholar 
Hunt, G. L. Jr et al. Advection in polar and sub-polar environments: impacts on high latitude marine ecosystems. Prog. Oceanogr. 149, 40–81 (2016).Article 

Google Scholar 
Hallegraeff, G. M. & Bolch, C. J. Transport of dinoflagellate cysts in ship’s ballast water: implications for plankton biogeography and aquaculture. J. Plankton Res. 14, 1067–1084 (1992).Article 

Google Scholar 
Russell, L. K., Hepburn, C. D., Hurd, C. L. & Stuart, M. D. The expanding range of Undaria pinnatifida in southern New Zealand: distribution, dispersal mechanisms and the invasion of wave-exposed environments. Biol. Invasions 10, 103–115 (2008).Article 

Google Scholar 
Uwai, S. et al. Genetic diversity in Undaria pinnatifida (Laminariales, Phaeophyceae) deduced from mitochondria genes—origins and succession of introduced populations. Phycologia 45, 687–695 (2006).Article 

Google Scholar  More

Notomi, T. et al. Loop-mediated isothermal amplification of DNA. Nucleic Acids Res. https://doi.org/10.1093/nar/28.12.e63 (2000).Article 

Google Scholar 
Nagamine, K., Hase, T. & Notomi, T. Accelerated reaction by loop-mediated isothermal amplification using loop primers. Mol. Cell. Probes 16, 223–229. https://doi.org/10.1006/mcpr.2002.0415 (2002).CAS 
Article 

Google Scholar 
Nagamine, K., Watanabe, K., Ohtsuka, K., Hase, T. & Notomi, T. Loop-mediated isothermal amplification reaction using a nondenatured template. Clin. Chem. 47, 1742–1743 (2001).CAS 
Article 

Google Scholar 
Thai, H. T. C. et al. Development and evaluation of a novel loop-mediated isothermal amplification method for rapid detection of severe acute respiratory syndrome coronavirus. J. Clin. Microbiol. 42, 1956–1961. https://doi.org/10.1128/jcm.42.5.1956-1961.2004 (2004).CAS 
Article 

Google Scholar 
Geojith, G., Dhanasekaran, S., Chandran, S. P. & Kenneth, J. Efficacy of loop mediated isothermal amplification (LAMP) assay for the laboratory identification of Mycobacterium tuberculosis isolates in a resource limited setting. J. Microbiol. Methods 84, 71–73. https://doi.org/10.1016/j.mimet.2010.10.015 (2011).CAS 
Article 

Google Scholar 
Saengsawang, N. et al. Development of a fluorescent distance-based paper device using loop-mediated isothermal amplification to detect Escherichia coli in urine. Analyst 145, 8077–8086. https://doi.org/10.1039/d0an01306d (2020).CAS 
Article 

Google Scholar 
Yoshikawa, R. et al. Development and evaluation of a rapid and simple diagnostic assay for COVID-19 based on loop-mediated isothermal amplification. Plos Neglect. Trop. Dis. 14, 14. https://doi.org/10.1371/journal.pntd.000885 (2021).Article 

Google Scholar 
Kim, J. et al. Development and evaluation of a multiplex loop-mediated isothermal amplification (LAMP) assay for differentiation of Mycobacterium tuberculosis and non-tuberculosis mycobacterium in clinical samples. PLoS ONE 16, 11. https://doi.org/10.1371/journal.pone.0244753 (2021).CAS 
Article 

Google Scholar 
Hongjaisee, S. et al. Rapid visual detection of hepatitis C virus using a reverse transcription loop-mediated isothermal ampli fi cation assay. Int. J. Infect. Dis. 102, 440–445. https://doi.org/10.1016/j.ijid.2020.10.082 (2021).CAS 
Article 

Google Scholar 
Niessen, L. & Vogel, R. F. Detection of Fusarium graminearum DNA using a loop-mediated isothermal amplification (LAMP) assay. Int. J. Food Microbiol. 140, 183–191. https://doi.org/10.1016/j.ijfoodmicro.2010.03.036 (2010).CAS 
Article 

Google Scholar 
Ren, W. C., Liu, N. & Li, B. H. Development and application of a LAMP method for rapid detection of apple blotch caused by Marssonina coronaria. Crop Prot. 141, 6. https://doi.org/10.1016/j.cropro.2020.105452 (2021).CAS 
Article 

Google Scholar 
Kong, G. H. et al. Detection of Peronophythora litchii on lychee by loop-mediated isothermal amplification assay. Crop Prot. 139, 6. https://doi.org/10.1016/j.cropro.2020.105370 (2021).CAS 
Article 

Google Scholar 
Zhou, Q. J. et al. Simultaneous detection of multiple bacterial and viral aquatic pathogens using a fluorogenic loop-mediated isothermal amplification-based dual-sample microfluidic chip. J. Fish Dis. https://doi.org/10.1111/jfd.13325 (2020).Article 

Google Scholar 
Huang, H. L. et al. Molecular method for rapid detection of the red tide dinoflagellate Karenia mikimotoi in the coastal region of Xiangshan Bay, China. J. Microbiol. Methods 168, 7. https://doi.org/10.1016/j.mimet.2019.105801 (2020).CAS 
Article 

Google Scholar 
Sridapan, T. et al. Rapid detection of Clostridium perfringens in food by loop-mediated isothermal amplification combined with a lateral flow biosensor. PLoS ONE 16, 14. https://doi.org/10.1371/journal.pone.0245144 (2021).CAS 
Article 

Google Scholar 
Xiong, X. et al. Using real time fluorescence loop-mediated isothermal amplification for rapid species authentication of Atlantic salmon (Salmo salar). J. Food Compos. Anal. 95, 7. https://doi.org/10.1016/j.jfca.2020.103659 (2021).CAS 
Article 

Google Scholar 
Huang, C. G., Hsu, J. C., Haymer, D. S., Lin, G. C. & Wu, W. J. Rapid identification of the Mediterranean fruit fly (Diptera: Tephritidae) by loop-mediated isothermal amplification. J. Econ. Entomol. 102, 1239–1246 (2009).CAS 
Article 

Google Scholar 
Ide, T., Kanzaki, N., Ohmura, W. & Okabe, K. Molecular identification of an invasive wood-boring insect Lyctus brunneus (Coleoptera: Bostrichidae: Lyctinae) using frass by loop-mediated isothermal amplification and nested PCR assays. J. Econ. Entomol. 109, 1410–1414. https://doi.org/10.1093/jee/tow030 (2016).CAS 
Article 

Google Scholar 
Stainton, K., Hall, J., Budge, G. E., Boonham, N. & Hodgetts, J. Rapid molecular methods for in-field and laboratory identification of the yellow-legged Asian hornet (Vespa velutina nigrithorax). J. Appl. Entomol. 142, 610–616. https://doi.org/10.1111/jen.12506 (2018).CAS 
Article 

Google Scholar 
Agarwal, A., Cunningham, J. P., Valenzuela, I. & Blacket, M. J. A diagnostic LAMP assay for the destructive grapevine insect pest, phylloxera (Daktulosphaira vitifoliae). Sci. Rep. 10, 10. https://doi.org/10.1038/s41598-020-77928-9 (2020).CAS 
Article 

Google Scholar 
Rizzo, D. et al. Molecular identification of Anoplophora glabripennis (Coleoptera: Cerambycidae) from frass by loop-mediated isothermal amplification. J. Econ. Entomol. 113, 2911–2919. https://doi.org/10.1093/jee/toaa206 (2020).CAS 
Article 

Google Scholar 
Hsieh, C. H., Wang, H. Y., Chen, Y. F. & Ko, C. C. Loop-mediated isothermal amplification for rapid identification of biotypes B and Q of the globally invasive pest Bemisia tabaci, and studying population dynamics. Pest Manag. Sci. 68, 1206–1213. https://doi.org/10.1002/ps.3298 (2012).CAS 
Article 

Google Scholar 
Williams, M. R. et al. Isothermal amplification of environmental DNA (eDNA) for direct field-based monitoring and laboratory confirmation of Dreissena sp. PLoS ONE 12, 18. https://doi.org/10.1371/journal.pone.0186462 (2017).CAS 
Article 

Google Scholar 
Ponting, S., Tomkies, V. & Stainton, K. Rapid identification of the invasive small hive beetle (Aethina tumida) using LAMP. Pest Manag. Sci. 77, 1476–1481. https://doi.org/10.1002/ps.6168 (2020).CAS 
Article 

Google Scholar 
Davis, C. N. et al. Rapid detection of Galba truncatula in water sources on pasture-land using loop-mediated isothermal amplification for control of trematode infections. Parasites Vectors 13, 11. https://doi.org/10.1186/s13071-020-04371-0 (2020).CAS 
Article 

Google Scholar 
Carvalho, J. et al. Faster monitoring of the invasive alien species (IAS) Dreissena polymorpha in river basins through isothermal amplification. Sci. Rep. 11, 10. https://doi.org/10.1038/s41598-021-89574-w (2021).CAS 
Article 

Google Scholar 
Treguier, A. et al. Environmental DNA surveillance for invertebrate species: Advantages and technical limitations to detect invasive crayfish Procambarus clarkii in freshwater ponds. J. Appl. Ecol. 51, 871–879. https://doi.org/10.1111/1365-2664.12262 (2014).CAS 
Article 

Google Scholar 
Cai, W. et al. Using eDNA to detect the distribution and density of invasive crayfish in the Honghe-Hani rice terrace World Heritage site. PLoS ONE https://doi.org/10.1371/journal.pone.0177724 (2017).Article 

Google Scholar 
Wilcox, T. M. et al. Understanding environmental DNA detection probabilities: A case study using a stream-dwelling char Salvelinus fontinalis. Biol. Conserv. 194, 209–216. https://doi.org/10.1016/j.biocon.2015.12.023 (2016).Article 

Google Scholar 
Hunter, M. E., Ferrante, J. A., Meigs-Friend, G. & Ulmer, A. Improving eDNA yield and inhibitor reduction through increased water volumes and multi-filter isolation techniques. Sci. Rep. https://doi.org/10.1038/s41598-019-40977-w (2019).Article 

Google Scholar 
Twardochleb, L. A., Olden, J. D. & Larson, E. R. A global meta-analysis of the ecological impacts of nonnative crayfish. Freshw. Sci. 32, 1367–1382. https://doi.org/10.1899/12-203.1 (2013).Article 

Google Scholar 
Andruszkiewicz, A. E., Zhang, W. G. & Govindarajan, A. F. Environmental DNA shedding and decay rates from diverse animal forms and thermal regimes. Environ. DNA 3, 492–514. https://doi.org/10.1002/edn3.141 (2021).Article 

Google Scholar 
Stedtfeld, R. D. et al. Static self-directed sample dispensing into a series of reaction wells on a microfluidic card for parallel genetic detection of microbial pathogens. Biomed. Microdev. 17, 89. https://doi.org/10.1007/s10544-015-9994-1 (2015).CAS 
Article 

Google Scholar 
Koloren, Z., Sotiriadou, I. & Karanis, P. Investigations and comparative detection of Cryptosporidium species by microscopy, nested PCR and LAMP in water supplies of Ordu, Middle Black Sea, Turkey. Ann. Trop. Med. Parasitol. 105, 607–615. https://doi.org/10.1179/2047773211y.0000000011 (2011).CAS 
Article 

Google Scholar 
Sabike, I. I. et al. Use of direct LAMP screening of broiler fecal samples for Campylobacter jejuni and Campylobacter coli in the positive flock identification strategy. Front. Microbiol. 7, 1582. https://doi.org/10.3389/fmicb.2016.01582 (2016).Article 

Google Scholar 
Gahlawat, S. K., Ellis, A. E. & Collet, B. A sensitive loop-mediated isothermal amplification (LAMP) method for detection of Renibacterium salmoninarum, causative agent of bacterial kidney disease in salmonids. J. Fish Dis. 32, 491–497. https://doi.org/10.1111/j.1365-2761.2009.01005.x (2009).CAS 
Article 

Google Scholar 
Levy, J. et al. Methods for rapid and effective PCR-based detection of ‘Candidatus Liberibacter solanacearum’ from the insect vector Bactericera cockerelli: Streamlining the DNA extraction/purification process. J. Econ. Entomol. 106, 1440–1445. https://doi.org/10.1603/ec12419 (2013).CAS 
Article 

Google Scholar 
Kaneko, H., Kawana, T., Fukushima, E. & Suzutani, T. Tolerance of loop-mediated isothermal amplification to a culture medium and biological substances. J. Biochem. Biophys. Methods 70, 499–501. https://doi.org/10.1016/j.jbbm.2006.08.008 (2007).CAS 
Article 

Google Scholar 
Curtis, A. N., Tiemann, J. S., Douglass, S. A., Davis, M. A. & Larson, E. R. High stream flows dilute environmental DNA (eDNA) concentrations and reduce detectability. Divers. Distrib. 27, 1918–1931. https://doi.org/10.1111/ddi.13196 (2020).Article 

Google Scholar 
Mauvisseau, Q. et al. Environmental DNA as an efficient tool for detecting invasive crayfishes in freshwater ponds. Hydrobiologia 805, 163–175. https://doi.org/10.1007/s10750-017-3288-y (2018).CAS 
Article 

Google Scholar 
RStudioTeam. Boston (ed. PBC) (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

Google Scholar  More

Magallón, S., Sánchez-Reyes, L. L. & Gómez-Acevedo, S. L. Thirty clues to the exceptional diversification of flowering plants. Ann. Bot. 123, 491–503 (2019).PubMed 
Article 

Google Scholar 
Shi, J. J. & Rabosky, D. L. Speciation dynamics during the global radiation of extant bats. Evolution 69, 1528–1545 (2015).PubMed 
Article 

Google Scholar 
Nicolai, M. P. J. & Matzke, N. J. Trait-based range expansion aided in the global radiation of Crocodylidae. Glob. Ecol. Biogeogr. 28, 1244–1258 (2019).Article 

Google Scholar 
Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).Price, T. & others. Speciation in Birds (Roberts and Co., 2008).Moyle, R. G., Filardi, C. E., Smith, C. E. & Diamond, J. Explosive Pleistocene diversification and hemispheric expansion of a “great speciator”. Proc. Natl Acad. Sci. USA 106, 1863–1868 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Van Bocxlaer, I. et al. Gradual adaptation toward a range-expansion phenotype initiated the global radiation of toads. Science 327, 679–682 (2010).ADS 
PubMed 
Article 
CAS 

Google Scholar 
Phillimore, A. B. & Price, T. D. in Speciation and Patterns on Diversity (eds Butlin, R., Bridle, J. & Schluter, D.) Ch. 13 (Cambridge Univ. Press, 2009).Price, T. D. et al. Niche filling slows the diversification of Himalayan songbirds. Nature 509, 222–225 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Nosil, P. Ecological Speciation (Oxford Univ. Press, 2012).Naciri, Y. & Linder, H. P. The genetics of evolutionary radiations. Biol. Rev. Camb. Philos. Soc. 95, 1055–1072 (2020).Price, T. D. & Sol, D. Introduction: genetics of colonizing species. Am. Nat. 172, S1–S3 (2008).PubMed 
Article 

Google Scholar 
Schluter, D. The Ecology of Adaptive Radiation (Oxford Univ. Press, 2000).Gill, F. & Donsker, D. IOC world bird list (v 8.1). 2018. (2018).Del Hoyo, J., Del Hoyo, J., Elliott, A. & Sargatal, J. Handbook of the Birds of the World Vol. 1 (Lynx edicions, 1992).Cassey, P. Are there body size implications for the success of globally introduced land birds? Ecography 24, 413–420 (2001).Article 

Google Scholar 
Fristoe, T. S., Iwaniuk, A. N. & Botero, C. A. Big brains stabilize populations and facilitate colonization of variable habitats in birds. Nat. Ecol. Evol. 1, 1706–1715 (2017).PubMed 
Article 

Google Scholar 
Sayol, F. et al. Environmental variation and the evolution of large brains in birds. Nat. Commun. 7, 1–8 (2016).Article 
CAS 

Google Scholar 
Sol, D. Revisiting the cognitive buffer hypothesis for the evolution of large brains. Biol. Lett. 5, 130–133 (2009).PubMed 
Article 

Google Scholar 
Lefebvre, L. & Sol, D. Brains, lifestyles and cognition: are there general trends? Brain. Behav. Evol. 72, 135–144 (2008).PubMed 
Article 

Google Scholar 
Jønsson, K. A. et al. A supermatrix phylogeny of corvoid passerine birds (Aves: Corvides). Mol. Phylogenet. Evol. 94, 87–94 (2016).PubMed 
Article 

Google Scholar 
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).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Marki, P. Z. et al. Breeding system evolution influenced the geographic expansion and diversification of the core Corvoidea (Aves: Passeriformes). Evolution 69, 1874–1924 (2015).PubMed 
Article 

Google Scholar 
KessLer, J. E. Evolution of Corvids and their presence in the neogene and the quaternary in the Carpathian Basin. Ornis Hungarica 28, 121–168 (2020).Article 

Google Scholar 
Olson, S. L. & Rasmussen, P. C., others. Miocene and Pliocene birds from the Lee Creek Mine, North Carolina. Smithson Contrib. Paleobiol. 90, 233–365 (2001).
Google Scholar 
Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9, e89543 (2014).Alfaro, M. E. et al. Lineage-specific diversification rates and high turnover in the history of jawed vertebrates. Proc. Natl Acad. Sci. USA 106, 13410–13414 (2009).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rabosky, D. L., Donnellan, S. C., Grundler, M. & Lovette, I. J. Analysis and visualization of complex macroevolutionary dynamics: an example from Australian scincid lizards. Syst. Biol. 63, 610–627 (2014).PubMed 
Article 

Google Scholar 
Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Kulemeyer, C., Asbahr, K., Gunz, P., Frahnert, S. & Bairlein, F. Functional morphology and integration of corvid skulls-a 3D geometric morphometric approach. Front. Zool. 6, 2 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Zeffer, A., Johansson, L. C. & Marmebro, Å. Functional correlation between habitat use and leg morphology in birds (Aves). Biol. J. Linn. Soc. 79, 461–484 (2003).Article 

Google Scholar 
Wang, X., McGowan, A. J. & Dyke, G. J. Avian wing proportions and flight styles: first step towards predicting the flight modes of Mesozoic birds. PLoS ONE 6, e28672 (2011).Corbin, C. E., Lowenberger, L. K. & Gray, B. L. Linkage and trade-off in trophic morphology and behavioural performance of birds. Funct. Ecol. 29, 808–815 (2015).Article 

Google Scholar 
Kennedy, J. D. et al. The influence of wing morphology upon the dispersal, geographical distributions and diversification of the Corvides (Aves; Passeriformes). Proc. R. Soc. B Biol. Sci. 283, 20161922 (2016).Article 

Google Scholar 
Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evol. 4, 230–239 (2020).PubMed 
Article 

Google Scholar 
Clavel, J., Escarguel, G. & Merceron, G. mvMORPH: an R package for fitting multivariate evolutionary models to morphometric data. Methods in Ecology and Evolution 6, 1311–1319 (2015).Uyeda, J. C., Caetano, D. S. & Pennell, M. W. Comparative analysis of principal components can be misleading. Syst. Biol. 64, 677–689 (2015).CAS 
PubMed 
Article 

Google Scholar 
Leyequién, E., de Boer, W. F. & Cleef, A. Influence of body size on coexistence of bird species. Ecol. Res. 22, 735–741 (2007).Article 

Google Scholar 
Grant, P. R. Bill size, body size, and the ecological adaptations of bird species to competitive situations on islands. Syst. Biol. 17, 319–333 (1968).CAS 
Article 

Google Scholar 
Meiri, S. & Dayan, T. On the validity of Bergmann’s rule. J. Biogeogr. 30, 331–351 (2003).Article 

Google Scholar 
Friedman, N. R. et al. Evolution of a multifunctional trait: shared effects of foraging ecology and thermoregulation on beak morphology, with consequences for song evolution. Proc. R. Soc. B 286, 20192474 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Friedman, N. R., Harmáčková, L., Economo, E. P. & Remeš, V. Smaller beaks for colder winters: Thermoregulation drives beak size evolution in Australasian songbirds. Evolution 71, 2120–2129 (2017).PubMed 
Article 

Google Scholar 
Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 1–9 (2020).Article 
CAS 

Google Scholar 
Rabosky, D. L. et al. BAMM tools: an R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5, 701–707 (2014).Article 

Google Scholar 
Thomas, G. H. & Freckleton, R. P. MOTMOT: models of trait macroevolution on trees. Methods Ecol. Evol. 3, 145–151 (2012).CAS 
Article 

Google Scholar 
O’Meara, B. C., Ané, C., Sanderson, M. J. & Wainwright, P. C. Testing for different rates of continuous trait evolution using likelihood. Evolution 60, 922–933 (2006).PubMed 
Article 

Google Scholar 
Harmon, L. J., Schulte, J. A., Larson, A. & Losos, J. B. Tempo and mode of evolutionary radiation in iguanian lizards. Science 301, 961–964 (2003).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Slater, G. J., Price, S. A., Santini, F. & Alfaro, M. E. Diversity versus disparity and the radiation of modern cetaceans. Proc. R. Soc. B Biol. Sci. 277, 3097–3104 (2010).Article 

Google Scholar 
Sullivan, B. L. et al. eBird: A citizen-based bird observation network in the biological sciences. Biol. Conserv. 142, 2282–2292 (2009).Article 

Google Scholar 
Broennimann, O. et al. Measuring ecological niche overlap from occurrence and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497 (2012).Article 

Google Scholar 
Heinrich, B. Ravens in Winter (Simon and Schuster, 2014).Taylor, A. H., Hunt, G. R., Medina, F. S. & Gray, R. D. Do new Caledonian crows solve physical problems through causal reasoning? Proc. R. Soc. B Biol. Sci. 276, 247–254 (2009).CAS 
Article 

Google Scholar 
Lefebvre, L., Reader, S. M. & Sol, D. Brains, innovations and evolution in birds and primates. Brain. Behav. Evol. 63, 233–246 (2004).PubMed 
Article 

Google Scholar 
Rensch, B. Increase of learning capability with increase of brain-size. Am. Nat. 90, 81–95 (1956).Article 

Google Scholar 
Roth, T. C., LaDage, L. D., Freas, C. A. & Pravosudov, V. V. Variation in memory and the hippocampus across populations from different climates: a common garden approach. Proc. R. Soc. B Biol. Sci. 279, 402–410 (2012).Article 

Google Scholar 
Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl Acad. Sci. USA 113, 7255–7260 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Sayol, F., Lefebvre, L. & Sol, D. Relative brain size and its relation with the associative pallium in birds. Brain. Behav. Evol. 87, 69–77 (2016).PubMed 
Article 

Google Scholar 
Garcia-Porta, J. & Ord, T. J. Key innovations and island colonization as engines of evolutionary diversification: a comparative test with the Australasian diplodactyloid geckos. J. Evol. Biol. 26, 2662–2680 (2013).Losos, J. B. & Ricklefs, R. E. Adaptation and diversification on islands. Nature 457, 830–836 (2009).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Stuart, Y. E. & Losos, J. B. Ecological character displacement: glass half full or half empty? Trends Ecol. Evol. 28, 402–408 (2013).PubMed 
Article 

Google Scholar 
Jenkins, D. G. et al. Does size matter for dispersal distance? Glob. Ecol. Biogeogr. 16, 415–425 (2007).Article 

Google Scholar 
Sol, D. et al. Evolutionary divergence in brain size between migratory and resident birds. PLoS ONE 5, e9617 (2010).Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).PubMed 
Article 

Google Scholar 
Sayol, F., Sol, D. & Pigot, A. L. Brain size and life history interact to predict urban tolerance in birds. Front. Ecol. Evol. 8, 58 (2020).Article 

Google Scholar 
Baltensperger, A. P. et al. Seasonal observations and machine-learning-based spatial model predictions for the common raven (Corvus corax) in the urban, sub-arctic environment of Fairbanks, Alaska. Polar Biol. 36, 1587–1599 (2013).Article 

Google Scholar 
Kövér, L. et al. Recent colonization and nest site selection of the Hooded Crow (Corvus corone cornix L.) in an urban environment. Landsc. Urban Plan. 133, 78–86 (2015).Article 

Google Scholar 
Oostra, V., Saastamoinen, M., Zwaan, B. J. & Wheat, C. W. Strong phenotypic plasticity limits potential for evolutionary responses to climate change. Nat. Commun. 9, 1–11 (2018).CAS 
Article 

Google Scholar 
Dukas, R. & Ratcliffe, J. M. Cognitive Ecology II (University of Chicago Press, 2009).Huey, R. B., Hertz, P. E. & Sinervo, B. Behavioral drive versus behavioral inertia in evolution: a null model approach. Am. Nat. 161, 357–366 (2003).PubMed 
Article 

Google Scholar 
Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T. & Gaitán-Espitia, J. D. Beyond buying time: the role of plasticity in phenotypic adaptation to rapid environmental change. Philos. Trans. R. Soc. Lond. B Biol. Sci. 374, 20180174 (2019).Aboitiz, F. Behavior, body types and the irreversibility of evolution. Acta Biotheor. 38, 91–101 (1990).Wcislo, W. T. Behavioral environments and evolutionary change. Annu. Rev. Ecol. Syst. 20, 137–169 (1989).Article 

Google Scholar 
Sol, D., Stirling, D. G. & Lefebvre, L. Behavioral drive or behavioral inhibition in evolution: subspecific diversification in Holarctic passerines. Evolution 59, 2669–2677 (2005).PubMed 
Article 

Google Scholar 
Mayr, E., Mayr, E., Mayr, E. & Mayr, E. Animal Species and Evolution Vol. 797 (Belknap Press of Harvard University Press, 1963).Mayr, E. The emergence of evolutionary novelties. Evol. Darwin 1, 349–380 (1960).
Google Scholar 
Hardy, A. C. The Living Stream: Evolution and Man (Harper & Row, 1967).Wyles, J. S., Kunkel, J. G. & Wilson, A. C. Birds, behavior, and anatomical evolution. Proc. Natl Acad. Sci. USA 80, 4394–4397 (1983).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Plotkin, H. C. The Role of Behavior in Evolution (MIT press, 1988).Lande, R. Models of speciation by sexual selection on polygenic traits. Proc. Natl Acad. Sci. USA 78, 3721–3725 (1981).ADS 
MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
West-Eberhard, M. J. Phenotypic plasticity and the origins of diversity. Annu. Rev. Ecol. Syst. 20, 249–278 (1989).Article 

Google Scholar 
Sol, D. & Price, T. D. Brain size and the diversification of body size in birds. Am. Nat. 172, 170–177 (2008).PubMed 
Article 

Google Scholar 
Sayol, F., Lapiedra, O., Ducatez, S. & Sol, D. Larger brains spur species diversification in birds. Evolution 73, 2085–2093 (2019).PubMed 
Article 

Google Scholar 
Abascal, F., Zardoya, R. & Telford, M. J. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 38, W7–W13 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Bouckaert, R. et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, e1003537 (2014).Bouckaert, R., Alvarado-Mora, M. V. & Pinho, J. R., others. Evolutionary rates and HBV: issues of rate estimation with Bayesian molecular methods. Antivir. Ther. 18, 497–503 (2013).PubMed 
Article 

Google Scholar 
Rambaut, A. & Drummond, A. J. Tracer v1. 4. (2007).Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 
PubMed 
Article 

Google Scholar 
Louca, S. & Louca, M. S. Package ‘castor’. (2017).Rasband, W. S. et al. ImageJ. (1997).Rohlf, F. J. & Slice, D. Extensions of the Procrustes method for the optimal superimposition of landmarks. Syst. Biol. 39, 40–59 (1990).
Google Scholar 
Adams, D. C. & Otárola-Castillo, E. geomorph: an R package for the collection and analysis of geometric morphometric shape data. Methods Ecol. Evol. 4, 393–399 (2013).Article 

Google Scholar 
Adams, D. C., Collyer, M., Kaliontzopoulou, A. & Sherratt, E. Geomorph: software for geometric morphometric analyses. (2016).Chira, A. M. & Thomas, G. H. The impact of rate heterogeneity on inference of phylogenetic models of trait evolution. J. Evol. Biol. 29, 2502–2518 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rodríguez Casal, A. & Pateiro López, B. Generalizing the convex hull of a sample: the R package alphahull. J. Stat. Softw. 34, 1–28 (2010).Zelditch, M. L., Swiderski, D. L. & Sheets, H. D. Geometric Morphometrics for Biologists: A Primer (Academic Press, 2012).Clavel, J. & Morlon, H. Reliable phylogenetic regressions for multivariate comparative data: illustration with the MANOVA and application to the effect of diet on mandible morphology in Phyllostomid bats. Syst. Biol. 69, 927–943 (2020).CAS 
PubMed 
Article 

Google Scholar 
Dujardin, J.-P., Le Pont, F. & Baylac, M. Geographical versus interspecific differentiation of sand flies (Diptera: Psychodidae): a landmark data analysis. Bull. Entomol. Res. 93, 87–90 (2003).PubMed 
Article 

Google Scholar 
Sidlauskas, B. Continuous and arrested morphological diversification in sister clades of characiform fishes: a phylomorphospace approach. Evolution 62, 3135–3156 (2008).PubMed 
Article 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
International, B. BirdLife International and handbook of the birds of the world (2017) Bird species distribution maps of the world. (2017).Callaghan, C. T., Nakagawa, S. & Cornwell, W. K. Global abundance estimates for 9,700 bird species. Proc. Natl. Acad. Sci. USA 118, e2023170118 (2021).Hijmans, R. & van Etten, J. raster: raster: geographic data analysis and modeling. R. Packag. version 517, 2 (2014).
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).Article 

Google Scholar 
Dray, S. & Dufour, A.-B., others. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22, 1–20 (2007).Article 

Google Scholar 
Ho, L. S. T. et al. Package ‘phylolm’. (2018).Akaike, H. Selected Papers of Hirotugu Akaike (Springer, 1998).Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004).CAS 
PubMed 
Article 

Google Scholar  More