Philippa Kaur

Potts, S. G. et al. (eds.). IPBES: The Assessment Report of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services on Pollinators, Pollination and Food Production (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Bonn, Germany) (2016).Potts, S. G. et al. Global pollinator declines: Trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).PubMed 

Google Scholar 
Goulson, D., Nicholls, E., Botías, C. & Rotheray, E. L. Bee declines driven by combined stress from parasites, pesticides, and lack of flowers. Science 347, 1255957 (2015).PubMed 

Google Scholar 
Majewska, A. A. & Altizer, S. Planting gardens to support insect pollinators. Conserv. Biol. 34, 15–25 (2020).PubMed 

Google Scholar 
Image, M. et al. Does agri-environment scheme participation in England increase pollinator populations and crop pollination services?. Agric. Ecosyst. Environ. 325, 107755 (2022).
Google Scholar 
Vaissière, B., Freitas, B. M. & Gemmill-Herren, B. Protocol to Detect and Assess Pollination Deficits in Crops: A Handbook for Its Use (FAO, 2011).
Google Scholar 
Archer, C. R., Pirk, C. W. W., Carvalheiro, L. G. & Nicolson, S. W. Economic and ecological implications of geographic bias in pollinator ecology in the light of pollinator declines. Oikos 123, 401–407 (2014).
Google Scholar 
M’Gonigle, L. K., Ponisio, L. C., Cutler, K. & Kremen, C. Habitat restoration promotes pollinator persistence and colonization in intensively managed agriculture. Ecol. Appl. 25, 1557–1565 (2015).PubMed 

Google Scholar 
Garbuzov, M. & Ratnieks, F. L. W. Listmania: The strengths and weaknesses of lists of garden plants to help pollinators. Bioscience 64, 1019–1026 (2014).
Google Scholar 
Garbuzov, M. & Ratnieks, F. L. W. Quantifying variation among garden plants in attractiveness to bees and other flower-visiting insects. Funct. Ecol. 28, 364–374 (2014).
Google Scholar 
Garbuzov, M., Alton, K. & Ratnieks, F. L. W. Most ornamental plants on sale in garden centres are unattractive to flower-visiting insects. PeerJ 5, e3066 (2017).PubMed 
PubMed Central 

Google Scholar 
Nichols, R. N., Goulson, D. & Holland, J. M. The best wildflowers for wild bees. J. Insect Conserv. 23, 819–830 (2019).
Google Scholar 
Harmon-Threatt, A. Influence of nesting characteristics on health of wild bee communities. Annu. Rev. Entomol. 65, 39–56 (2020).CAS 
PubMed 

Google Scholar 
Requier, F. & Leonhardt, S. D. Beyond flowers: Including non-floral resources in bee conservation schemes. J. Insect Conserv. 24, 5–16 (2020).
Google Scholar 
Sinu, P. A. & Bronstein, J. L. Foraging preferences of leafcutter bees in three contrasting geographical zones. Divers. Distrib. 24, 621–628 (2018).
Google Scholar 
Cecala, J. M. & Rankin, E. E. Pollinators and plant nurseries: How irrigation and pesticide treatment of native ornamental plants impact solitary bees. Proc. R. Soc. B Biol. Sci. 288, 20211287 (2021).
CAS 

Google Scholar 
Gonzalez, V. H., Gustafson, G. T. & Engel, M. S. Morphological phylogeny of Megachilini and the evolution of leaf-cutter behavior in bees (Hymenoptera: Megachilidae). J. Melittology 85, 1–123 (2019).
Google Scholar 
Kambli̇, S. S. et al. M. S. Aiswarya, K. Manoj, S. Varma, G. Asha, T. P. Rajesh, P. A. Sinu, Leaf foraging sources of leafcutter bees in a tropical environment: Implications for conservation. Apidologie 48, 473–482 (2017).Ascher, J. S. & Pickering, J. Discover Life Bee Species Guide and World Checklist (Hymenoptera: Apoidea: Anthophila) (2019).McCabe, L. M., Aslan, S. E. & Cobb, N. S. Decreased bee emergence along an elevation gradient: implications for climate change revealed by a transplant experiment. Ecology 103, e03598 (2021).PubMed 

Google Scholar 
Pitts-Singer, T. L. & Cane, J. H. The Alfalfa leafcutting bee, Megachile rotundata: The worlds most intensively managed solitary bee. Annu. Rev. Entomol. 56, 221–237 (2011).CAS 
PubMed 

Google Scholar 
MacIvor, J. S. & Packer, L. “Bee hotels” as tools for native pollinator conservation: A premature verdict?. PLoS ONE 10, e0122126 (2015).PubMed 
PubMed Central 

Google Scholar 
Maclvor, J. S. DNA barcoding to identify leaf preference of leafcutting bees. R. Soc. Open Sci. 3, 150623 (2016).ADS 

Google Scholar 
Wissemann, V. & Ritz, C. M. The genus Rosa (Rosoideae, Rosaceae) revisited: Molecular analysis of nrITS-1 and atpB-rbcL intergenic spacer (IGS) versus conventional taxonomy. Botanical J. Linn. Soc. 147, 275–290 (2005).
Google Scholar 
Wang, G. Study on the history of Chinese roses from ancient works and images. Acta Hort. 751, 347–356 (2007).
Google Scholar 
Nybom, H. & Werlemark, G. Realizing the potential of health-promoting rosehips from dogroses (Rosa sect. Caninae). Curr. Bioact. Compd. 13, 3–17 (2016).
Google Scholar 
Chang, Y. Z., Chen, H. M. & Qi, R. S. Ornamental pest—studies on leafcutting bees Megachile subtranquilla Yasumatsu. Acta Agriculturae Universitatis Pekinensis 15, 208–213 (1989).
Google Scholar 
Stroom, K., Fetzer, J. & Krischik, V. Insect Pests of Roses. 1–12 (Minnesota Extension Service, University of Minnesota, 1997).Knox, G. W., Paret, M. & Mizell, R. F. III. Pests of roses in Florida (2008).Hayward, A. et al. The leafcutter bee, Megachile rotundata, is more sensitive to N-cyanoamidine neonicotinoid and butenolide insecticides than other managed bees. Nat. Ecol. Evol. 3, 1521–1524 (2019).PubMed 

Google Scholar 
Fox, J. et al. Package ‘car’, Vol. 16, (R Foundation for Statistical Computing, 2012).K. Barton, Package Multi-Model Inference (MuMIn). https://cran.r-project.org/web/packages/MuMIn/MuMIn.pdf (2013).Hartig, F. & Hartig M. F. Package ‘DHARMa’:R package (2017).R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2021).Boff, S., Raizer, J. & Lupi, D. Environmental display can buffer the effect of pesticides on solitary bees. Insects. 11, 1–15 (2020).
Google Scholar 
Cameron, S. A. et al. Patterns of widespread decline in North American bumble bees. Proc. Natl. Acad. Sci. USA. 108, 662–667 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Cameron, S. A. & Sadd, B. M. Global trends in bumble bee health. Annu. Rev. Entomol. 65, 209–232 (2020).CAS 
PubMed 

Google Scholar 
Kopit, A. M. & Pitts-Singer, T. L. Routes of pesticide exposure in solitary, cavity-nesting bees. Environ. Entomol. 47, 499–510 (2018).CAS 

Google Scholar 
Pitts-Singer, T. L. & Barbour, J. D. Effects of residual novaluron on reproduction in alfalfa leafcutting bees, Megachile rotundata F. (Megachilidae). Pest Manag. Sci. 73, 153–159 (2017).CAS 
PubMed 

Google Scholar 
McKinney, M. L. Urbanization, biodiversity, and conservation. Bioscience 52, 883–890 (2002).
Google Scholar 
Baldock, K. C. R. et al. A systems approach reveals urban pollinator hotspots and conservation opportunities. Nat. Ecol. Evol. 3, 363–373 (2019).PubMed 
PubMed Central 

Google Scholar 
Choate, B. A., Hickman, P. L. & Moretti, E. A. Wild bee species abundance and richness across an urban–rural gradient. J. Insect Conserv. 22, 391–403 (2018).
Google Scholar 
Theodorou, P. et al. Pollination services enhanced with urbanization despite increasing pollinator parasitism. Proc. R. Soc. B Biol. Sci. 283, 20160561 (2016).
Google Scholar 
Theodorou, P. et al. Urban areas as hotspots for bees and pollination but not a panacea for all insects. Nat. Commun. 11, 1–13 (2020).
Google Scholar 
Rocha-Filho, L. C., Martins, A. C. & Marchi, P. Notes on a nest of Megachile (Moureapis) apicipennis Schrottky (Megachilidae) constructed in an abandoned gallery of Xylocopa frontalis (Olivier) (Apidae). Sociobiology 64, 442–450 (2017).
Google Scholar 
Sheffield, C. S. Unusual nesting behavior in Megachile (Eutricharaea) rotundata (Hymenoptera: Megachilidae). J. Melittol. 69, 1–6 (2017).
Google Scholar 
Krischik, V., Rogers, M., Gupta, G. & Varshney, A. Soil-applied imidacloprid translocates to ornamental flowers and reduces survival of adult Coleomegilla maculata, Harmonia axyridis, and Hippodamia convergens lady beetles, and larval Danaus plexippus and Vanessa cardui butterflies. PLoS ONE 10, e0119133 (2015).PubMed 
PubMed Central 

Google Scholar 
Senapathi, D. et al. The impact of over 80 years of land cover changes on bee and wasp pollinator communities in England. Proc. R. Soc. B Biol. Sci. 282, 20150294 (2015).
Google Scholar 
Potts, S. G. et al. Role of nesting resources in organising diverse bee communities in a Mediterranean landscape. Ecol. Entomol. 30, 78–85 (2005).
Google Scholar 
Acar, C., Acar, H. & Eroǧlu, E. Evaluation of ornamental plant resources to urban biodiversity and cultural changing: A case study of residential landscapes in Trabzon city (Turkey). Build. Environ. 42, 218–229 (2007).
Google Scholar 
Wang, H. F., Qureshi, S., Knapp, S., Friedman, C. R. & Hubacek, K. A basic assessment of residential plant diversity and its ecosystem services and disservices in Beijing, China. Appl. Geogr. 64, 121–131 (2015).
Google Scholar 
Pergl, J. et al. Dark side of the fence: ornamental plants as a source of wildgrowing flora in the Czech Republic. Preslia 88, 163–184 (2016).
Google Scholar 
Avolio, M. et al. Urban plant diversity in Los Angeles, California: Species and functional type turnover in cultivated landscapes. Plants People Planet. 2, 144–156 (2020).
Google Scholar 
Orr, M. C. et al. Global patterns and drivers of bee distribution. Curr. Biol. 31, 451–458 (2021).CAS 
PubMed 

Google Scholar 
Sinu, P. A., Kuriakose, G. & Shivanna, K. R. Is the bumblebee (Bombus haemorrhoidalis) the only pollinator of large cardamom in central Himalayas, India?. Apidologie 42, 690–695 (2012).
Google Scholar 
Veereshkumar, V. V. & Gupta, A. Parasitisation of leaf-cutter bees (Megachilidae: Apoidea) by Melittobia. Entomon 40, 105–112 (2015).
Google Scholar 
Cecala, J. M. & Wilson Rankin, E. E. Petals and leaves: Quantifying the use of nest building materials by the world’s most valuable solitary bee. Ecology 103, e03584 (2021).PubMed 

Google Scholar 
Soh, E. J. Y., Soh, Z. W. W., Ascher, J. S. & Tan, H. T. W. Diversity of plants with leaves cut by bees of the genus Megachile in Singapore. Nat. Singap. 12, 63–74 (2019).
Google Scholar 
MacIvor, J. S. & Moore, A. E. Bees collect polyurethane and polyethylene plastics as novel nest materials. Ecosphere 4, 155 (2013).
Google Scholar 
Allasino, M. L., Marrero, H. J., Dorado, J. & Torretta, J. P. Scientific note: First global report of a bee nest built only with plastic. Apidologie 50, 230–233 (2019).
Google Scholar  More

Davies EK, Peters AD, Keightley PD (1999) High frequency of cryptic deleterious mutations in Caenorhabditis elegans. Science 285:1748–1751Article 
CAS 
PubMed 

Google Scholar 
Eyre-Walker A, Keightley PD (2007) The distribution of fitness effects of new mutations. Nat Rev Genet 8:610–618Article 
CAS 
PubMed 

Google Scholar 
Eyre-Walker A, Keightley PD (2013) A comparison of models to infer the distribution of fitness effects of new mutations. Genetics 193:1197–1208Article 

Google Scholar 
Fry JD, Keightley PD, Heinsohn SL, Nuzhdi SV (1999) New estimates of the rates and effects of mildly deleterious mutation in Drosophila melanogaster. Proc Natl Acad Sci 96:574–579Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2007) Shortcut predictions for fitness properties at the mutation-selection-drift balance and for its buildup after size reduction under different management strategies. Genetics 176:983–997Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2012) Understanding and predicting the fitness decline of shrunk populations: inbreeding, purging, mutation, and standard selection. Genetics 190:1461–1476Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A (2015) On the consequences of ignoring purging on genetic recommendations for minimum viable population rules. Heredity 115:185–187Article 
PubMed 
PubMed Central 

Google Scholar 
García-Dorado A, Caballero A (2021) Neutral genetic diversity as a useful tool for conservation biology. Conserv Genet 22:541–545Article 

Google Scholar 
Garner BA, Hoban S, Luikart G (2020) IUCN Red List and the value of integrating genetics. Conserv Genet 21:795–801Article 

Google Scholar 
Hedrick PW, García-Dorado A (2016) Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol Evol 31:940–952Article 
PubMed 

Google Scholar 
Kardos M, Armstrong EE, Fitzpatrick SW, Hauser S, Hedrick PW, Miller J et al. (2021) The crucial role of genome-wide genetic variation in conservation. Proc Natl Acad Sci USA 118:e2104642118Khan A, Patel A, Shukla H, Viswanathan A, van der Valk T, Borthakur U, … & Ramakrishnan U (2021) Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers. Proc. Natl. Acad. Sci. 118Kimura M, Maruyama T, Crow JF (1963) The mutation load in small populations. Genetics 48:1303–1312Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Kimura M (1980) Average time until fixation of a mutant allele in a finite population under continued mutation pressure: Studies by analytical, numerical, and pseudo-sampling methods. Proc Natl Acad Sci 77:522–526Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Morin PA, Archer FI, Avila CD, Balacco JR, Bukhman YV, Chow, W, … & Jarvis ED (2021) Reference genome and demographic history of the most endangered marine mammal, the vaquita. Mol Ecol Resour 21:1008–1020Mukai T (1964) The genetic structure of natural populations of Drosophila melanogaster. I. Spontaneous mutation rate of polygenes controlling viability. Genetics 50:1–19Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Nietlisbach P, Muff S, Reid JM, Whitlock MC, Keller LF (2019) Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load. Evol Applic 12:266–279Article 

Google Scholar 
O’Grady JJ, Brook BW, Reed DH, Ballou JD, Tonkyn DW, Frankham R (2006) Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol Conserv 133:42–51Article 

Google Scholar 
Pérez-Pereira N, Caballero A, García-Dorado A (2021) Reviewing the consequences of genetic purging on the success of rescue programs. Conserv Gen 23:1–17Article 

Google Scholar 
Pérez-Pereira N, Wang J, Quesada H, Caballero A (2022). Prediction of the minimum effective size of a population viable in the long term. Biodivers Conserv https://doi.org/10.1007/s10531-022-02456-zRobinson JA, Kyriazis CC, Nigenda-Morales SF, Beichman AC, Rojas-Bracho L, Robertson KM et al. (2022) The critically endangered vaquita is not doomed to extinction by inbreeding depression. Science 376:635–639Article 
CAS 
PubMed 

Google Scholar 
Teixeira JC, Huber CD (2021) The inflated significance of neutral genetic diversity in conservation genetics. Proc Natl Acad Sci USA 118:e2015096118Wade EE, Kyriazis C, Cavassim MIA, Lohmueller KE (2022) Quantifying the fraction of new mutations that are recessive lethal. bioRxiv 1–24, https://www.biorxiv.org/content/10.1101/2022.04.22.489225v1 More

Sites overview and characteristicsThis study focused on three regions located in subantarctic, arctic, and subarctic latitudes. The respective latitudinal and longitudinal ranges covered in this study were: 54.95 to 52.08 °S, and 72.03 to 67.34 °W in Patagonia; 67.44 to 67.54 °N, and 86.59 to 86.71 °E in Siberia; 63.21 to 68.63 °N, and −150.79 to −145.98 °W in Alaska (Figs. 1 and 2). The exact coordinates for each sample were included in the submitted dataset. The field campaigns were conducted in 2016, during the summer for each respective region: January-February in Chilean Patagonia, June-July in Alaska and July-August in Siberia.Fig. 1Location of the three areas included in this study (panel a). The permafrost state and the number of sites and samples per region is indicated for each area. General views of 5 sites are provided as examples (b–f). Panel B provides a large view of the ecosystem surrounding the wetland ALP2 (Alaska, exact location indicated by the white circle). Lake PCL1 (panel c) is representative of the lakes on Navarino island (Chilean Patagonia). The glacial lake SIL2 is shown in panel d. At site SIP5, the hollow at first plan is surrounded by palsa (hummock, second plan), characterized by dark organic matter and lichen vegetation (panel e). The PPP3 peatland shown in panel f is dominated by Sphagnum magellanicum, like most peatlands in the area.Full size imageFig. 2Maps of sampling sites in Patagonia, Alaska and Siberia, indicating the ecosystem type (lake, wetland, soil). The tables show the complete- (in white) and the partial- (in grey) characterization sites. The exact coordinates of each sample are provided in the data record (See data records section).Full size imageFor every site included in the present study, a set of nine qualitative environmental and/or ecological site-scale descriptors was selected and adapted from ENVO Environment Ontology40, which included for example permafrost state, biome, environmental feature and vegetation type (Table 1, Fig. 3). Permafrost state was obtained from the NSIDC permafrost map41. The biome, large-scale descriptor based on climate and vegetation criteria, was derived from Olson et al.42. Temperate forest, boreal forest, and tundra biomes were included. The environmental features that were representative for the three regions were considered: lakes, wetlands, broadleaf/coniferous/mixed forest soils, grassland, tundra, and palsa. All the metadata was included in the submitted dataset. Table 2 summarizes the main types of sampled ecosystems and their main characteristics in the three regions, while Supplementary Table S1 provides the details of each sampling site.Table 1 Overview of the dataset contained in Mimarks sheet.Full size tableFig. 3Description of the qualitative environmental/ecological descriptors used to describe every sample, derived from ENVO Environment Ontology40.Full size imageTable 2 Main types of sampled ecosystems in the three studied regions.Full size tableIn Alaska, the studied area ranged from the Alaska Range and Fairbanks area (interior, continental climate, 63–65°N, discontinuous permafrost) up to Toolik Field Station (North Slope, arctic climate, 66–69°N, continuous permafrost; Fig. 2). The physiochemistry and CH4 emissions of lakes ALL1 (Killarney lake), ALL2 (Otto lake), ALL3 (Nutella lake), and ALL4 (Goldstream lake) were previously characterized35. A number of heterogeneous soil and wetland samples were collected around the studied Alaskan lakes and/or from monitored sites, as detailed in Supplementary Table S1. In the Alaska Range and Fairbanks area, soils were mostly covered by mixed or taiga forests, alpine tundra, and bogs or fens wetlands. In the norther Brooks Ranges mountain system, the landscape was piedmont hills with a predominant soil of porous organic peat underlain by silt and glacial till, all in a permafrost state, characterized mainly by Sphagnum and Eriophorum vegetation, as well as dwarf shrubs.In Siberia, the studied area was located in the discontinuous permafrost region surrounding Igarka, on the eastern bank of the Yenisei River (Fig. 2). This region was mainly covered by forest, dominated by larch (Larix Siberica), birch (Betula Pendula), and Siberian pine (Pinus Siberica), and palsa landscapes (frozen peat mounts), the latter being dominated by moss, lichens, Labrador tea and dwarf birch. In degraded areas, thermokarst bogs were dominated by Sphagnum spp. and Eriophorum spp. Land cover was an indicator of permafrost status, since forested areas reflected a deep permafrost table ( >2 m) associated with Pleistocene permafrost, while palsa-dominated landscapes were indicative of the presence of near-surface ( More

Mueller, R. C. et al. An emerging view of the diversity, ecology, and function of Archaea in alkaline hydrothermal environments. FEMS Microbiol. Ecol. 97, fiaa246 (2020).
Google Scholar 
López-López, O., Cerdán, M.-E. & González-Siso, M.-I. Thermus thermophilus as a source of thermostable lipolytic enzymes. Microorganisms 3, 792–808 (2015).PubMed 
PubMed Central 

Google Scholar 
Sahay, H. et al. Hot springs of Indian Himalayas: Potential sources of microbial diversity and thermostable hydrolytic enzymes. 3 Biotech 7, 118 (2017).PubMed 
PubMed Central 

Google Scholar 
Patel, A. K., Singhania, R. R., Sim, S. J. & Pandey, A. Thermostable cellulases: Current status and perspectives. Bioresour Technol 279, 385–392 (2019).CAS 
PubMed 

Google Scholar 
Decastro, M.-E., Rodríguez-Belmonte, E. & González-Siso, M.-I. Metagenomics of thermophiles with a focus on discovery of novel thermozymes. Front. Microbiol. 7, 1521–1521 (2016).PubMed 
PubMed Central 

Google Scholar 
Meslé, M. M. et al. Isolation and characterization of lignocellulose-degrading geobacillus thermoleovorans from Yellowstone National Park. Appl. Environ. Microbiol. 88, e0095821 (2022).PubMed 

Google Scholar 
Verma, P., Yadav, A. N., Shukla, L., Saxena, A. K. & Suman, A. Hydrolytic enzymes production by thermotolerant Bacillus altitudinis IARI-MB-9 and Gulbenkiania mobilis IARI-MB-18 isolated from Manikaran hot springs. Int. J. Adv. Res. 3, 1241–1250 (2015).CAS 

Google Scholar 
Wu, B. et al. Microbial sulfur metabolism and environmental implications. Sci. Total Environ. 778, 146085 (2021).ADS 
CAS 
PubMed 

Google Scholar 
Lavrentyeva, E. V. et al. Bacterial diversity and functional activity of microbial communities in hot springs of the Baikal Rift Zone. Microbiology 87, 272–281 (2018).CAS 

Google Scholar 
Miller Scott, R., Strong Aaron, L., Jones Kenneth, L. & Ungerer Mark, C. Bar-Coded pyrosequencing reveals shared bacterial community properties along the temperature gradients of two alkaline hot springs in Yellowstone National Park. Appl. Environ. Microbiol. 75, 4565–4572 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sharp, C. E. et al. Humboldt’s spa: Microbial diversity is controlled by temperature in geothermal environments. ISME J. 8, 1166–1174 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stefanova, K. et al. Archaeal and bacterial diversity in two hot springs from geothermal regions in Bulgaria as demostrated by 16S rRNA and GH-57 genes. Int. Microbiol. 18, 217–223 (2015).CAS 
PubMed 

Google Scholar 
Hou, W. et al. A comprehensive census of microbial diversity in hot springs of Tengchong, Yunnan Province China using 16S rRNA gene pyrosequencing. PLoS ONE 8, e53350 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sahm, K. et al. High abundance of heterotrophic prokaryotes in hydrothermal springs of the Azores as revealed by a network of 16S rRNA gene-based methods. Extremophiles 17, 649–662 (2013).CAS 
PubMed 

Google Scholar 
Purcell, D. et al. The effects of temperature, pH and sulphide on the community structure of hyperthermophilic streamers in hot springs of northern Thailand. FEMS Microbiol. Ecol. 60, 456–466 (2007).CAS 
PubMed 

Google Scholar 
Meyer-Dombard, D. R. & Amend, J. P. Geochemistry and microbial ecology in alkaline hot springs of Ambitle Island, Papua New Guinea. Extremophiles 18, 763–778 (2014).CAS 
PubMed 

Google Scholar 
de Leon, K. B., Gerlach, R., Peyton, B. M. & Fields, M. W. Archaeal and bacterial communities in three alkaline hot springs in Heart Lake Geyser Basin, Yellowstone National Park. Front. Microbiol. 4, 10 (2013).
Google Scholar 
Boomer, S. M., Noll, K. L., Geesey, G. G. & Dutton, B. E. Formation of multilayered photosynthetic biofilms in an alkaline thermal spring in Yellowstone National Park, Wyoming. Appl. Environ. Microbiol. 75, 2464–2475 (2009).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wang, S. et al. Greater temporal changes of sediment microbial community than its waterborne counterpart in Tengchong hot springs, Yunnan Province, China. Sci. Rep. 4, 7479 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sun, Y., Liu, Y., Pan, J., Wang, F. & Li, M. Perspectives on cultivation strategies of archaea. Microb. Ecol. 79, 770–784 (2020).PubMed 

Google Scholar 
Brock, T. D. Life at high temperatures. Science 158, 1012 (1967).ADS 
CAS 
PubMed 

Google Scholar 
Christiansen, R. L. The Quaternary and Pliocene Yellowstone Plateau volcanic field of Wyoming, Idaho, and Montana. Professional Paper (2001).Rowe, J. J., Fournier, R. & Morey, G. Chemical analysis of thermal waters in Yellowstone National Park, Wyoming, 1960–65. USGS https://doi.org/10.3133/b1303 (1973).Article 

Google Scholar 
Fournier, R., Thompson, M. J. & Hutchinson, R. A. The geochemistry of hot spring waters at Norris Geyser Basin, Yellowstone National Park. International symposium on water-rock interactions (1992).Podar, P. T., Yang, Z., Björnsdóttir, S. H. & Podar, M. Comparative analysis of microbial diversity across temperature gradients in hot springs from Yellowstone and Iceland. Front. Microbiol. 11, 1625 (2020).PubMed 
PubMed Central 

Google Scholar 
Pala, C. et al. Environmental drivers controlling bacterial and archaeal abundance in the sediments of a Mediterranean lagoon ecosystem. Curr. Microbiol. 75, 1147–1155 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Foyer, C. H., Noctor, G. & Hodges, M. Respiration and nitrogen assimilation: Targeting mitochondria-associated metabolism as a means to enhance nitrogen use efficiency. J. Exp. Bot. 62, 1467–1482 (2011).CAS 
PubMed 

Google Scholar 
Ershanovich, V. N. et al. Nitrogen assimilation enzymes in Bacillus subtilis mutants with hyperproduction of riboflavin. Mol. Gen. Mikrobiol. Virusol. 2005(3), 29–34 (2005).
Google Scholar 
Offre, P., Spang, A. & Schleper, C. Archaea in biogeochemical cycles. Annu Rev Microbiol 67, 437–457 (2013).CAS 
PubMed 

Google Scholar 
Cabello, P., Roldán, M. D. & Moreno-Vivián, C. Nitrate reduction and the nitrogen cycle in archaea. Microbiology 150, 3527–3546 (2004).CAS 
PubMed 

Google Scholar 
Graupner, M., Xu, H. & White, R. H. The pyrimidine nucleotide reductase step in riboflavin and F(420) biosynthesis in archaea proceeds by the eukaryotic route to riboflavin. J. Bacteriol. 184, 1952–1957 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chernyh, N. A. et al. Dissimilatory sulfate reduction in the archaeon “Candidatus Vulcanisaeta moutnovskia” sheds light on the evolution of sulfur metabolism. Nat. Microbiol. 5, 1428–1438 (2020).CAS 
PubMed 

Google Scholar 
Castelle, C. J. & Banfield, J. F. Major new microbial groups expand diversity and alter our understanding of the tree of life. Cell 172, 1181–1197 (2018).CAS 
PubMed 

Google Scholar 
Williams, T. A. et al. Integrative modeling of gene and genome evolution roots the archaeal tree of life. Proc. Natl. Acad. Sci. U.S.A. 114, E4602–E4611 (2017).ADS 
CAS 
PubMed 
PubMed Central 

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 

Google Scholar 
Wang, Y., Wegener, G., Hou, J., Wang, F. & Xiao, X. Expanding anaerobic alkane metabolism in the domain of Archaea. Nat. Microbiol. 4, 595–602 (2019).CAS 
PubMed 

Google Scholar 
Hedlund, B. P. et al. Uncultivated thermophiles: Current status and spotlight on ‘Aigarchaeota’. Curr. Opin. Microbiol. 25, 136–145 (2015).CAS 
PubMed 

Google Scholar 
Reichart, N. J. et al. Activity-based cell sorting reveals responses of uncultured archaea and bacteria to substrate amendment. ISME J. 14, 2851–2861 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hua, Z.-S. et al. Genomic inference of the metabolism and evolution of the archaeal phylum Aigarchaeota. Nat. Commun. 9, 2832 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Beam, J. P. et al. Ecophysiology of an uncultivated lineage of Aigarchaeota from an oxic, hot spring filamentous “streamer” community. ISME J. 10, 210–224 (2016).CAS 
PubMed 

Google Scholar 
Gonsior, M. et al. Yellowstone hot springs are organic chemodiversity hot spots. Sci. Rep. 8, 14155 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
Gibson, M. L. & Hinman, N. W. Mixing of hydrothermal water and groundwater near hot springs, Yellowstone National Park (USA): Hydrology and geochemistry. Hydrogeol. J. 21, 919–933 (2013).ADS 
CAS 

Google Scholar 
Campbell, K. M. et al. Sulfolobus islandicus meta-populations in Yellowstone National Park hot springs. Environ. Microbiol. 19, 2334–2347 (2017).PubMed 

Google Scholar 
Thiel, V. et al. The dark side of the mushroom spring microbial mat: Life in the shadow of chlorophototrophs. I. Microbial diversity based on 16S rRNA gene amplicons and metagenomic sequencing. Front. Microbiol. 7, 919 (2016).PubMed 
PubMed Central 

Google Scholar 
Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. U.S.A. 108, 4516–4522 (2011).ADS 
CAS 
PubMed 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: Assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
Google Scholar 
Thompson, L. R. et al. A communal catalogue reveals Earth’s multiscale microbial diversity. Nature 555, 457–463 (2017).ADS 

Google Scholar 
Eloe-Fadrosh, E. A., Ivanova, N. N., Woyke, T. & Kyrpides, N. C. Metagenomics uncovers gaps in amplicon-based detection of microbial diversity. Nat. Microbiol. 1, 15032 (2016).CAS 
PubMed 

Google Scholar 
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).CAS 
PubMed 

Google Scholar 
Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv https://doi.org/10.1101/081257 (2016).Article 

Google Scholar 
Murali, A., Bhargava, A. & Wright, E. S. IDTAXA: A novel approach for accurate taxonomic classification of microbiome sequences. Microbiome 6, 140 (2018).PubMed 
PubMed Central 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 38, 1079–1086 (2020).CAS 
PubMed 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: Linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 11, 538 (2010).
Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).CAS 
PubMed 

Google Scholar 
Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30, 918–920 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 11, 395 (2010).
Google Scholar 
Patiny, L. & Borel, A. ChemCalc: A building block for tomorrow’s chemical infrastructure. J. Chem. Inf. Model. 53, 1223–1228 (2013).CAS 
PubMed 

Google Scholar 
Chong, J., Wishart, D. S. & Xia, J. Using MetaboAnalyst 4.0 for comprehensive and integrative metabolomics data analysis. Curr. Protoc. Bioinform. 68, e86 (2019).
Google Scholar 
Liu, G., Lee, D. P., Schmidt, E. & Prasad, G. L. Pathway analysis of global metabolomic profiles identified enrichment of caffeine, energy, and arginine metabolism in smokers but not moist snuff consumers. Bioinform. Biol. Insights 13, 1177932219882961–1177932219882961 (2019).PubMed 
PubMed Central 

Google Scholar 
Xia, J. & Wishart, D. S. MetPA: A web-based metabolomics tool for pathway analysis and visualization. Bioinformatics 26, 2342–2344 (2010).CAS 
PubMed 

Google Scholar 
Huber, W. et al. Orchestrating high-throughput genomic analysis with Bioconductor. Nat. Methods 12, 115–121 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rohart, F., Gautier, B., Singh, A. & Lé Cao, K.-A. mixOmics: An R package for ’omics feature selection and multiple data integration. PLoS Comput. Biol. 13, e1005752–e1005752 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar  More

Our results show that GoM BFT larval survey samples can provide the crucial mark events for eventual CKMR estimates of adult abundance. The adult parents marked by larval samples can be directly recaptured in the fishery many years later as POPs, and indirectly through their progeny in future samples of larvae, as evidenced by the two cross-cohort HSPs (XHSPs) recovered in this study, which imply that a parent survived and spawned in the GoM in consecutive years. As more cohorts are sampled in future, the growing number of XHSPs could be used to estimate average adult survival rates, in addition to helping with the estimation of adult abundance31, as is now done for southern blue tuna40.There is a modest level of sibship within our 2016 samples, and a high level (involving over half the samples) in 2017, but it turns out not to be high enough to cause serious problems for POP-based CKMR. High sibship per se does not lead to bias in CKMR by virtue of the statistical construction of the estimate, but it does increase variance, which can be summarized through a reduction in effective sample size. In a POP-based CKMR model, our effective sample size would be about 75% of nominal for the two years combined, or 66% of nominal for the targeted sampling of 2017. Since it is actually the product of adult and juvenile sample sizes which drives precision in CKMR14, one way to think about the 75% is that we will need about 33% more adult samples to achieve a given precision on abundance estimates than if we had somehow been able to collect the same number of “independent” juvenile samples (i.e. without oversampling siblings). That increase is appreciable but entirely achievable; for WBFT, it is logistically much easier to collect more feeding-ground adult samples than to collect more larvae, and at present there is no known practical way to collect large numbers of older, more dispersed, and thus more independent, juvenile western origin bluefin tuna (WBFT).This study was motivated by the concern that sibship might be a serious impediment to use of WBFT larvae for CKMR. High levels of sibship have been found in larval collections for other taxa despite a pelagic larval phase, suggesting that abiotic factors can impede random mixing of larvae after a spawning event41. Our larval samples were only a few days old (4–11) and thus had little time to disperse since fertilization; our concern beforehand was that each tow might sample the offspring of a very small number of adults (one spawning group in one night), and in 2017 that repeatedly towing the same water mass might simply be resampling the same “family”. In practice, though, the cumulative effect was limited. Samples were not dominated by progeny from just a few adults; the maximum DPG size (i.e., number of offspring from any one adult) was 5, which is under 2% of the larval sample size. There are several possible reasons for this finding. First, plankton sample tows are typically standardized to a ten-minute duration, covering on average about 0.3 nautical miles. Based on continuous plankton cameras42, each tow is likely to tow through multiple patches of zooplankton, and therefore potentially multiple patches of BFT larvae. Second, spawning aggregations of BFT may contain many adults. For example, on the spawning grounds near the Balearic Islands in the Mediterranean, purse seine fisheries target spawning fish and individual net sets routinely capture upwards of 500 mature individuals43. These numbers suggest that BFT spawner aggregations can be quite large, although the number of individuals that contribute gametes to a single spawning event may be lower. The results of this study pose intriguing scenarios for understanding BFT larval ecology and spawning behavior, which could be explored with larger sample sizes paired with data on oceanographic conditions, direct observation of spawning aggregations, and modeling to compare observed and predicted dispersal. The results of this study are based on just two years of sampling, and numerous practical and theoretical challenges remain to fully understand BFT reproduction in the GoM.Our sibship impact calculations assume use of an unmodified adult-size-based CKMR POP model, where each juvenile is compared to each adult taking into account the latter’s size (e.g.,14). That will give unbiased estimates, which we regard as essential in a CKMR model. However, for WBFT the estimates are not fully statistically efficient, in that some adults receive more statistical weight than others because they are marked more often (by having a large DPG), and thus variance might not be the lowest achievable. Modifying the model to fix that would be simple in a “cartoon” CKMR setting where all adults are identical (e.g., Fig. 1 of14), simply by first condensing each DPG to a single representative, then only using those representatives (rather than all the larvae) in POP comparisons. Each marked parent then receives the same weight, giving maximum efficiency. For the cartoon, this condensed-DPG model still gives an unbiased estimate of abundance, because each DPG has one parent of given sex, and the chance of any sampled cartoon adult of that sex being that parent is 1/N. The DPG-condensed effective sample size is simply half the number of distinct parents, which would be a little larger than the effective sample sizes for the unmodified model shown in Table 3; e.g., in 2017, 504/2 = 252 versus 209. However, no such straightforward improvement is available for an adult-size-based CKMR model such as is needed for WABFT. Using condensed DPGs directly would bias the juvenile sampling against larger more-fecund adults, whose DPGs will tend on average to be larger and thus to experience disproportionate condensation. Those adults would be marked less often by the DPG-condensed juveniles than the model assumes, violating the basic requirements for unbiased CKMR in14. A more sophisticated model might be able to combine unbiasedness with higher efficiency but, since the unmodified adult-size-based POP model that we expect to use is unbiased and only mildly inefficient (at worst 209/252 = 83% efficient, in 2017) there seems no particular need for extra complications at present. However, that may not hold true if we eventually move to a POP + XHSP model, where the impact on unmodified CKMR variance is worse (though there is still no bias, for the same reason as with POPs). Intuitively, the biggest impact that a DPG of size 5 can have in a POP model is to suddenly raise the number of POPs by 5 if its parent happens to be sampled; within a useful total of, say, 75 POPs, the influence is not that large. But if two DPGs both of size 5 in different cohorts happen to share a parent, then the total of XHSPs suddenly jumps by 25— likely a substantial proportion of total XHSPs. Supplementary Material B also includes effective sample size formulae for a simplified XHSP-only model, which demonstrate the increased impact of within-cohort sibship; for our WBFT samples, it turns out that the XHSP-effective size is slightly lower for the targeted 2017 samples (110) than for the 2016 samples (130), unlike the POP-only effective size. Dropping from a maximum theoretical effective sample size of 252 (half the number of DPGs) down to 110 would be rather inefficient and would increase the number of years of sampling required to yield a useful XHSP dataset. This motivates developing a modified POP + XHSP model that retains unbiasedness without sacrificing too much efficiency. In principle, that can be done by condensing each DPG but then conditioning its comparison probabilities on the DPG’s original size, in accordance with the framework in14. This is a topic for subsequent research, and the results will inform future sampling strategy decisions for WBFT.One potential difficulty for western BFT CKMR might occur if a substantial proportion of animals reaching maturity are the offspring of “Western” (in genetic terms) adults who persistently spawn in the western North Atlantic but outside the GoM. However, as long as the adults marked by GoM larvae are well mixed at the time of sampling with any western adults that do spawn outside of the GoM, the total POP-based population estimate of genetically-western BFT from CKMR will remain unbiased. Given evidence from tagging of widespread adult movements within the western North Atlantic2, good mixing in the sampled feeding grounds seems likely; so, even if successful non-GoM western BFT spawning really is commonplace, there should not be a problem with relying on GoM larvae for at least the POP component of CKMR14.Studies of fish early life history have long been considered to have great potential to provide novel insight into the unique population dynamics of fishes44,45,46. Sampling efforts aimed at estimating fish recruitment dynamics have spawned a diversity of larval survey programs. Examples of these long-term programs include the California Cooperative Oceanic Fisheries Investigations, International Council for the Exploration of the Sea (ICES) surveys in the North Atlantic and adjacent areas, Southeast Monitoring and Assessment Program (SEAMAP) in the GoM, Ecosystem Monitoring (EcoMon) in the Northeast U.S., and numerous others, many of which provide indices of larval abundance widely used in fisheries and ecosystem assessments. Yet, as a result of the inherent patchiness of larvae42, sampling variability, and highly variable density dependent mortality45, fisheries scientists have often struggled to determine how larval surveys relate to the adult fish populations. Inclusion of estimates of sibship among larvae collected in surveys could refine estimates of adult spawning stock biomass estimated from these surveys.The results of this study also represent products of decades of work and coordination in obtaining high-quality DNA from larval specimens. Key steps to successful genotyping of larvae include ensuring that larvae are preserved, sorted, and handled in 95% non-denatured ethanol. In addition, strict instrument cleaning protocols must be followed, and stomachs should be removed or avoided (this study used larval tails and, when possible, eyes to avoid cross contamination of prey contents, including possible congeners and other BFT individuals). Exposure to hot lamps during the sorting and dissection processes should also be minimized to ensure that DNA quality is sufficiently high for genotyping-by-sequencing. Although the tissues available for genetic analysis were limited by the needs of other experiments that required BFT tissues, otoliths, gut contents, and other information from the same larvae, we were able to successfully genotype most larvae greater than 6 mm SL and identify thousands of informative SNPs. The lower size limit of larvae could likely be decreased if whole specimens were available for genotyping, although the use of younger larvae could increase the incidence of sibship.In summary, while we observed both FSPs and HSPs in larval collections, with elevated sibship overall and with siblings being more prevalent within tows and in nearby tows, the level of sibship was sufficiently low that collections of GoM BFT larvae can still provide the critical genetic mark of parental genotypes required for CKMR. Our results demonstrate a crucial proof of concept and are the first step towards an operational CKMR modelling estimate of spawning stock abundance for western BFT. More

Leclère, D. et al. Bending the curve of terrestrial biodiversity needs an integrated strategy. Nature 585, 551–556 (2020).Article 
PubMed 

Google Scholar 
Pillay, R. et al. Tropical forests are home to over half of the world’s vertebrate species. Front. Ecol. Environ. 20, 10–15 (2022).Article 
PubMed 

Google Scholar 
Turubanova, S., Potapov, P. V., Tyukavina, A. & Hansen, M. C. Ongoing primary forest loss in Brazil, Democratic Republic of the Congo, and Indonesia. Environ. Res. Lett. 13, 074028 (2018).Article 

Google Scholar 
Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).Article 
CAS 
PubMed 

Google Scholar 
Gibson, L. et al. Primary forests are irreplaceable for sustaining tropical biodiversity. Nature 478, 378–381 (2011).Article 
CAS 
PubMed 

Google Scholar 
Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535, 144–147 (2016).Article 
CAS 
PubMed 

Google Scholar 
Watson, J. E. M. et al. The exceptional value of intact forest ecosystems. Nat. Ecol. Evol. 2, 599–610 (2018).Article 
PubMed 

Google Scholar 
Hansen, A. et al. Global humid tropics forest structural condition and forest structural integrity maps. Sci. Data 6, 232 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Hansen, A. J. et al. A policy-driven framework for conserving the best of Earth’s remaining moist tropical forests. Nat. Ecol. Evol. 4, 1377–1384 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
COP 11 Decision X/2. Strategic Plan for Biodiversity 2011–2020 (Convention on Biological Diversity, 2010).New York Declaration on Forests (UN, 2014).Transforming our World: The 2030 Agenda for Sustainable Development. A/RES/70/1 Resolution Adopted by the United Nations General Assembly (UN, 2015).Adoption of the Paris Agreement. Proposal by the President. Draft Decision -/CP.21 (UNFCCC, 2015).Hansen, A. J. et al. Toward monitoring forest ecosystem integrity within the post-2020 Global Biodiversity Framework. Conserv. Lett. 14, e12822 (2021).Article 

Google Scholar 
Scholes, R. et al. (eds) Summary for Policymakers of the Assessment Report on Land Degradation and Restoration (IPBES, 2018).First Draft of the Post-2020 Global Biodiversity Framework (Convention on Biological Diversity, 2021).Williams, B. A. et al. Change in terrestrial human footprint drives continued loss of intact ecosystems. One Earth 3, 371–382 (2020).Article 

Google Scholar 
The IUCN Red List of Threatened Species Version 2020–1 (IUCN, 2020).Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. Bioscience 67, 534–545 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Ives, A. R. & Garland, T. Phylogenetic logistic regression for binary dependent variables. Syst. Biol. 59, 9–26 (2010).Article 
PubMed 

Google Scholar 
Di Marco, M., Ferrier, S., Harwood, T. D., Hoskins, A. J. & Watson, J. E. M. Wilderness areas halve the extinction risk of terrestrial biodiversity. Nature 573, 582–585 (2019).Article 
PubMed 

Google Scholar 
Betts, M. G. et al. Global forest loss disproportionately erodes biodiversity in intact landscapes. Nature 547, 441–444 (2017).Article 
CAS 
PubMed 

Google Scholar 
Fletcher, R. & Fortin, M.-J. Spatial Ecology and Conservation Modeling: Applications with R (Springer, 2018). https://doi.org/10.1007/978-3-030-01989-1Briant, G., Gond, V. & Laurance, S. G. W. Habitat fragmentation and the desiccation of forest canopies: a case study from eastern Amazonia. Biol. Conserv. 143, 2763–2769 (2010).Article 

Google Scholar 
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, eaaz7005 (2020).Article 
CAS 
PubMed 

Google Scholar 
Pillay, R. et al. Using interview surveys and multispecies occupancy models to inform vertebrate conservation. Conserv. Biol. 36, e13832 (2022).Article 
PubMed 

Google Scholar 
Agresti, A. Categorical Data Analysis (John Wiley and Sons, 2002).Smith, A. C., Koper, N., Francis, C. M. & Fahrig, L. Confronting collinearity: comparing methods for disentangling the effects of habitat loss and fragmentation. Landsc. Ecol. 24, 1271–1285 (2009).Article 

Google Scholar 
Mittermeier, R. A. et al. Wilderness and biodiversity conservation. Proc. Natl Acad. Sci. USA 18, 10309–10313 (2003).Article 

Google Scholar 
Turner, I. M. & Corlett, R. T. The conservation value of small, isolated fragments of lowland tropical rain forest. Trends Ecol. Evol. 11, 330–333 (1996).Article 
CAS 
PubMed 

Google Scholar 
Tulloch, A. I. T., Barnes, M. D., Ringma, J., Fuller, R. A. & Watson, J. E. M. Understanding the importance of small patches of habitat for conservation. J. Appl. Ecol. 53, 418–429 (2016).Article 

Google Scholar 
Wintle, B. A. et al. Global synthesis of conservation studies reveals the importance of small habitat patches for biodiversity. Proc. Natl Acad. Sci. USA 116, 909–914 (2019).Article 
CAS 
PubMed 

Google Scholar 
Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6, eaax8574 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Prugh, L. R., Hodges, K. E., Sinclair, A. R. E. & Brashares, J. S. Effect of habitat area and isolation on fragmented animal populations. Proc. Natl Acad. Sci. USA 105, 20770–20775 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Grantham, H. S. et al. Anthropogenic modification of forests means only 40% of remaining forests have high ecosystem integrity. Nat. Commun. 11, 5978 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Beyer, H. L., Venter, O., Grantham, H. S. & Watson, J. E. M. Substantial losses in ecoregion intactness highlight urgency of globally coordinated action. Conserv. Lett. 13, e12692 (2020).Article 

Google Scholar 
Ehbrecht, M. et al. Global patterns and climatic controls of forest structural complexity. Nat. Commun. 12, 519 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
França, F. et al. Do space-for-time assessments underestimate the impacts of logging on tropical biodiversity? An Amazonian case study using dung beetles. J. Appl. Ecol. 53, 1098–1105 (2016).Article 

Google Scholar 
Di Marco, M., Venter, O., Possingham, H. P. & Watson, J. E. M. Changes in human footprint drive changes in species extinction risk. Nat. Commun. 9, 4621 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Betts, M. G. et al. Forest degradation drives widespread avian habitat and population declines. Nat. Ecol. Evol. 6, 709–719 (2022).Article 
PubMed 
PubMed Central 

Google Scholar 
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Basset, Y. et al. Arthropod diversity in a tropical forest. Science 338, 1481–1484 (2012).Article 
CAS 
PubMed 

Google Scholar 
Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).Article 
CAS 
PubMed 

Google Scholar 
Newbold, T. et al. Ecological traits affect the response of tropical forest bird species to land-use intensity. Proc. R. Soc. B 280, 20122131 (2013).Article 
PubMed 
PubMed Central 

Google Scholar 
Maron, M., Simmonds, J. S. & Watson, J. E. M. Bold nature retention targets are essential for the global environment agenda. Nat. Ecol. Evol. 2, 1194–1195 (2018).Article 
PubMed 

Google Scholar 
Díaz, S. et al. Set ambitious goals for biodiversity and sustainability. Science 370, 411–413 (2020).Article 
PubMed 

Google Scholar 
Bird Species Distribution Maps of the World Version 2018.1 (BirdLife International, accessed 16 August 2019).Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).Article 
PubMed 

Google Scholar 
González-del-Pliego, P. et al. Phylogenetic and trait-based prediction of extinction risk for data-deficient amphibians. Curr. Biol. 29, 1557–1563 (2019).Article 
PubMed 

Google Scholar 
IUCN Habitats Classification Scheme Version 3.1 (IUCN, 2012).Böhm, M. et al. The conservation status of the world’s reptiles. Biol. Conserv. 157, 372–385 (2013).Article 

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

Google Scholar 
Hansen, M. C. et al. Mapping tree height distributions in Sub-Saharan Africa using Landsat 7 and 8 data. Remote Sens. Environ. 185, 221–232 (2016).Article 

Google Scholar 
Sanderson, E. W. et al. The human footprint and the last of the wild. Bioscience 52, 891–904 (2002).Article 

Google Scholar 
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Di Marco, M., Watson, J. E. M., Possingham, H. P. & Venter, O. Limitations and trade-offs in the use of species distribution maps for protected area planning. J. Appl. Ecol. 54, 402–411 (2017).Article 

Google Scholar 
Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2603–E2610 (2013).Article 

Google Scholar 
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 116, G04021 (2011).Article 

Google Scholar 
Sexton, J. O. et al. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error. Int. J. Digit. Earth 6, 427–448 (2013).Article 

Google Scholar 
Potapov, P. et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sens. Environ. 253, 112165 (2021).Article 

Google Scholar 
Upham, N. S., Esselstyn, J. A. & Jetz, W. Inferring the mammal tree: species-level sets of phylogenies for questions in ecology, evolution, and conservation. PLoS Biol. 17, e3000494 (2019).Article 
CAS 
PubMed 
PubMed Central 

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).Article 
CAS 
PubMed 

Google Scholar 
Tonini, J. F. R., Beard, K. H., Ferreira, R. B., Jetz, W. & Pyron, R. A. Fully-sampled phylogenies of squamates reveal evolutionary patterns in threat status. Biol. Conserv. 204, 23–31 (2016).Article 

Google Scholar 
Jetz, W. & Pyron, R. A. The interplay of past diversification and evolutionary isolation with present imperilment across the amphibian tree of life. Nat. Ecol. Evol. 2, 850–858 (2018).Article 
PubMed 

Google Scholar 
Ho, L. S. T. & Ané, C. A linear-time algorithm for Gaussian and non-Gaussian trait evolution models. Syst. Biol. 63, 397–408 (2014).Article 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Verhoeven, K. J. F., Simonsen, K. L. & McIntyre, L. M. Implementing false discovery rate control: increasing your power. Oikos 108, 643–647 (2005).Article 

Google Scholar 
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
Google Scholar 
Bivand, R. et al. spdep: Spatial dependence: weighting schemes, statistics and models. R package version 0.7-4 (2017).Bjornstad, O. N. ncf: Spatial covariance functions. R package version 1.2-1 (2018). More

Gordon, D. M. Anim. Behav. 49, 649–659 (1995).Article 

Google Scholar 
Gordon, D. M. The Ecology of Collective Behavior (Princeton Univ. Press, in the press).Gordon, D. M. Anim. Behav. 38, 194–204 (1989).Article 

Google Scholar 
Greene, M. J. & Gordon, D. M. Nature 423, 32 (2003).Article 
CAS 

Google Scholar 
Pinter-Wollman, N. et al. Anim. Behav. 86, 197–207 (2013).Article 

Google Scholar 
Gordon, D. M., Guetz, A., Greene, M. J. & Holmes, S. Behav. Ecol. 22, 429–435 (2011).Article 

Google Scholar 
Prabhakar, B., Dektar, K. N. & Gordon, D. M. PLOS Comput. Biol. 8, e1002670 (2012).Article 
CAS 

Google Scholar 
Davidson, J. D., Arauco-Aliaga, R. P., Crow, S., Gordon, D. M. & Goldman, M. S. Front. Ecol. Evol. 4, 115 (2016).Article 

Google Scholar 
Pagliara, R., Gordon, D. M. & Leonard, N. E. PLOS Comput. Biol. 14, e1006200 (2018).Article 

Google Scholar 
Friedman, D. A. et al. iScience 8, 283–294 (2018).Article 
CAS 

Google Scholar 
Gordon, D. M. Ant Encounters: Interaction Networks and Colony Behavior (Princeton Univ. Press, 2010).Sundaram, M., Steiner, E. & Gordon, D. M. Ecol. Monogr. 92, e1503 (2022).Article 

Google Scholar 
Ingram, K. K., Pilko, A., Heer, J. & Gordon, D. M. J. Anim. Ecol. 82, 540–550 (2013).Article 

Google Scholar 
Gordon, D. M. Nature 498, 91–93 (2013).Article 
CAS 

Google Scholar  More

Isotope labelling and phylogenetic analysis of a natural marine bacterioplankton population at seaMediterranean seawater was collected during August 2017 (station N1200, 32.45° N, 34.37 °E) from 11 depths by Niskin bottles and divided into triplicate 250 ml polycarbonate bottles. Two bottles from each depth were labelled with 1 mM sodium bicarbonate-13C and 1 mM ammonium-15N chloride (Sigma-Aldrich), and all three bottles (two labelled and one control) were incubated at the original depth and station at sea for 3.5 h around mid-day. The stable isotopes were chosen to enable direct comparison of C and N uptake in single cells, and the short incubation time was chosen to minimize isotope dilution and potential recycling and transfer of 13C and 15N between community members25. After incubation, bottles were brought back on board and the incubations were stopped by fixing with 2× electron-microscopy-grade glutaraldehyde (2.5% final concentration) and stored at 4 °C until sorting analysis. Cell sorting, NanoSIMS analyses and the calculation of uptake rates were performed as described in Roth-Rosenberg et al.26.DNA collection and extraction from seawaterSamples for DNA were collected on 0.22 µm Sterivex filters (Millipore). Excess water was removed using a syringe, 1 ml lysis buffer (40 mM EDTA, 50 mM Tris pH 8.3, and 0.75 M sucrose) was added and both ends of the filter were closed with parafilm. Samples were kept at −80 °C until extraction. DNA was extracted by using a semi-automated protocol including manual chemical cell lysis before automated steps using the QIAamp DNA Mini Protocol: DNA Purification from Blood or Body Fluids (Spin Protocol, starting from step 6, at the BioRap unit, Faculty of Medicine, Technion). The manual protocol began with thawing the samples, then the storage buffer was removed using a syringe and 170 µl lysis buffer added to the filters. Thirty microlitres of Lysozyme (20 mg ml−1) were added to the filters and incubated at 37 °C for 30 min. After incubation, 20 µl proteinase K and 200 µl buffer AL (from the Qiagen kit) were added to the tube for 1 h at 56 °C (with agitation). The supernatant was transferred to a new tube, and DNA was extracted using the QIAcube automated system. All DNA samples were eluted in 100 μl DNA-free distilled water.ITS PCR amplificationPCR amplification of the ITS was carried out with specific primers for Prochlorococcus CS1_16S_1247F (5′-ACACTGACGACATGGTTCTACACGTACTACAATGCTACGG) and Cs2_ITS_Ar (5′-TACGGTAGCAGAGACTTGGTCTGGACCTCACCCTTATCAGGG)21,22. The first PCR was performed in triplicate in a total volume of 25 μl containing 0.5 ng of template, 12.5 μl of MyTaq Red Mix (Bioline) and 0.5 μl of 10 μM of each primer. The amplification conditions comprised steps at 95 °C for 5 min, 28/25 (16 S/ITS) cycles at 95 °C for 30 s, 50 °C for 30 s and 72 °C for 1 min followed by one step of 5 min at 72 °C. All PCR products were validated on a 1% agarose gel, and triplicates were pooled. Subsequently, a second PCR amplification was performed to prepare libraries. These were pooled and after a quality control sequenced (2 × 250 paired-end reads) using an Illumina MiSeq sequencer. Library preparation and pooling were performed at the DNA Services facility, Research Resources Center, University of Illinois at Chicago. MiSeq sequencing was performed at the W.M. Keck Center for Comparative and Functional Genomics at the University of Illinois at Urbana-Champaign.ITS sequence processingPaired-end reads were analysed using the Dada2 pipeline46. The quality of the sequences per sample was examined using the Dada2 ‘plotQualityProfile’ command. Quality filtering was performed using the Dada2 ‘filterAndTrim’ command with parameters for quality filtering truncLen=c(290,260), maxN=0, maxEE=c(2,2), truncQ=2, rm.phix=TRUE, trimLeft=c(20,20). Following error estimation and dereplication, the Dada2 algorithm was used to correct sequences. Merging of the forward and reverse reads was done with minimum overlap of 4 bp. Detection and removal of suspected chimaeras was done with command ‘removeBimeraDenovo’. In total, 388,417 sequences in 484 amplicon sequence variants were counted. The amplicon sequence variants were aligned in MEGA6 (ref. 47), and the first ~295 nucleotides, corresponding to the 16S gene, were trimmed. The ITS sequences were then classified using BLASTn against a custom database of ITS sequences from cultured Prochlorococcus and Synechococcus strains as well as from uncultured HL and LL clades.Individual-based modelPlanktonIndividuals.jl (v0.1.9) was used to run the individual-based simulations48. Briefly, the cells fix inorganic carbon through photosynthesis and nitrogen, phosphorus and DOC from the water column into intracellular quotas and grow until division or grazing. Cell division is modelled as a probabilistic function of cell size. Grazing is represented by a quadratic probabilistic function of cell population. Cells consume nutrient resources, which are represented as Eulerian, density-based tracers. A full documentation of state variables and model equations are available online at https://juliaocean.github.io/PlanktonIndividuals.jl/dev/. Equations related to mixotrophy are shown below as an addition to the online documentation.$$V_{{mathrm{DOC}}} = V_{{mathrm{DOC}}}^{{mathrm{max}}} cdot {{mathrm{max}}}left( {0.0,{{mathrm{min}}}left( {1.0,,frac{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}}}{{q_{mathrm{C}}^{{mathrm{max}}} – q_{mathrm{C}}^{{mathrm{min}}}}}} right)} right) cdot frac{{{mathrm{DOC}}}}{{{mathrm{DOC}} + K_{{mathrm{DOC}}}^{{mathrm{sat}}}}}$$
(1)
$$f_{{mathrm{PS}}} = frac{{P_{mathrm{S}}}}{{P_{mathrm{S}} + V_{{mathrm{DOC}}}}}$$
(2)
$$V_{{mathrm{DOC}}} = 0,,{mathrm{if}},f_{{mathrm{PS}}} < f_{{mathrm{PS}}}^{{mathrm{min}}}$$ (3) where VDOC is the cell-specific DOC uptake rate (mol C cell−1 s−1), (V_{{mathrm{DOC}}}^{{mathrm{max}}}) is the maximum cell-specific DOC uptake rate (mol C cell−1 s−1), (q_{mathrm{C}}^{{mathrm{max}}}) is the maximum cell carbon quota (mol C cell−1), (q_{mathrm{C}}^{{mathrm{min}}}) is the minimum cell carbon quota (mol C cell−1). The maximum and minimum functions here is used to keep qC between (q_{mathrm{C}}^{{mathrm{min}}}) and (q_{mathrm{C}}^{{mathrm{max}}}). (K_{{mathrm{DOC}}}^{{mathrm{sat}}}) is the half-saturation constant for DOC uptake (mol C m−3). fPS is the fraction of fixed C originating from photosynthesis (PS, mol C cell−1 s−1). DOC uptake stops when fPS is smaller than (f_{{mathrm{PS}}}^{{mathrm{min}}})(minimum fraction of fixed C originating form photosynthesis, 1% by default) according to laboratory studies of Prochlorococcus that showed that they cannot survive long exposure to darkness (beyond several days) even when supplied with organic carbon sources13. (1 − fPS) is also shown in Fig. 3 as the contribution of DOC uptake.We set up two separate simulations; each of them has a population of either an obligate photo-autotroph or a mixotroph that also consumes DOC. The initial conditions and parameters (Supplementary Table 3) are the same for the two simulations except the ability of mixotrophy. The simulations were run with a timestep of 1 min for 360 simulated days to achieve a steady state. We run the two simulations for multiple times in order to get the range of the stochastic processes.Evaluation of autotrophic growth ratesWe evaluated the carbon-specific, daily-averaged carbon fixation rate, ℙ as a function of light intensity (I, µE), following Platt et al.33:$${Bbb P} = frac{1}{{Delta t}}{int}_0^{Delta t} {frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}} P_{mathrm{S}}^{{mathrm{Chl}}}left( {1 - e^{ - alpha _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}} right)e^{ - beta _{{mathrm{Chl}}}I/P_{mathrm{S}}^{{mathrm{Chl}}}}Delta t$$ (4) Here, (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl are empirically determined coefficients representing the chlorophyll-a-specific carbon fixation rate (mol C (mol Chl)−1 s−1), the initial slope of the photosynthesis–light relationship and photo-inhibition effects at high photon fluxes, respectively. We impose empirically determined values for (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl from the published study of Moore and Chisholm24. The natural Prochlorococcus community comprises HL and LL ecotypes, which have different values of (P_{mathrm{S}}^{{mathrm{Chl}}}), αChl and βChl, and the community growth rate is expected to be between that of HL extremes and LL extremes. Therefore, we use photo-physiological parameters for an HL-adapted ecotype (MIT9215), acclimated at 70 µmol photons m−2 s−1 and an LL-adapted ecotype (MIT9211), acclimated 9 µmol photons m−2 s−1. The models with these values are shown as the different lines in Fig. 2b,d. I is the hourly PAR, estimated by scaling the observed noon value at each depth with a diurnal variation evaluated from astronomical formulae based on geographic location and time of year37,38.(frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is the molar chlorophyll-a to carbon ratio, which is modelled as a function of growth rate and light intensity using the Inomura34 model (equation 17 therein) where parameters were calibrated with laboratory data from Healey49. In addition, the maximum growth rate ((mu _{{mathrm{max}}}^I)) based on macromolecular allocation is also estimated using the Inomura model (equation 30 therein). An initial guess of the growth rate and the empirically informed light intensity are used to estimate (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}), which is then used to evaluate the light-limited, photoautotrophic growth rate$${Bbb V}_{mathrm{C}}^{{mathrm{auto}}} = min left( {{Bbb P} - K_{mathrm{R}},mu _{{mathrm{max}}}^I} right)$$ (5) from which the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) is again updated. The light-limited growth rate is used to re-evaluate the (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}). Repeating this sequence until the values converge, ({Bbb V}_{mathrm{C}}^{{mathrm{auto}}}) and (frac{{q_{{mathrm{Chl}}}}}{{q_{mathrm{C}}}}) are solved iteratively.The nitrogen-specific uptake rate of fixed nitrogen (day−1) is modelled as$${Bbb V}_{{{mathrm{N}}}} = {Bbb V}_{mathrm{N}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{N}}}}frac{N}{{N + K_{{{mathrm{N}}}}}}$$ (6) where values of the maximum uptake rate, ({Bbb V}_{mathrm{N}}^{{mathrm{max}}}), and half-saturation, KN, are determined from empirical allometric scalings35, along with a nitrogen cell quota QN from Bertilsson et al.39.The P-limited growth rate, or the phosphorus-specific uptake rate of phosphate (day−1), is modelled as$${Bbb V}_{mathrm{P}} = {Bbb V}_{mathrm{P}}^{{mathrm{max}}}frac{1}{{Q_{mathrm{P}}}}frac{{{mathrm{PO}_{4}}^{3 - }}}{{{mathrm{PO}_{4}}^{3 - } + K_{mathrm{P}}}}$$ (7) where values of the maximum uptake rate, ({Bbb V}_{mathrm{P}}^{{mathrm{max}}}). and half-saturation, KP, are determined from empirical allometric scalings35, along with a nitrogen cell quota QP from Bertilsson et al.39.Iron uptake is modelled as a linear function of cell surface area (SA), with rate constant ((k_{{mathrm{Fe}}}^{{mathrm{SA}}})) following Lis et al.36.$${Bbb V}_{{mathrm{Fe}}} = k_{{mathrm{Fe}}}^{{mathrm{SA}}} cdot {mathrm{SA}}frac{1}{{Q_{{mathrm{Fe}}}}}{mathrm{Fe}}$$ (8) The potential light-, nitrogen-, phosphorus- and iron-limited growth rates (({Bbb V}_{mathrm{C}},{Bbb V}_{mathrm{N}},{Bbb V}_{mathrm{P}},{Bbb V}_{{mathrm{Fe}}})) were evaluated at each depth in the water column and the minimum is the local modelled photo-autotrophic growth rate estimate, assuming no mixotrophy (Fig. 2b,d, blue lines). Parameters used in this evaluation are listed in Supplementary Table 2.An important premise of this study is that heterotrophy is providing for the shortfall in carbon under very low light conditions, but not nitrogen. It is known that Prochlorococcus can assimilate amino acids9 and therefore the stoichiometry of the heterotrophic contribution might alter the interpretations. However, it is also known that Prochlorococcus can exude amino acids40, which might cancel out the effects on the stoichiometry of Prochlorococcus.For the estimates of phototrophic growth rate from local environmental conditions (Fig. 2) we employed photo-physiological parameters from laboratory cultures of Prochlorococcus24. For the purposes of this study, we have assumed that the photosynthetic rates predicted are net primary production, which means that autotrophic respiration has been accounted for in the measurement. However, the incubations in that study were of relatively short timescale (45 min), which might suggest they are perhaps more representative of gross primary production. If this is the case, our estimates of photo-autotrophic would be even lower after accounting for autotrophic respiration, and thus would demand a higher contribution from heterotrophic carbon uptake. In this regard, our estimates might be considered a lower bound for organic carbon assimilation.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More