Philippa Kaur

Azam, F. et al. The ecological role of water-column microbes in the sea. Marine Ecol. Prog. Ser. 10, 257–263 (1983).Fuhrman, J. A. & Caron D. A. in Manual of Environmental Microbiology (eds Yates, M. V. et al.) 4.2.2–4.2.2.-34 (ASM Press, 2016).Gasol, J. M. & Kirchman, D. L. Microbial Ecology of the Oceans (John Wiley & Sons, 2018).Fuhrman, J. A. et al. A latitudinal diversity gradient in planktonic marine bacteria. Proc. Natl Acad. Sci. 105, 7774–7778 (2008).Article 
ADS 
CAS 

Google Scholar 
Gilbert, J. A. et al. The seasonal structure of microbial communities in the Western English Channel. Environ. Microbiol. 11, 3132–3139 (2009).Article 
CAS 

Google Scholar 
Gilbert, J. A. et al. Defining seasonal marine microbial community dynamics. ISME J. 6, 298–308 (2012).Article 
CAS 

Google Scholar 
Hatosy, S. M. et al. Beta diversity of marine bacteria depends on temporal scale. Ecology 94, 1898–1904 (2013).Article 

Google Scholar 
Ward, C. S. et al. Annual community patterns are driven by seasonal switching between closely related marine bacteria. ISME J. 11, 1412–1422 (2017).Article 

Google Scholar 
Fuhrman, J. A. et al. Annually reoccurring bacterial communities are predictable from ocean conditions. Proc. Natl Acad. Sci. 103, 13104–13109 (2006).Article 
ADS 
CAS 

Google Scholar 
Gonzalez, J. M., Sherr, E. B. & Sherr, B. F. Size-selective grazing on bacteria by natural assemblages of estuarine flagellates and ciliates. Appl. Environ. Microbiol. 56, 583–589 (1990).Article 
ADS 
CAS 

Google Scholar 
Guixa-Boixereu, N., Vaque, D., Gasol, J. M. & Pedros-Alio, C. Distribution of viruses and their potential effect on bacterioplankton in an oligotrophic marine system. Aquat. Microb. Ecol. 19, 205–213 (1999).Article 

Google Scholar 
Šimek, K. et al. Shifts in bacterial community composition associated with different microzooplankton size fractions in a eutrophic reservoir. Limnol. Oceanogr. 44, 1634–1644 (1999).Article 
ADS 

Google Scholar 
Hewson, I., Vargo, G. & Fuhrman, J. Bacterial diversity in shallow oligotrophic marine benthos and overlying waters: effects of virus infection, containment, and nutrient enrichment. Microb. Ecol. 46, 322–336 (2003).Article 
CAS 

Google Scholar 
Schwalbach, M. S., Hewson, I. & Fuhrman, J. A. Viral effects on bacterial community composition in marine plankton microcosms. Aquat. Microb. Ecol. 34, 117–127 (2004).Article 

Google Scholar 
Winter, C., Smit, A., Herndl, G. J. & Weinbauer, M. G. Linking bacterial richness with viral abundance and prokaryotic activity. Limnol. Oceanogr. 50, 968–977 (2005).Article 
ADS 

Google Scholar 
Chow, C.-E. T., Kim, D. Y., Sachdeva, R., Caron, D. A. & Fuhrman, J. A. Top-down controls on bacterial community structure: microbial network analysis of bacteria, T4-like viruses and protists. ISME J. 8, 816–829 (2014).Article 
CAS 

Google Scholar 
Suzuki, S. et al. Comparison of community structures between particle-associated and free-living prokaryotes in tropical and subtropical Pacific Ocean surface waters. J. Oceanogr. 73, 383–395 (2017).Article 
CAS 

Google Scholar 
Milici, M. et al. Diversity and community composition of particle‐associated and free‐living bacteria in mesopelagic and bathypelagic Southern Ocean water masses: evidence of dispersal limitation in the Bransfield Strait. Limnol. Oceanogr. 62, 1080–1095 (2017).Article 
ADS 

Google Scholar 
D’ambrosio, L., Ziervogel, K., MacGregor, B., Teske, A. & Arnosti, C. Composition and enzymatic function of particle-associated and free-living bacteria: a coastal/offshore comparison. ISME J. 8, 2167–2179 (2014).Article 

Google Scholar 
Rieck, A., Herlemann, D. P., Jürgens, K. & Grossart, H.-P. Particle-associated differ from free-living bacteria in surface waters of the Baltic Sea. Front. Microbiol. 6, 1297 (2015).Article 

Google Scholar 
Yung, C.-M., Ward, C. S., Davis, K. M., Johnson, Z. I. & Hunt, D. E. Insensitivity of diverse and temporally variable particle-associated microbial communities to bulk seawater environmental parameters. Appl. Environ. Microbiol. 82, 3431–3437 (2016).Article 
ADS 
CAS 

Google Scholar 
Buchan, A., LeCleir, G. R., Gulvik, C. A. & González, J. M. Master recyclers: features and functions of bacteria associated with phytoplankton blooms. Nat. Rev. Microbiol. 12, 686–698 (2014).Article 
CAS 

Google Scholar 
Duret, M. T., Lampitt, R. S. & Lam, P. Prokaryotic niche partitioning between suspended and sinking marine particles. Environ. Microbiol. Rep. 11, 386–400 (2019).Article 
CAS 

Google Scholar 
Crespo, B. G., Pommier, T., Fernández‐Gómez, B. & Pedrós‐Alió, C. Taxonomic composition of the particle‐attached and free‐living bacterial assemblages in the Northwest Mediterranean Sea analyzed by pyrosequencing of the 16S rRNA. Microbiologyopen 2, 541–552 (2013).Article 
CAS 

Google Scholar 
Mestre, M., Borrull, E., Sala, M. & Gasol, J. M. Patterns of bacterial diversity in the marine planktonic particulate matter continuum. ISME J. 11, 999–1010 (2017).Yeh, Y. C. et al. Comprehensive single‐PCR 16S and 18S rRNA community analysis validated with mock communities, and estimation of sequencing bias against 18S. Environ. Microbiol. 23, 3240–3250 (2021).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).Article 
CAS 

Google Scholar 
Needham, D. M. et al. Dynamics and interactions of highly resolved marine plankton via automated high-frequency sampling. ISME J. 12, 2417 (2018).Article 
CAS 

Google Scholar 
McNichol, J., Berube, P. M., Biller, S. J. & Fuhrman, J. A. Evaluating and improving small subunit rRNA PCR primer coverage for bacteria, archaea, and eukaryotes using metagenomes from global ocean surveys. Msystems 6, e00565–00521 (2021).Article 
CAS 

Google Scholar 
Chow, C. E. T. & Fuhrman, J. A. Seasonality and monthly dynamics of marine myovirus communities. Environ. Microbiol. 14, 2171–2183 (2012).Article 

Google Scholar 
Filée, J., Tétart, F., Suttle, C. A. & Krisch, H. Marine T4-type bacteriophages, a ubiquitous component of the dark matter of the biosphere. Proc. Natl Acad. Sci. 102, 12471–12476 (2005).Article 
ADS 

Google Scholar 
Pagarete, A. et al. Strong seasonality and interannual recurrence in marine myovirus communities. Appl. Environ. Microbiol. 79, 6253–6259 (2013).Article 
ADS 
CAS 

Google Scholar 
Comeau, A. M. & Krisch, H. M. The capsid of the T4 phage superfamily: the evolution, diversity, and structure of some of the most prevalent proteins in the biosphere. Mol. Biol. Evolution 25, 1321–1332 (2008).Article 
CAS 

Google Scholar 
Needham, D. M. et al. Short-term observations of marine bacterial and viral communities: patterns, connections and resilience. ISME J. 7, 1274–1285 (2013).Article 
CAS 

Google Scholar 
Needham, D. M., Sachdeva, R. & Fuhrman, J. A. Ecological dynamics and co-occurrence among marine phytoplankton, bacteria and myoviruses shows microdiversity matters. ISME J. 11, 1614–1629 (2017).Article 

Google Scholar 
Ahlgren, N. A., Perelman, J. N., Yeh, Y. C. & Fuhrman, J. A. Multi‐year dynamics of fine‐scale marine cyanobacterial populations are more strongly explained by phage interactions than abiotic, bottom‐up factors. Environ. Microbiol. 21, 2948–2963 (2019).Article 
CAS 

Google Scholar 
Ren, J., Ahlgren, N. A., Lu, Y. Y., Fuhrman, J. A. & Sun, F. VirFinder: a novel k-mer based tool for identifying viral sequences from assembled metagenomic data. Microbiome 5, 1–20 (2017).Article 

Google Scholar 
Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).Article 

Google Scholar 
Ignacio-Espinoza, J. C., Ahlgren, N. A. & Fuhrman, J. A. Long-term stability and Red Queen-like strain dynamics in marine viruses. Nat. Microbiol. 5, 265–271 (2020).Article 
CAS 

Google Scholar 
Brum, J. R. et al. Patterns and ecological drivers of ocean viral communities. Science 348, (2015).Brown, M. V. et al. Global biogeography of SAR11 marine bacteria. Mol. Syst. Biol. 8, 595 (2012).Article 
ADS 

Google Scholar 
Johnson, Z. I. et al. Niche partitioning among Prochlorococcus ecotypes along ocean-scale environmental gradients. Science 311, 1737–1740 (2006).Article 
ADS 
CAS 

Google Scholar 
Zwirglmaier, K. et al. Global phylogeography of marine Synechococcus and Prochlorococcus reveals a distinct partitioning of lineages among oceanic biomes. Environ. Microbiol. 10, 147–161 (2008).
Google Scholar 
Martiny, A. C., Tai, A. P., Veneziano, D., Primeau, F. & Chisholm, S. W. Taxonomic resolution, ecotypes and the biogeography of Prochlorococcus. Environ. Microbiol. 11, 823–832 (2009).Article 

Google Scholar 
Bond, N. A., Cronin, M. F., Freeland, H. & Mantua, N. Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys. Res. Lett. 42, 3414–3420 (2015).Article 
ADS 

Google Scholar 
Di Lorenzo, E. & Mantua, N. Multi-year persistence of the 2014/15 North Pacific marine heatwave. Nat. Clim. Change 6, 1042–1047 (2016).Article 
ADS 

Google Scholar 
Traving, S. J. et al. Prokaryotic responses to a warm temperature anomaly in northeast subarctic Pacific waters. Commun. Biol. 4, 1–12 (2021).Article 

Google Scholar 
Peña, M. A., Nemcek, N. & Robert, M. Phytoplankton responses to the 2014–2016 warming anomaly in the northeast subarctic Pacific Ocean. Limnol. Oceanogr. 64, 515–525 (2019).Article 
ADS 

Google Scholar 
Yang, B., Emerson, S. R. & Peña, M. A. The effect of the 2013–2016 high temperature anomaly in the subarctic Northeast Pacific (the “Blob”) on net community production. Biogeosciences 15, 6747–6759 (2018).Article 
ADS 
CAS 

Google Scholar 
Cavole, L. M. et al. Biological impacts of the 2013–2015 warm-water anomaly in the Northeast Pacific: winners, losers, and the future. Oceanography 29, 273–285 (2016).Article 

Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).Article 
CAS 

Google Scholar 
Grossart, H. P., Levold, F., Allgaier, M., Simon, M. & Brinkhoff, T. Marine diatom species harbour distinct bacterial communities. Environ. Microbiol. 7, 860–873 (2005).Article 
CAS 

Google Scholar 
Teeling, H. et al. Substrate-controlled succession of marine bacterioplankton populations induced by a phytoplankton bloom. Science 336, 608–611 (2012).Article 
ADS 
CAS 

Google Scholar 
Chafee, M. et al. Recurrent patterns of microdiversity in a temperate coastal marine environment. ISME J. 12, 237–252 (2018).Article 

Google Scholar 
Teeling, H. et al. Recurring patterns in bacterioplankton dynamics during coastal spring algae blooms. elife 5, e11888 (2016).Article 

Google Scholar 
Unfried, F. et al. Adaptive mechanisms that provide competitive advantages to marine bacteroidetes during microalgal blooms. ISME J. 12, 2894–2906 (2018).Article 
CAS 

Google Scholar 
Francis, T. B. et al. Changing expression patterns of TonB-dependent transporters suggest shifts in polysaccharide consumption over the course of a spring phytoplankton bloom. ISME J. 15, 2336–2350 (2021).Thingstad, T. F. & Lignell, R. Theoretical models for the control of bacterial growth rate, abundance, diversity and carbon demand. Aquat. Microb. Ecol. 13, 19–27 (1997).Article 

Google Scholar 
Thingstad, T. F., Våge, S., Storesund, J. E., Sandaa, R.-A. & Giske, J. A theoretical analysis of how strain-specific viruses can control microbial species diversity. Proc. Natl Acad. Sci. 111, 7813–7818 (2014).Article 
ADS 
CAS 

Google Scholar 
Thingstad, T. F., Pree, B., Giske, J. & Våge, S. What difference does it make if viruses are strain-, rather than species-specific? Front. Microbiol. 6, 320 (2015).Article 

Google Scholar 
Prokopowich, C. D., Gregory, T. R. & Crease, T. J. The correlation between rDNA copy number and genome size in eukaryotes. Genome 46, 48–50 (2003).Article 
CAS 

Google Scholar 
Zhu, F., Massana, R., Not, F., Marie, D. & Vaulot, D. Mapping of picoeucaryotes in marine ecosystems with quantitative PCR of the 18S rRNA gene. FEMS Microbiol. Ecol. 52, 79–92 (2005).Article 
CAS 

Google Scholar 
Sintes, E. & Del Giorgio, P. A. Feedbacks between protistan single-cell activity and bacterial physiological structure reinforce the predator/prey link in microbial foodwebs. Front. Microbiol. 5, 453 (2014).Article 

Google Scholar 
Del Giorgio, P. A. et al. Bacterioplankton community structure: protists control net production and the proportion of active bacteria in a coastal marine community. Limnol. Oceanogr. 41, 1169–1179 (1996).Article 
ADS 

Google Scholar 
Andersson, A., Larsson, U. & Hagström, Å. Size-selective grazing by a microflagellate on pelagic bacteria. Marine Ecol. Prog. Ser. 33, 51–57 (1986).Pernthaler, J. Predation on prokaryotes in the water column and its ecological implications. Nat. Rev. Microbiol. 3, 537–546 (2005).Article 
CAS 

Google Scholar 
Baltar, F. et al. Marine bacterial community structure resilience to changes in protist predation under phytoplankton bloom conditions. ISME J. 10, 568–581 (2016).Article 

Google Scholar 
Suzuki, M. T. Effect of protistan bacterivory on coastal bacterioplankton diversity. Aquat. Microb. Ecol. 20, 261–272 (1999).Article 

Google Scholar 
Yokokawa, T. & Nagata, T. Growth and grazing mortality rates of phylogenetic groups of bacterioplankton in coastal marine environments. Appl. Environ. Microbiol. 71, 6799–6807 (2005).Article 
ADS 
CAS 

Google Scholar 
Eren, A. M. et al. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol. Evolution 4, 1111–1119 (2013).Article 

Google Scholar 
Coleman, M. L. & Chisholm, S. W. Code and context: Prochlorococcus as a model for cross-scale biology. Trends Microbiol. 15, 398–407 (2007).Article 
CAS 

Google Scholar 
Scanlan, D. J. et al. Ecological genomics of marine picocyanobacteria. Microbiol. Mol. Biol. Rev. 73, 249–299 (2009).Article 
CAS 

Google Scholar 
Moore, L. R., Rocap, G. & Chisholm, S. W. Physiology and molecular phylogeny of coexisting Prochlorococcus ecotypes. Nature 393, 464–467 (1998).Article 
ADS 
CAS 

Google Scholar 
Rusch, D. B., Martiny, A. C., Dupont, C. L., Halpern, A. L. & Venter, J. C. Characterization of Prochlorococcus clades from iron-depleted oceanic regions. Proc. Natl Acad. Sci. 107, 16184–16189 (2010).Article 
ADS 
CAS 

Google Scholar 
Larkin, A. A. et al. Persistent El Niño driven shifts in marine cyanobacteria populations. PloS ONE 15, e0238405 (2020).Article 
CAS 

Google Scholar 
Arandia‐Gorostidi, N. et al. Warming the phycosphere: differential effect of temperature on the use of diatom‐derived carbon by two copiotrophic bacterial taxa. Environ. Microbiol. 22, 1381–1396 (2020).Article 

Google Scholar 
Arandia‐Gorostidi, N., Huete‐Stauffer, T. M., Alonso‐Sáez L, G. & Morán, X. A. Testing the metabolic theory of ecology with marine bacteria: different temperature sensitivity of major phylogenetic groups during the spring phytoplankton bloom. Environ. Microbiol. 19, 4493–4505 (2017).Article 

Google Scholar 
Fagan, A. J., Moreno, A. R. & Martiny, A. C. Role of ENSO conditions on particulate organic matter concentrations and elemental ratios in the Southern California Bight. Front. Mar. Sci. 6, 386 (2019).Article 

Google Scholar 
Chang, C. W. et al. Reconstructing large interaction networks from empirical time series data. Ecol. Lett. 24, 2763–2774 (2021).Article 

Google Scholar 
Lie, A. A., Kim, D. Y., Schnetzer, A. & Caron, D. A. Small-scale temporal and spatial variations in protistan community composition at the San Pedro Ocean Time-series station off the coast of southern California. Aquat. Microb. Ecol. 70, 93–110 (2013).Article 

Google Scholar 
Yeh, Y.-C., Needham, D. M., Sieradzki, E. T. & Fuhrman, J. A. Taxon disappearance from microbiome analysis reinforces the value of mock communities as a standard in every sequencing run. MSystems 3, e00023–00018 (2018).Article 

Google Scholar 
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).Article 
CAS 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).Article 
CAS 

Google Scholar 
Guillou, L. et al. The Protist Ribosomal Reference database (PR2): a catalog of unicellular eukaryote small sub-unit rRNA sequences with curated taxonomy. Nucleic Acids Res. 41, D579–D604 (2013).
Google Scholar 
Amir, A. et al. Deblur rapidly resolves single-nucleotide community sequence patterns. MSystems 2, (2017).Decelle, J. et al. Phyto REF: a reference database of the plastidial 16S rRNA gene of photosynthetic eukaryotes with curated taxonomy. Mol. Ecol. Resour. 15, 1435–1445 (2015).Article 
CAS 

Google Scholar 
Amin, S. et al. Interaction and signalling between a cosmopolitan phytoplankton and associated bacteria. Nature 522, 98–101 (2015).Article 
ADS 
CAS 

Google Scholar 
Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).Article 
ADS 

Google Scholar 
Hill, M. O. & Gauch, H. G. J. Detrended correspondence analysis: an improved ordination technique. Vegetatio 42, 47–58 (1980).Ter Braak, C. J. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

Google Scholar 
Peres-Neto, P. R., Legendre, P., Dray, S. & Borcard, D. Variation partitioning of species data matrices: estimation and comparison of fractions. Ecology 87, 2614–2625 (2006).Article 

Google Scholar  More

Waples, R. S. & Gaggiotti, O. INVITED REVIEW: What is a population? An empirical evaluation of some genetic methods for identifying the number of gene pools and their degree of connectivity. Mol. Ecol. 15, 1419–1439 (2006).Article 
CAS 

Google Scholar 
Mendez, M., Rosenbaum, H. C., Subramaniam, A., Yackulic, C. & Bordino, P. Isolation by environmental distance in mobile marine species: Molecular ecology of franciscana dolphins at their southern range. Mol. Ecol. 19, 2212–2228 (2010).Article 
CAS 

Google Scholar 
De Meeûs, T. et al. Population genetics and molecular epidemiology or how to “débusquer la bête”. Infect. Genet. Evol. 7, 308–332 (2007).Article 

Google Scholar 
Durigan, M. et al. Population genetic analysis of Giardia duodenalis: Genetic diversity and haplotype sharing between clinical and environmental sources. MicrobiologyOpen 6, e00424 (2017).Article 

Google Scholar 
Amaral, A. R. et al. Seascape genetics of a globally distributed, highly mobile marine mammal: The short-beaked common dolphin (genus Delphinus). PLoS ONE 7, e31482 (2012).Article 
ADS 
CAS 

Google Scholar 
Mendez, M. et al. Molecular ecology meets remote sensing: Environmental drivers to population structure of humpback dolphins in the Western Indian Ocean. Heredity 107, 349–361 (2011).Article 
CAS 

Google Scholar 
de los Angeles Bayas-Rea, R., Félix, F. & Montufar, R. Genetic divergence and fine scale population structure of the common bottlenose dolphin (Tursiops truncatus, Montagu) found in the Gulf of Guayaquil. Ecuador. PeerJ 6, e4589 (2018).Article 

Google Scholar 
Natoli, A., Peddemors, V. M. & Rus Hoelzel, A. Population structure and speciation in the genus Tursiops based on microsatellite and mitochondrial DNA analyses. J. Evol. Biol. 17, 363–375 (2004).Article 
CAS 

Google Scholar 
Oliveira, L. R., Loizaga De Castro, R., Cárdenas-Alayza, S. & Bonatto, S. L. Conservation genetics of South American aquatic mammals: An overview of gene diversity, population structure, phylogeography, non-invasive methods and forensics. Mammal Rev. 42, 275–303 (2012).Article 

Google Scholar 
Vollmer, N. L. & Rosel, P. E. Fine-scale population structure of common bottlenose dolphins (Tursiops truncatus) in offshore and coastal waters of the US Gulf of Mexico. Mar. Biol. 164, 1–15 (2017).Article 

Google Scholar 
MacLeod, C. D. Global climate change, range changes and potential implications for the conservation of marine cetaceans: A review and synthesis. Endanger. Species Res. 7, 125–136 (2009).Article 

Google Scholar 
Hartl, D. L., Clark, A. G. & Clark, A. G. Principles of Population Genetics, Vol. 116 (Sinauer associates Sunderland, 1997).Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145 (2004).Article 
ADS 
CAS 

Google Scholar 
Reeves, R. R., Smith, B. D., Crespo, E. A. & Notarbartolo di Sciara, G. Dolphins, whales and porpoises: 2002–2010 conservation action plan for the world’s cetaceans, Vol. 58 (IUCN, 2003).Crespo, E. A. & Hall, M. A. In Marine Mammals, 463–490 (Springer, 2002).Crespo, E. A. et al. Direct and indirect effects of highseas fisheries on the marine mammal populations in the northern and central Patagonian coast. J. Northwest Atl. Fish. Sci. 22, 189–207 (1997).Article 

Google Scholar 
Harlin-Cognato, A. D., Markowitz, T., Würsig, B. & Honeycutt, R. L. Multi-locus phylogeography of the dusky dolphin (Lagenorhynchus obscurus): Passive dispersal via the west-wind drift or response to prey species and climate change?. BMC Evol. Biol. 7, 1–17 (2007).Article 

Google Scholar 
Hoelzel, A. Evolution of population genetic structure in marine mammal species. In Population genetics for animal conservation, 294–318 (Cambridge University Press, Cambridge, 2009).Fraser, C. I., Nikula, R., Ruzzante, D. E. & Waters, J. M. Poleward bound: Biological impacts of Southern Hemisphere glaciation. Trends Ecol. Evol. 27, 462–471 (2012).Article 

Google Scholar 
Louis, M. et al. Influence of past climate change on phylogeography and demographic history of narwhals, Monodon monoceros. Proc. R. Soc. B 287, 20192964 (2020).Article 
CAS 

Google Scholar 
Skovrind, M. et al. Circumpolar phylogeography and demographic history of beluga whales reflect past climatic fluctuations. Mol. Ecol. 30, 2543–2559 (2021).Article 

Google Scholar 
Foote, A. D. et al. Ancient DNA reveals that bowhead whale lineages survived Late Pleistocene climate change and habitat shifts. Nat. Commun. 4, 1–7 (2013).Article 

Google Scholar 
Crespo, E. A. et al. Status, population trend and genetic structure of South American fur seals, Arctocephalus australis, in southwestern Atlantic waters. Mar. Mamm. Sci. 31, 866–890 (2015).Article 

Google Scholar 
Feijoo, M., Lessa, E. P., De Castro, R. L. & Crespo, E. A. Mitochondrial and microsatellite assessment of population structure of South American sea lion (Otaria flavescens) in the Southwestern Atlantic Ocean. Mar. Biol. 158, 1857–1867 (2011).Article 

Google Scholar 
Túnez, J. I., Cappozzo, H. L., Nardelli, M. & Cassini, M. H. Population genetic structure and historical population dynamics of the South American sea lion, Otaria flavescens, in north-central Patagonia. Genetica 138, 831–841 (2010).Article 

Google Scholar 
Oliveira, L., Ott, P. H., Grazziotin, F. G., White, B. & Bonatto, S. In Paper (SC/S11/RW26) presented to the Southern Right Whale Assessment Workshop (Commission International Whaling).Loizaga de Castro, R., Dans, S. L. & Crespo, E. A. Spatial genetic structure of dusky dolphin, Lagenorhynchus obscurus, along the argentine coast: Preserve what scale?. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 173–183 (2016).Article 

Google Scholar 
Pimper, L. E., Goodall, R. N. P. & Remis, M. I. First mitochondrial DNA analysis of the spectacled porpoise (Phocoena dioptrica) from Tierra del Fuego, Argentina. Mamm. Biol. 77, 459–462 (2012).Article 

Google Scholar 
Pichler, F. B. et al. Origin and radiation of Southern Hemisphere coastal dolphins (genus Cephalorhynchus). Mol. Ecol. 10, 2215–2223 (2001).Article 
CAS 

Google Scholar 
Dawson, S. M. In Encyclopedia of Marine Mammals, 166–172 (Elsevier, 2018).Robineau, D., Goodall, R. N. P., Pichler, F. & Baker, C. S. Description of a new subspecies of Commerson’s dolphin, Cephalorhynchus commersonii (Lacépède, 1804), inhabiting the coastal waters of the Kerguelen Islands. Mammalia 71, 172–180 (2007).Article 

Google Scholar 
Crespo, E. A. et al. Cephalorhynchus commersonii, Commerson’s Dolphin. IUCN; The IUCN Red List of Threatened Species; 10-2017; 1-14 (2017).Goodall, R. Commerson’s dolphin Cephalorhynchus commersonii (Lacépède 1804). Handb. Mar. Mamm. 5, 241–267 (1994).
Google Scholar 
Coscarella, M. A. Ecologıa, comportamiento y evaluación del impacto de embarcaciones sobre manadas de tonina overa Cephalorhynchus commersonii en Bahıa Engano, Chubut (Universidad de Buenos Aires, Buenos Aires, 2005).Dellabianca, N. A. et al. Spatial models of abundance and habitat preferences of commerson’s and peale’s dolphin in southern patagonian waters. PLoS ONE 11, e0163441 (2016).Article 

Google Scholar 
Goodall, R. et al. Studies of Commerson’s dolphins, Cephalorhynchus commersonii, off Tierra del Fuego, 1976–1984. Report of the International Whaling Commission (Special Issue 9), 143–160 (1988).White, R. The Distribution of Seabirds and Marine Mammals in Falkland Islands Waters (Joint Nature Conservation Committee, 2002).Loizaga de Castro, R., Dans, S. L., Coscarella, M. A. & Crespo, E. A. Living in an estuary: Commerson’s dolphin (Cephalorhynchus commersonii (Lacépède, 1804)), habitat use and behavioural pattern at the Santa Cruz River, Patagonia, Argentina. Latin Am. J. Aquat. Res. 41, 985–991 (2013).Article 

Google Scholar 
Pedraza, S. Ecología poblacional de la tonina overa, Cephalorhynchus commersonii, (Lacépède, 1804) en el litoral patagónico. Unpublished PhD thesis, Universidad de Buenos Aires, Buenos Aires, Argentina (2008).Garaffo, G. V. et al. Modeling habitat use for dusky dolphin and Commerson’s dolphin in Patagonia. Mar. Ecol. Prog. Ser. 421, 217–227 (2011).Article 
ADS 

Google Scholar 
Cipriano, F., Hevia, M. & Iñíguez, M. Genetic divergence over small geographic scales and conservation implications for Commerson’s dolphins (Cephalorhynchus commersonii) in southern Argentina. Mar. Mamm. Sci. 27, 701–718 (2011).Article 
CAS 

Google Scholar 
Pimper, L. E., Baker, C. S., Goodall, R. N. P., Olavarría, C. & Remis, M. I. Mitochondrial DNA variation and population structure of Commerson’s dolphins (Cephalorhynchus commersonii) in their southernmost distribution. Conserv. Genet. 11, 2157–2168 (2010).Article 

Google Scholar 
O’Brien, S. J. A role for molecular genetics in biological conservation. Proc. Natl. Acad. Sci. 91, 5748–5755 (1994).Article 
ADS 
CAS 

Google Scholar 
Loizaga de Castro, R., Hoelzel, A. & Crespo, E. Behavioural responses of Argentine coastal dusky dolphins (Lagenorhynchus obscurus) to a biopsy pole system. Anim. Welf. 22, 13–23 (2013).Article 
CAS 

Google Scholar 
Elphinstone, M. S., Hinten, G. N., Anderson, M. J. & Nock, C. J. An inexpensive and high-throughput procedure to extract and purify total genomic DNA for population studies. Mol. Ecol. Notes 3, 317–320 (2003).Article 
CAS 

Google Scholar 
Bérubé, M. & Palsbøll, P. Identification of sex in cetaceans by multiplexing with three ZFX and ZFY specific primers. Mol. Ecol. 5, 283–287 (1996).Article 

Google Scholar 
Hoelzel, A., Hancock, J. & Dover, G. Evolution of the cetacean mitochondrial D-loop region. Mol. Biol. Evol. 8, 475–493 (1991).CAS 

Google Scholar 
Kumar, S., Stecher, G. & Tamura, K. MEGA7: Molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evol. 33, 1870–1874 (2016).Article 
CAS 

Google Scholar 
Ruzzante, D. E. et al. Validation of close-kin mark–recapture (CKMR) methods for estimating population abundance. Methods Ecol. Evol. 10, 1445–1453 (2019).Article 

Google Scholar 
Faircloth, B. C., Branstetter, M. G., White, N. D. & Brady, S. G. Target enrichment of ultraconserved elements from arthropods provides a genomic perspective on relationships among H ymenoptera. Mol. Ecol. Resour. 15, 489–501 (2015).Article 
CAS 

Google Scholar 
Faircloth, B. C. MSATCOMMANDER: Detection of microsatellite repeat arrays and automated, locus-specific primer design. Mol. Ecol. Resour. 8, 92–94 (2008).Article 
CAS 

Google Scholar 
Zhan, L. et al. MEGASAT: Automated inference of microsatellite genotypes from sequence data. Mol. Ecol. Resour. 17, 247–256 (2017).Article 
CAS 

Google Scholar 
Nei, M. Molecular Evolutionary Genetics (Columbia University Press, 1987).Librado, P. & Rozas, J. DnaSP v5: A software for comprehensive analysis of DNA polymorphism data. Bioinformatics 25, 1451–1452 (2009).Article 
CAS 

Google Scholar 
Schneider, S., Roessli, D. & Excoffier, L. Arlequin: A software for population genetics data analysis, version 2.000. Genetics Biometry Laboratory, Department of Anthropology, University of Geneva, Switzerland (2000).Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).Article 
CAS 

Google Scholar 
Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: More models, new heuristics and parallel computing. Nat. Methods 9, 772 (2012).Article 
CAS 

Google Scholar 
Excoffier, L. & Lischer, H. E. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour 10, 564–567 (2010).Article 

Google Scholar 
Bandelt, H.-J., Forster, P. & Röhl, A. Median-joining networks for inferring intraspecific phylogenies. Mol. Biol. Evol. 16, 37–48 (1999).Article 
CAS 

Google Scholar 
Fu, Y.-X. Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147, 915–925 (1997).Article 
CAS 

Google Scholar 
Rogers, A. R. & Harpending, H. Population growth makes waves in the distribution of pairwise genetic differences. Mol. Biol. Evol. 9, 552–569 (1992).CAS 

Google Scholar 
Peakall, R. & Smouse, P. E. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

Google Scholar 
Mantel, N. The detection of disease clustering and a generalized regression approach. Can. Res. 27, 209–220 (1967).CAS 

Google Scholar 
Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).Article 

Google Scholar 
Harlin, A. D., Markowitz, T., Baker, C. S., Würsig, B. & Honeycutt, R. L. Genetic structure, diversity, and historical demography of New Zealand’s dusky dolphin (Lagenorhynchus obscurus). J. Mammal. 84, 702–717 (2003).Article 

Google Scholar 
Rambaut, A., Suchard, M., Xie, D. & Drummond, A. Tracer v1. 6. http://beast.bio.ed.ac.uk/Tracer (2014).Van Oosterhout, C., Hutchinson, W. F., Wills, D. P. & Shipley, P. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4, 535–538 (2004).Article 

Google Scholar 
Rice, W. R. Analyzing tables of statistical tests. Evolution 43, 223–225 (1989).
Google Scholar 
Goudet, J. FSTAT, a program to estimate and test gene diversities and fixation indices, version 2.9. 3. http://www2.unil.ch/popgen/softwares/fstat.htm (2001).Waples, R. S. & Do, C. LDNE: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).Article 

Google Scholar 
Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).Article 
CAS 

Google Scholar 
Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).Article 
CAS 

Google Scholar 
Earl, D. A. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

Google Scholar 
Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275 (1989).
Google Scholar 
Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).Article 

Google Scholar 
Milinkovitch, M. C., Leduc, R., Tiedemann, R. & Dizon, A. In Marine Mammals: Biology and Conservation (ed Evans, P. G. H. & Raga, J. A.) 325–359 (Springer, 2002).Pichler, F. Population structure and genetic variation in Hector’s dolphin (Cephalorhynchus hectori), ResearchSpace@ Auckland (2001).Pichler, F. & Baker, C. Loss of genetic diversity in the endemic Hector’s dolphin due to fisheries-related mortality. Proc. R. Soc. Lond. Ser. B Biol. Sci. 267, 97–102 (2000).Article 
CAS 

Google Scholar 
Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162 (1980).Article 

Google Scholar 
Chilvers, B. L. & Wilkinson, I. S. Philopatry and site fidelity of New Zealand sea lions (Phocarctos hookeri). Wildl. Res. 35, 463–470 (2008).Article 

Google Scholar 
Engelhaupt, D. et al. Female philopatry in coastal basins and male dispersion across the North Atlantic in a highly mobile marine species, the sperm whale (Physeter macrocephalus). Mol. Ecol. 18, 4193–4205 (2009).Article 
CAS 

Google Scholar 
Möller, L. M. & Beheregaray, L. B. Genetic evidence for sex-biased dispersal in resident bottlenose dolphins (Tursiops aduncus). Mol. Ecol. 13, 1607–1612 (2004).Article 

Google Scholar 
Jansen van Vuuren, B., Best, P., Roux, J. P. & Robinson, T. Phylogeographic population structure in the Heaviside’s dolphin (Cephalorhynchus heavisidii): Conservation implications. Anim. Conserv. 5, 303–307 (2002).Article 

Google Scholar 
Pérez-Alvarez, M. J. et al. Microsatellite markers reveal strong genetic structure in the endemic Chilean dolphin. PLoS ONE 10, e0123956 (2015).Article 

Google Scholar 
Hamner, R. M., Pichler, F. B., Heimeier, D., Constantine, R. & Baker, C. S. Genetic differentiation and limited gene flow among fragmented populations of New Zealand endemic Hector’s and Maui’s dolphins. Conserv. Genet. 13, 987–1002 (2012).Article 

Google Scholar 
Pichler, F., Dawson, S., Slooten, E. & Baker, C. Geographic isolation of Hector’s dolphin populations described by mitochondrial DNA sequences. Conserv. Biol. 12, 676–682 (1998).Article 

Google Scholar 
Kraft, S. et al. From settlers to subspecies: Genetic differentiation in commerson’s Dolphins between South America and the Kerguelen Islands. Front. Mar. Sci. 8, 782512 (2021).Article 

Google Scholar 
Grant, W. & Bowen, B. W. Shallow population histories in deep evolutionary lineages of marine fishes: Insights from sardines and anchovies and lessons for conservation. J. Hered. 89, 415–426 (1998).Article 

Google Scholar 
Ponce, J. F., Rabassa, J., Coronato, A. & Borromei, A. M. Palaeogeographical evolution of the Atlantic coast of Pampa and Patagonia from the last glacial maximum to the Middle Holocene. Biol. J. Lin. Soc. 103, 363–379 (2011).Article 

Google Scholar 
Wright, S. Isolation by distance. Genetics 28, 114 (1943).Article 
CAS 

Google Scholar 
Meirmans, P. G. Nonconvergence in B ayesian estimation of migration rates. Mol. Ecol. Resour. 14, 726–733 (2014).Article 

Google Scholar  More

Takoutsing, B. et al. Assessment of soil health indicators for sustainable production of maize in smallholder farming systems in the highlands of Cameroon. Geoderma 276, 64–73 (2016).Article 
ADS 
CAS 

Google Scholar 
Wenzel, W. W. et al. Soil and land use factors control organic caron status and accumulation in agricultural soils of Lower Austria. Geoderma 409, 115595. https://doi.org/10.1016/j.geoderma.2021.115595 (2022).Article 
ADS 
CAS 

Google Scholar 
Rinot, O., Levy, G. J., Steinberger, Y., Svoray, T. & Eshel, G. Soil health assessment: A critical review of current methodologies and a proposed new approach. Sci. Total. Environ. 648, 1484–1491 (2019).Article 
ADS 
CAS 

Google Scholar 
Veum, K. S., Sudduth, K. A., Kremer, R. J. & Kitchen, R. (2017) Sensor data fusion for soil health assessment. Geoderma 305, 53–61 (2017).Article 
ADS 

Google Scholar 
Nunes, M. R., Van Es, H. M., Schindelbeck, R., Ristow, A. J. & Ryan, M. No-till and cropping system diversification improve soil health and crop yield. Geoderma 328, 30–43 (2018).Article 
ADS 
CAS 

Google Scholar 
Chhipaa, V., Stein, A., Shankar, H., George, K. J. & Alidoost, F. Assessing and transferring soil health information in a hilly terrain. Geoderma 343, 130–138 (2019).Article 
ADS 

Google Scholar 
Oliver, D. P., Bramley, R. G. V., Riches, D., Porter, I. & Edwards, J. A review of soil physical and chemical properties as indicators of soil quality in Australian viticulture. Aust. J. Grape Wine Res. 19, 129–139 (2013).Article 
CAS 

Google Scholar 
Riches, D. et al. Review: soil biological properties as indicators of soil quality in Australian viticulture. Aust. J. Grape Wine Res. 19, 311–323 (2013).CAS 

Google Scholar 
Ritz, K., Black, H. I. J., Campbell, C. D., Harris, J. A. & Wood, C. Selecting biological indicators for monitoring soils: a framework for balancing scientific opinion to assist policy development. Ecol. Ind. 9, 1212–1221 (2009).Article 
CAS 

Google Scholar 
Zhuo, Z., Kirchner, I., Pfahl, S. & Cubasch, U. Climate impact of volcanic eruptions: the sensitivity to eruption season and latitude in MPI-ESM ensemble experiments. Atmos. Chem. Phys. 21, 13425–13442 (2021).Article 
ADS 
CAS 

Google Scholar 
Griffiths, B. S., Bonkowski, M., Roy, J. & Ritz, K. Functional stability, substrate utilisation and biological indicators of soils following environmental impacts. Appl. Soil Ecol. 16(1), 49–61 (2001).Article 

Google Scholar 
Avidano, L., Gamalero, E., Cossa, G. P. & Carraro, E. Characterization of soil health in an Italian polluted site by using microorganisms as bioindicators. Appl. Soil Ecol. 30(1), 21–33 (2005).Article 

Google Scholar 
Pattison, A. B. et al. Development of key soil health indicators for the Australian banana industry. Appl. Soil Ecol. 40(1), 155–164 (2008).Article 

Google Scholar 
Damsma, K. M., Rose, M. T. & Cavagnaro, T. R. Landscape scale survey of indicators of soil health in grazing systems. Soil Res. 53(2), 154–167 (2015).Article 

Google Scholar 
Fine, A. K., van Es, H. M. & Schindelbeck, R. R. Statistics, scoring functions, and regional analysis of a comprehensive soil health database. Soil Sci. Soc. Am. J. 81(3), 589–601 (2016).Article 

Google Scholar 
Roper, W. R., Osmond, D. L., Heitman, J. L., Wagger, M. G. & Reberg-Horton, S. C. Soil Health indicators do not differentiate among agronomic management systems in North Carolina soils. Soil Sci. Soc. Am. J. 81(4), 828–843 (2016).Article 

Google Scholar 
Li, Z. et al. Rapid diagnosis of agricultural soil health: A novel soil health index based on natural soil productivity and human management. J. Environ. Manage. 277, 111402. https://doi.org/10.1016/j.jenvman.2020.111402 (2021).Article 

Google Scholar 
Oren, A. & Steinberger, Y. Catabolic profiles of soil fungal communities along a geographic climatic gradient in Israel. Soil Biol. Biochem. 40, 2578–2587 (2008).Article 
CAS 

Google Scholar 
Yu, J., Glazer, N. & Steinberger, Y. Carbon utilization, microbial biomass, and respiration in biological soil crusts in the Negev Desert. Biol. Fert. Soils 50, 285–293 (2014).Article 
CAS 

Google Scholar 
Van der Putten, W. H. et al. Plant–soil feedbacks: the past, the present and future challenges. J. Ecol. 101, 265–276 (2013).Article 

Google Scholar 
Sherman, C. & Steinberger, Y. Microbial functional diversity associated with plant litter decomposition along a climatic gradient. Microb. Ecol. 64, 399–415 (2012).Article 
CAS 

Google Scholar 
Dwivedi, V. & Soni, P. A review on the role of soil microbial biomass in eco-restoration of degraded ecosystem with special reference to mining areas. J. Appl. Nat. Sci. 3(1), 151–158 (2011).Article 

Google Scholar 
Barreiro, A., Martín, A., Carballas, T. & Díaz-Raviña, M. Long-term response of soil microbial communities to fire and fire-fighting chemicals. Biol. Fertil. Soils 52, 963–975 (2016).Article 
CAS 

Google Scholar 
Soil Science Division Staff. Soil survey manual. In USDA Handbook 18 (ed. Ditzler, C., Scheffe, K. & Monger, H.C.). (Washington, G. P. O., 2017).Campbell, C. D., Chapman, S. J., Cameron, C. M., Davidson, M. S. & Potts, J. M. A rapid microtiter plate method to measure carbon dioxide evolved from carbon substrate amendments so as to determine the physiological profiles of soil microbial communities by using whole soil. Appl. Environ. Microbiol. 69, 3593–3599 (2003).Article 
ADS 
CAS 

Google Scholar 
Anderson, J. P. E. & Domsch, K. H. Physiological method for quantitative measurement of microbial biomass in soils. Soil Biol. Biochem. 10, 215–221 (1978).Article 
CAS 

Google Scholar 
Creamer, R. E., Stone, D., Berry, P. & Kuiper, I. Measuring respiration profiles of soil microbial communities across Europe using MicroResp™ method. Appl. Soil Ecol. 97, 36–43 (2016).Article 

Google Scholar 
Oren, A. & Steinberger, Y. Coping with artifacts induced by CaCO3–CO2–H2O equilibria in substrate utilization profiling of calcareous soils. Soil Biol. Biochem. 40, 2569–2577 (2008).Article 
CAS 

Google Scholar 
Zak, J. C., Willig, M. R., Howard, D. L. & Wildman, G. Functional diversity of microbial communities: A quantitative approach. Soil Biol. Biochem. 26(9), 1101–1108 (1994).Article 

Google Scholar 
Hotelling, H. The most predictable criterion. J. Educ. Psychol. 26, 139–142 (1935).Article 

Google Scholar 
Morrison, D. F. Multivariate Statistical Methods 2nd edn. (McGraw-Hill, 1976).MATH 

Google Scholar 
Rencher, A. C. Methods of Multivariate Analysis (Wiley, Uk, 1995).MATH 

Google Scholar 
IBM Corp. Released 2020. IBM SPSS Statistics for Windows, Version 27.0. (Armonk, NY: IBM Corp., 2020)R Core Team. A language and environment for statistical computing (R Foundation for Statistical Computing, 2021).
Google Scholar 
Bartoń K. MuMIn: Multi-Model Inference. R package version 1.46.0, https://CRAN.R-project.org/package=MuMIn, 2022.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2016).Book 
MATH 

Google Scholar 
Johnson, J. B. & Omland, K. S. Model selection in ecology and evolution. Trends Ecol. Evol. 19, 101–108 (2004).Article 

Google Scholar 
Kiryushin, V. I. The management of soil fertility and productivity of agrocenoses in adaptive-landscape farming systems. Eurasian Soil Sci. 52, 1137–1145 (2019).Article 
ADS 
CAS 

Google Scholar 
Hermans, S. M. et al. Using soil bacterial communities to predict physic-chemical variables and soil quality. Microbiome 8, 79 (2020).Article 
CAS 

Google Scholar 
Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. U. S. A. 103, 626–631 (2006).Article 
ADS 
CAS 

Google Scholar 
Frey, S. D., Drijber, R., Smith, H. & Melillo, J. M. Microbial biomass, functional capacity, and community structure after 12 years of soil warming. Soil Biol. Biochem. 40, 2904–2907 (2008).Article 
CAS 

Google Scholar 
Powlson, D. S., Brookes, P. C. & Christensen, B. T. Measurement of soil microbial biomass provides an early indication of changes in total soil organic matter due to straw incorporation. Soil Biol. Biochem. 19, 159–164 (1987).Article 
CAS 

Google Scholar 
Brookes, P. C. The use of microbial parameters in monitoring soil pollution by heavy metals. Biol. Fertil. Soils. 19, 269–279 (1995).Article 
CAS 

Google Scholar 
Hermans, S. M. et al. Bacteria as emerging indicators of soil condition. Appl. Environ. Microbiol. 83, e02826-e2916. https://doi.org/10.1128/AEM.02826-16 (2016).Article 

Google Scholar 
Liddicoat, C. et al. Can bacterial indicators of a grassy woodland restoration inform ecosystem assessment and microbiota-mediated human health?. Environ. Int. 129, 105–117 (2019).Article 

Google Scholar 
Jeanne, T., Parent, S. -É. & Hogue, R. Using a soil bacterial species balance index to estimate potato crop productivity. PLoS ONE 14, e0214089. https://doi.org/10.1371/journal.pone.0214089 (2019).Article 
CAS 

Google Scholar 
Taylor, B. R. & Parkinson, D. Respiration and mass loss rates of aspen and pine leaf litter decomposing in laboratory microcosms. Can. J. Bot. 66, 1948–1959 (1988).Article 

Google Scholar 
Wardle, D. A. & Parkinson, D. Response of the soil microbial biomass to glucose, and selective inhibitors, across a soil moisture gradient. Soil Biol. Biochem. 22, 825–834 (1990).Article 
CAS 

Google Scholar 
Baldrian, P., Merhautová, V., Petránková, M., Cajthaml, T. & Snajdr, J. Distribution of microbial biomass and activity of extracellular enzymes in a hardwood forest soil reflect soil moisture content. Appl. Soil Ecol. 46, 177–182 (2010).Article 

Google Scholar 
Holland, T. C. et al. The response of soil biota to water availability in vineyards. Pedobiol. Int. J. Soil Biol. 56, 9–14 (2013).
Google Scholar 
Yu, J. & Steinberger, Y. Vertical distribution of microbial-community functionality under the canopies of Zygophyllum dumosum and Hammada scoparia in the Negev Desert. Microb. Ecol. 62, 218–227 (2011).Article 

Google Scholar 
Wardle, D. A. & Parkinson, D. Interaction between microclimatic variables and the soil microbial biomass. Biol. Fertil. Soils. 9, 273–280 (1990).Article 

Google Scholar  More

Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proc. Natl Acad. Sci. USA 109, 16083–16088 (2012).Article 
ADS 
CAS 

Google Scholar 
McDonald, R. I., Marcotullio, P. J. & Güneralp, B. Urbanization and global trends in biodiversity and ecosystem services. in Urbanization, Biodiversity and Ecosystem Services: Challenges And Opportunities, 31–52 (Springer, 2013).McDonald, R. I., Kareiva, P. & Forman, R. T. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).Article 

Google Scholar 
Müller, N., Ignatieva, M., Nilon, C. H., Werner, P. & Zipperer, W. C. Patterns and trends in urban biodiversity and landscape design. In Urbanization, Biodiversity and Ecosystem Services: Challenges And Opportunities, 123–174 (Springer, 2013).Lahr, E. C., Dunn, R. R. & Frank, S. D. Getting ahead of the curve: Cities as surrogates for global change. Proc. R. Soc. B. 285, 20180643 (2018).Article 

Google Scholar 
Cadotte, M. W., Yasui, S. L. E., Livingstone, S. & MacIvor, J. S. Are urban systems beneficial, detrimental, or indifferent for biological invasion?. Biol. Invasions. 19, 3489–3503 (2017).Article 

Google Scholar 
Thompson, K. A., Rieseberg, L. H. & Schluter, D. Speciation and the city. Trends Ecol. Evol. 33, 815–826 (2018).Article 

Google Scholar 
Borden, J. B. & Flory, S. L. Urban evolution of invasive species. Front. Ecol. Environ. 19, 184–191 (2021).Article 

Google Scholar 
Melliger, R. L., Braschler, B., Rusterholz, H. P. & Baur, B. Diverse effects of degree of urbanisation and forest size on species richness and functional diversity of plants, and ground surface-active ants and spiders. PLoS ONE 13, e0199245 (2018).Article 

Google Scholar 
McKinney, M. L. Urbanization, biodiversity, and conservation: The impacts of urbanization on native species are poorly studied, but educating a highly urbanized human population about these impacts can greatly improve species conservation in all ecosystems. Bioscience 52, 883–890 (2002).Article 

Google Scholar 
Roshnath, R. & Sinu, P. A. Nesting tree characteristics of heronry birds of urban ecosystems in peninsular India: Implications for habitat management. Curr. Zool. 63, 599–605 (2017).Article 

Google Scholar 
Roshnath, R., Athira, K. & Sinu, P. A. Does predation pressure drive heronry birds to nest in the urban landscape?. J. Asia Pac. Biodivers. 12, 311–315 (2019).Article 

Google Scholar 
Fenoglio, M. S., Rossetti, M. R. & Videla, M. Negative effects of urbanization on terrestrial arthropod communities: A meta-analysis. Glob. Ecol. Biogeogr. 29, 1412–1429 (2020).Article 

Google Scholar 
Saari, S. et al. Urbanization is not associated with increased abundance or decreased richness of terrestrial animals-dissecting the literature through meta-analysis. Urban Ecosyst. 19, 1251–1264 (2016).Article 

Google Scholar 
Lessard, J. P. & Buddle, C. M. The effects of urbanization on ant assemblages (Hymenoptera: Formicidae) associated with the Molson Nature Reserve. Quebec. Can. Entomol. 137, 215–225 (2005).Article 

Google Scholar 
Uno, S., Cotton, J. & Philpott, S. M. Diversity, abundance, and species composition of ants in urban green spaces. Urban Ecosyst. 13, 425–441 (2010).Article 

Google Scholar 
Fortel, L. et al. Decreasing abundance, increasing diversity and changing structure of the wild bee community (Hymenoptera: Anthophila) along an urbanization gradient. PLoS ONE 9, e104679 (2014).Article 
ADS 

Google Scholar 
Baldock, K. C. et al. Where is the UK’s pollinator biodiversity? The importance of urban areas for flower-visiting insects. Proc. R. Soc. B. 282, 20142849 (2015).Article 

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).Article 

Google Scholar 
Rocha, E. A. & Fellowes, M. D. Urbanisation alters ecological interactions: Ant mutualists increase and specialist insect predators decrease on an urban gradient. Sci. Rep. 10, 1–8 (2020).Article 
ADS 

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).Article 

Google Scholar 
Carvalho, R. L. et al. Understanding what bioindicators are actually indicating: Linking disturbance responses to ecological traits of dung beetles and ants. Ecol. Indic. 108, 105764 (2020).Article 

Google Scholar 
Asha, G., Manoj, K., Megha, P. P. & Sinu, P. A. Spatiotemporal effects on dung beetle activities in island forests-home garden matrix in a tropical village landscape. Sci. Rep. 11, 1–13 (2021).Article 

Google Scholar 
Correa, C. M. A., da Silva, P. G., Ferreira, K. R. & Puker, A. Residential sites increase species loss and cause high temporal changes in functional diversity of dung beetles in an urbanized Brazilian Cerrado landscape. J. Insect Conserv. 25, 417–428 (2021).Article 

Google Scholar 
Correa, C. M. A., Ferreira, K. R., Puker, A., Audino, L. D. & Korasaki, V. Greenspace sites conserve taxonomic and functional diversity of dung beetles in an urbanized landscape in the Brazilian Cerrado. Urban Ecosyst. 24, 1023–1034 (2021).Article 

Google Scholar 
Beiroz, W. et al. Spatial and temporal shifts in functional and taxonomic diversity of dung beetle in a human-modified tropical forest landscape. Ecol. Indic. 95, 418–526 (2018).Article 

Google Scholar 
Fuzessy, L. F. et al. Identifying the anthropogenic drivers of declines in tropical dung beetle communities and functions. Biol. Conserv. 256, 109063 (2021).Article 

Google Scholar 
Barragan, F., Moreno, C. E., Escobar, F., Halffter, G. & Navarrete, D. Negative impacts of human land use on dung beetle functional diversity. PLoS ONE 6, e17976 (2011).Article 
ADS 
CAS 

Google Scholar 
Salomão, R. P. et al. Urbanization effects on dung beetle assemblages in a tropical city. Ecol. Indic. 103, 665–675 (2019).Article 

Google Scholar 
Filgueiras, B. K. C., Liberal, C. N., Aguiar, C. D. M., Hernández, M. I. M. & Iannuzzi, L. Attractivity of omnivore, carnivore and herbivore mammalian dung to Scarabaeinae (Coleoptera: Scarabaeidae) in a tropical Atlantic rainforest remnant. Rev. Bras. Entomol. 53, 422–427 (2009).Article 

Google Scholar 
Ramírez-Restrepo, L. & Halffter, G. Copro-necrophagous beetles (Coleoptera: Scarabaeinae) in urban areas: A global review. Urban Ecosyst. 19, 1179–1195 (2016).Article 

Google Scholar 
Krell, F. T. et al. Human influence on the dung fauna in Afrotropical grasslands (Insecta: Coleoptera). In African Biodiversity: Molecules Organisms Ecosystems (eds Huber, B. A. et al.) 133–139 (Springer, 2005).Chapter 

Google Scholar 
Jiménez-Ferbans, L., Mendieta-Otálora, W., García, H. & Amat-García, G. Notes on dung beetles (Coleoptera: Scarabaeinae) in dry environments of the Santa Marta region, Colombia. Acta Biol. Colomb. 13, 203–208 (2008).
Google Scholar 
Costa, F. C. et al. What is the importance of open habitat in a predominantly closed forest area to the dung beetle (Coleoptera, Scarabaeinae) assemblage?. Rev. Bras. Entomol. 57, 329–334 (2013).Article 

Google Scholar 
Korasaki, V., Lopes, J., Gardner, B. G. & Louzada, J. Using dung beetles to evaluate the effects of urbanization on Atlantic Forest biodiversity. Insect Sci. 20, 393–406 (2013).Article 

Google Scholar 
Audino, L., Louzada, J. & Comita, L. Dung beetles as indicators of tropical forest restoration success: Is it possible to recover species and functional diversity?. Biol. Conserv. 169, 248–257 (2014).Article 

Google Scholar 
Gómez-Cifuentez, A., Munevar, A., Gimenez, V. C., Gatti, M. G. & Zurita, G. A. Influence of land use on the taxonomic and functional diversity of dung beetles (Coleoptera: Scarabaeinae) in the southern Atlantic Forest of Argentina. J. Insect. Conserv. 21, 147–156 (2017).Article 

Google Scholar 
Gómez-Cifuentes, A., Gómez, V. C. G., Moreno, C. E. & Zurita, G. A. Tree retention in cattle ranching systems partially preserves dung beetle diversity and functional groups in the semideciduous Atlantic forest: The role of microclimate and soil conditions. Basic Appl. Ecol. 34, 64–74 (2019).Article 

Google Scholar 
Magnano, L. F. S. et al. Functional attributes change but functional richness is unchanged after fragmentation of Brazilian Atlantic forests. J. Ecol. 102, 475–485 (2014).Article 

Google Scholar 
GiménezGómez, V. C., Verdú, J. R., Casanoves, F. & Zurita, G. A. Functional responses to anthropogenic disturbance and the importance of selected traits: a study case using dung beetles. Ecol. Entomol. 1, 1–12 (2022).
Google Scholar 
Lobo, J. M. Decline of roller dung beetle (Scarabaeinae) populations in the Iberian Peninsula during the 20th century. Biol. Conserv. 97, 43–50 (2001).Article 

Google Scholar 
Ballullaya, U. P. et al. Stakeholder motivation for the conservation of sacred groves in south India: An analysis of environmentalperceptions of rural and urban neighbourhood communities. Land Use Policy 89, 104213 (2019).Article 

Google Scholar 
Lowman, M. D. & Sinu, P. A. Can the spiritual values of forests inspire effective conservation?. Bioscience 67, 688–690 (2017).Article 

Google Scholar 
Bhagwat, S. A., Kushalappa, C. G., Williams, P. H. & Brown, N. D. The role of informal protected areas in maintaining biodiversity in the Western Ghats of India. Ecol. Soc 10, 108 (2005).Article 

Google Scholar 
Rajesh, T. P., Prashanth Ballullaya, U., Unni, A. P., Parvathy, S. & Sinu, P. A. Interactive effects of urbanization and year on invasive and native ant diversity of sacred groves of South India. Urban Ecosyst. 23, 1335–1348 (2020).Article 

Google Scholar 
Asha, G., Navya, K. K., Rajesh, T. P. & Sinu, P. A. Roller dung beetles of dung piles suggest habitats are alike, but that of guarding pitfall traps suggest habitats are different. J. Trop. Ecol. 37, 209–213 (2021).Article 

Google Scholar 
Arrow, G. J. The Fauna Of British India Including Ceylon And Burma, Coleoptera: Lamellicornia (Coprinae) (Taylor and Francis, 1931).
Google Scholar 
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: Interpolation and extrapolation for species diversity. R package version 2.0.20. http://chao.stat.nthu.edu.tw/wordpress/software-download/ (2020).Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

Google Scholar 
Fox, J. et al. Package ‘car’, Vol. 16, (R Foundation for Statistical Computing, 2012).Barton, K. MuMIn: Multi-Model Inference. R package version 1.43.17. https://CRAN.R-project.org/package=MuMIn (2020).Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7. https://CRAN.R-project.org/package=vegan (2020).Hartig, F. & Hartig, M. F. Package ‘DHARMa’. R package (2017).Warnes, G. R. et al. gplots: Various R Programming Tools for Plotting Data. R package version 3.1.1. https://CRAN.R-project.org/package=gplots (2020).R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/ (2021).Venugopal, K. S., Thomas, S. K. & Flemming, A. T. Diversity and community structure of dung beetles (Coleoptera: Scarabaeinae) associated with semi-urban fragmented agricultural land in the Malabar coast in southern India. J. Threat. Taxa. 4, 2685–2692 (2012).Article 

Google Scholar 
Sabu, T. K. & Nithya, S. Comparison of the arboreal dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) of the wet and dry forests of the western Ghats. India. Coleopt. Bull. 70, 144–148 (2016).Article 

Google Scholar 
Sabu, T. K., Vinod, K. V. & Vineesh, P. J. Guild structure, diversity and succession of dung beetles associated with Indian elephant dung in South Western Ghats forests. J. Insect Sci. 6, 6–17 (2006).Article 

Google Scholar 
Rodrigues, M. M., Uchôa, M. A. & Ide, S. Dung beetles (Coleoptera: Scarabaeoidea) in three landscapes in Mato Grosso do Sul, Brazil. Braz. J. Biol. 73, 211–220 (2013).Article 
CAS 

Google Scholar 
Rios-Diaz, C. L. et al. Sheep herding in small grasslands promotes dung beetle diversity in a mountain forest landscape. J. Insect. Conserv. 25, 13–26 (2020).Article 

Google Scholar 
Carrión-Paladines, V. et al. Effects of land-use change on the community structure of the dung beetle (Scarabaeinae) in an altered ecosystem in Southern Ecuador. Insects. 12, 306 (2021).Article 

Google Scholar 
Gómez, V. C. G., Verdú, J. R. & Zurita, G. A. Thermal niche helps to explain the ability of dung beetles to exploit disturbed habitats. Sci. Rep. 10, 1–14 (2020).ADS 

Google Scholar 
Slade, E. M., Mann, D. J., Villanueva, J. F. & Lewis, O. T. Experimental evidence for the effects of dung beetle functional group richness and composition on ecosystem function in a tropical forest. J. Anim. Ecol. 76, 1094–1104 (2007).Article 

Google Scholar 
Vinod, K. V. & Sabu, T. K. Species composition and community structure of dung beetles attracted to dung of gaur and elephant in the moist forests of South Western Ghats. J. Insect. Sci. 7, 1–14 (2007).Article 
CAS 

Google Scholar 
Milotić, T. et al. Functionally richer communities improve ecosystem functioning: Dung removal and secondary seed dispersal by dung beetles in the Western Palaearctic. J. Biogeogr. 46, 70–82 (2019).Article 

Google Scholar 
Braga, R. F., Korasaki, V., Andresen, E. & Louzada, J. Dung beetle community and functions along a habitat-disturbance gradient in the amazon: A rapid assessment of ecological functions associated to biodiversity. PLoS ONE 8, e57786 (2013).Article 
ADS 
CAS 

Google Scholar 
Nichols, E. et al. Trait-dependent response of dung beetle populations to tropical forest conversion at local and regional scales. Ecology 94, 180–189 (2013).Article 

Google Scholar 
Gardner, T. A. et al. The cost-effectiveness of biodiversity surveys in tropical forests. Ecol. Lett. 11, 139–150 (2008).Article 

Google Scholar  More

Stokland, J. N., Siitonen, J. & Jonsson, B. G. Biodiversity in Dead Wood (Cambridge Univ. Press, 2012).Turetsky, M. R. et al. Nat. Geosci. 8, 11–14 (2014).Article 

Google Scholar 
Wenger, S. J., Subalusky, A. L. & Freeman, M. C. Food Webs 18, e00106 (2019).Article 

Google Scholar 
Tomatsuri, M. & Kon, K. Hydrobiologia 790, 225–232 (2017).Article 

Google Scholar 
Henry, L. A. & Roberts, J. M. in Marine Animal Forests (eds Rossi, S. et al.) 235–256 (Springer, 2017).Walton, M. E. M. et al. Sci. Total Environ. 820, 153191 (2022).Article 
CAS 

Google Scholar 
Wolfe, K., Kenyon, T. M. & Mumby, P. J. Coral Reefs 40, 1769–1806 (2021).Article 

Google Scholar 
Kim, H. et al. Glob. Change Biol. 28, 6180–6193 (2022).Jackson, R. B. et al. Annu. Rev. Ecol. Evol. Syst. 48, 419–445 (2017).Article 

Google Scholar 
Pan, Y. et al. Science 333, 988–993 (2011).Article 
CAS 

Google Scholar 
Hedges, J. I., Keil, R. G. & Benner, R. Org. Geochem. 27, 195–212 (1997).Article 
CAS 

Google Scholar 
Lønborg, C. et al. Front. Mar. Sci. 7, 466 (2020).Article 

Google Scholar 
Harden, J. W. et al. Glob. Change Biol. 6, 174–184 (2000).Davidson, E. A. & Janssens, I. A. Nature 440, 165–173 (2006).Article 
CAS 

Google Scholar 
Hugelius, G. et al. Proc. Natl Acad. Sci. USA 117, 20438–20446 (2020).Article 
CAS 

Google Scholar 
Hennige, S. J. et al. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00668 (2020).Article 

Google Scholar 
Wolfram, U. et al. Sci. Rep. 12, 8052 (2022).Article 
CAS 

Google Scholar 
Roberts, J. M., Wheeler, A. J. & Freiwald, A. Science 312, 543–547 (2006).Article 
CAS 

Google Scholar 
Mortensen, P. B. & Fosså, J. H. Species diversity and spatial distribution of invertebrates on deep-water Lophelia reefs in Norway. In Proc. 10th Int. Coral Reef Symp. 1849–1868 (ICRS, 2006).Maier, S. R. et al. Deep Sea Res. I 175, 103574 (2021).. More

Odum, E. P. The strategy of ecosystem development. Science 164, 262–270 (1969).Article 
ADS 
CAS 

Google Scholar 
Gorham, E., Vitousek, P. M. & Reiners, W. A. The regulation of element budgets over the course of terrestrial ecosystem succession. Annu. Rev. Ecol. Syst. 10, 53–84 (1979).Article 
CAS 

Google Scholar 
Corman, J. R. et al. Foundations and frontiers of ecosystem science: Legacy of a classic paper (Odum 1969). Ecosystems 22, 1160–1172. https://doi.org/10.1007/s10021-018-0316-3 (2019).Article 

Google Scholar 
Santín, C. et al. Towards a global assessment of pyrogenic carbon from vegetation fires. Glob. Change Biol. 22, 76–91. https://doi.org/10.1111/gcb.12985 (2016).Article 
ADS 

Google Scholar 
Kominoski, J. S., Gaiser, E. E. & Baer, S. G. Advancing theories of ecosystem development through long-term ecological research. Bioscience 68, 554–562. https://doi.org/10.1093/biosci/biy070 (2018).Article 

Google Scholar 
Balch, J. K., Bradley, B. A., D’Antonio, C. M. & Gómez-Dans, J. Introduced annual grass increases regional fire activity across the arid western USA (1980–2009). Glob. Change Biol. 19, 173–183. https://doi.org/10.1111/gcb.12046 (2013).Article 
ADS 

Google Scholar 
Abatzoglou, J. T. & Kolden, C. A. Climate change in Western US deserts: Potential for increased wildfire and invasive annual grasses. Rangeland Ecol. Manag. 64(5), 471–478 (2011).Article 

Google Scholar 
Shi, H. et al. Historical cover trends in a sagebrush steppe ecosystem from 1985 to 2013: Links with climate, disturbance, and management. Ecosystems 21, 913–929. https://doi.org/10.1007/s10021-017-0191-3 (2018).Article 

Google Scholar 
Seyfried, M. S. & Wilcox, B. P. Scale and the nature of spatial variability: Field examples having implications for hydrologic modeling. Water Resour. Res. 31, 173–184. https://doi.org/10.1029/94WR02025 (1995).Article 
ADS 

Google Scholar 
Gasch, C. K., Huzurbazar, S. V. & Stahl, P. D. Description of vegetation and soil properties in sagebrush steppe following pipeline burial, reclamation, and recovery time. Geoderma 265, 19–26. https://doi.org/10.1016/j.geoderma.2015.11.013 (2016).Article 
ADS 

Google Scholar 
Huber, D. P. et al. Vegetation and precipitation shifts interact to alter organic and inorganic carbon storage in desert soils. Ecosphere 10, e02655. https://doi.org/10.1002/ecs2.2655 (2019).Article 

Google Scholar 
Knight, D. H., Jones, G. P., Reiners, W. A. & Romme, W. H. Mountains and Plains: The Ecology of Wyoming Landscapes (Yale University Press, 2014).
Google Scholar 
Patton, N. R., Lohse, K. A., Seyfried, M. S., Godsey, S. E. & Parsons, S. Topographic controls on soil organic carbon on soil mantled landscapes. Sci. Rep. 9, 6390. https://doi.org/10.1038/s41598-019-42556-5 (2019).Article 
ADS 
CAS 

Google Scholar 
Schwabedissen, S. G., Lohse, K. A., Reed, S. C., Aho, K. A. & Magnuson, T. S. Nitrogenase activity by biological soil crusts in cold sagebrush steppe ecosystems. Biogeochemistry 134, 57–76. https://doi.org/10.1007/s10533-017-0342-9 (2017).Article 
CAS 

Google Scholar 
You, Y. et al. Biological soil crust bacterial communities vary along climatic and shrub cover gradients within a sagebrush steppe ecosystem. Front. Microbiol. 12, 2365. https://doi.org/10.3389/fmicb.2021.569791 (2021).Article 

Google Scholar 
Burke, I. C., Reiners, W. A. & Olson, R. K. Topographic control of vegetation in a mountain big sagebrush steppe. Vegetation 84, 77–86 (1989).Article 

Google Scholar 
Poulos, M. J., Pierce, J. L., Flores, A. N. & Benner, S. G. Hillslope asymmetry maps reveal widespread, multi-scale organization. Geophys. Res. Lett. 39, 6. https://doi.org/10.1029/2012GL051283 (2012).Article 

Google Scholar 
Smith, T. & Bookhagen, B. Climatic and biotic controls on topographic asymmetry at the global scale. J. Geophys. Res.: Earth Surf. 126, e2020JF005692. https://doi.org/10.1029/2020JF005692Received22 (2021).Article 
ADS 

Google Scholar 
Seyfried, M., Link, T., Marks, D. & Murdock, M. Soil temperature variability in complex terrain measured using fiber-optic distributed temperature sensing. Vadose Zone J. 15, 6. https://doi.org/10.2136/vzj2015.09.0128 (2016).Article 

Google Scholar 
Chambers, J. C. et al. Resilience and resistance of sagebrush ecosystems: Implications for state and transition models and management treatments. Rangel. Ecol. Manage. 67, 440–454. https://doi.org/10.2111/REM-D-13-00074.1 (2014).Article 

Google Scholar 
Chambers, J. C. et al. Operationalizing resilience and resistance concepts to address invasive grass-fire cycles. Front. Ecol. Evol. 7, 2369. https://doi.org/10.3389/fevo.2019.00185 (2019).Article 

Google Scholar 
Boehm, A. R. et al. Slope and aspect effects on seedbed microclimate and germination timing of fall-planted seeds. Rangel. Ecol. Manage. 75, 58–67. https://doi.org/10.1016/j.rama.2020.12.003 (2021).Article 

Google Scholar 
Sankey, J. B., Germino, M. J., Sankey, T. T. & Hoover, A. N. Fire effects on the spatial patterning of soil properties in sagebrush steppe, USA: A meta-analysis. Int. J. Wildl. Fire 21, 545–556. https://doi.org/10.1071/WF11092 (2012).Article 

Google Scholar 
Fellows, A., Flerchinger, G., Seyfried, M. S. & Lohse, K. A. Rapid recovery of mesic mountain big sagebrush gross production and respiration following prescribed fire. Ecosystems 21, 1283–1294. https://doi.org/10.1007/s10021-017-0218-9 (2018).Article 

Google Scholar 
Vega, S. P. et al. Interaction of wind and cold-season hydrologic processes on erosion from complex topography following wildfire in sagebrush steppe. Earth Surf. Process. Landforms https://doi.org/10.1002/esp.4778 (2019).Article 

Google Scholar 
Xie, J., Li, Y., Zhai, C., Li, C. & Lan, Z. CO2 absorption by alkaline soils and its implication to the global carbon cycle. Environ. Geol. 56, 953–961 (2009).Article 
ADS 
CAS 

Google Scholar 
Stanbery, C., Pierce, J. L., Benner, S. G. & Lohse, K. On the rocks: Quantifying storage of inorganic soil carbon on gravels and determining pedon-scale variability. CATENA 157, 436–442. https://doi.org/10.1016/j.catena.2017.06.011 (2017).Article 
CAS 

Google Scholar 
Stanbery, C. et al. Controls on the presence and concentration of soil inorganic carbon in a semi-arid watershed. CATENA https://doi.org/10.2139/ssrn.4267018 (2023).Article 

Google Scholar 
Cerling, T. E. & Quade, J. Stable carbon and oxygen isotopes in soil carbonates. Geophys. Monogr. 78, 217–231 (1993).ADS 

Google Scholar 
Tappa, D. J., Kohn, M. J., McNamara, J. P., Benner, S. G. & Flores, A. N. Isotopic composition of precipitation in a topographically steep, seasonally snow-dominated watershed and implications of variations from the global meteoric water line. Hydrol. Process. 30, 4582–4592. https://doi.org/10.1002/hyp.10940 (2016).Article 
ADS 
CAS 

Google Scholar 
Salomons, W., Goudie, A. & Mook, W. G. Isotopic composition of calcrete deposits from Europe, Africa and India. Earth Surf. Process. 3, 43–57. https://doi.org/10.1002/esp.3290030105 (1978).Article 
CAS 

Google Scholar 
Salomons, W. & Mook, W. G. In Handbook of Environmental Isotope Geochemistry (eds P. Fritz & J. Fontes) Ch. 6, 241–269 (Elsevier, 1986).Bodí, M. B. et al. Wildland fire ash: Production, composition and eco-hydro-geomorphic effects. Earth Sci. Rev. 130, 103–127. https://doi.org/10.1016/j.earscirev.2013.12.007 (2014).Article 
ADS 
CAS 

Google Scholar 
Kéraval, B. et al. Soil carbon dioxide emissions controlled by an extracellular oxidative metabolism identifiable by its isotope signature. Biogeosciences 13, 6353–6362. https://doi.org/10.5194/bg-13-6353-2016 (2016).Article 
ADS 
CAS 

Google Scholar 
Goforth, B. R., Graham, R. C., Hubbert, K. R., Zanner, C. W. & Minnich, R. A. Spatial distribution and properties of ash and thermally altered soils after high-severity forest fire, southern California. Int. J. Wildland Fire 14, 343–354 (2005).Article 

Google Scholar 
Glossner, K. L. et al. Long-term suspended sediment and particulate organic carbon yields from the Reynolds Creek Experimental Watershed and Critical Zone Observatory. Hydrol. Process. 36, e14484. https://doi.org/10.1002/hyp.14484 (2022).Article 
CAS 

Google Scholar 
Seyfried, M. S. et al. Reynolds creek experimental watershed and critical zone observatory. Vadoze Zone J. 17, 180129. https://doi.org/10.2136/vzj2018.07.0129 (2018).Article 
CAS 

Google Scholar 
McIntyre, D. H. Cenozoic geology of the Reynolds Creek Experimental Watershed, Owyhee County, Idaho (Idaho Bureau of Mines and Geology, 1972).Earth Resources Observation and Science (EROS) Center, U. Image of the week: Burned Area Analysis for the Soda Fire, Idaho, https://eros.usgs.gov/media-gallery/image-of-the-week/burned-area-analysis-the-soda-fire-idaho (2015).Jenny, H. Factors of Soil Formation (McGraw-Hill, 1941).Book 

Google Scholar 
Kormos, P. R. et al. 31 years of hourly spatially distributed air temperature, humidity, and precipitation amount and phase from Reynolds Critical Zone Observatory. Earth Syst. Sci. Data 10, 1197–1205. https://doi.org/10.5194/essd-10-1197-2018 (2018).Article 
ADS 

Google Scholar 
Thomas, G. W. In Methods in Soil Analysis. Part 3. Chemical Methods (ed Sparks, D. L. ) (Soil Science Society of America and American Society of Agronomy, 1996).Brodie, C. R. et al. Evidence for bias in C and N concentrations and δ13C composition of terrestrial and aquatic organic materials due to pre-analysis acid preparation methods. Chem. Geol. 282, 67–83. https://doi.org/10.1016/j.chemgeo.2011.01.007 (2011).Article 
ADS 
CAS 

Google Scholar 
Patton, N. P., Lohse, K. A., Seyfried, M. S., Will, R. & Benner, S. G. Lithology and coarse fraction adjusted bulk density estimates for determining total organic carbon stocks in dryland soils. Geoderma 337, 844–852. https://doi.org/10.1016/j.geoderma.2018.10.036 (2019).Article 
ADS 
CAS 

Google Scholar 
McGuire, L. A., Rasmussen, C., Youberg, A. M., Sanderman, J. & Fenerty, B. Controls on the Spatial distribution of near-surface pyrogenic carbon on hillslopes 1 year following wildfire. J. Geophys. Res.: Earth Surf. 126, e2020JF005996. https://doi.org/10.1029/2020JF005996 (2021).Article 
ADS 

Google Scholar 
Jiménez-González, M. A. et al. Spatial distribution of pyrogenic carbon in Iberian topsoils estimated by chemometric analysis of infrared spectra. Sci. Total Env. 790, 148170. https://doi.org/10.1016/j.scitotenv.2021.148170 (2021).Article 
CAS 

Google Scholar 
Baldock, J. A. et al. Quantifying the allocation of soil organic carbon to biologically significant fractions. Soil Res. 51, 561–576. https://doi.org/10.1071/SR12374 (2013).Article 
CAS 

Google Scholar 
Sanderman, J. et al. Soil organic carbon fractions in the Great Plains of the United States: An application of mid-infrared spectroscopy. Biogeochemistry 156, 97–114. https://doi.org/10.1007/s10533-021-00755-1 (2021).Article 
CAS 

Google Scholar 
Sherrod, L. A., Dunn, G., Peterson, G. A. & Kolberg, R. L. Inorganic carbon analysis by modified pressure-calcimeter method. Soil Sci. Soc. Am. J. 66, 299–305 (2002).Article 
ADS 
CAS 

Google Scholar 
Mikutta, R., Kleber, M., Kaiser, K. & Jahn, R. Review. Soil Sci. Soc. Am. J. 69, 120–135. https://doi.org/10.2136/sssaj2005.0120 (2005).Article 
ADS 
CAS 

Google Scholar 
Risk, D., Nickerson, N., Creelman, C., McArthur, G. & Owens, J. Forced Diffusion soil flux: A new technique for continuous monitoring of soil gas efflux. Agric. For. Meteorol. 151, 1622–1631. https://doi.org/10.1016/j.agrformet.2011.06.020 (2011).Article 
ADS 

Google Scholar 
Fierer, N. & Schimel, J. P. Effects of drying–rewetting frequency on soil carbon and nitrogen transformations. Soil Biol. Biochem. 34, 777–787. https://doi.org/10.1016/S0038-0717(02)00007-X (2002).Article 
CAS 

Google Scholar 
Dane, J. H., Topp, G. C. & Campbell, G. S. In Methods of Soil Analysis: Physical Methods. Vol. 4 (ed SSSA) 721–738 (2002). More

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

Google Scholar 
Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).Article 

Google Scholar 
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).Article 

Google Scholar 
Assis, J., Serrão, E. A., Duarte, C. M., Fragkopoulou, E. & Krause-Jensen, D. Major expansion of marine forests in a warmer Arctic. Front. Mar. Sci. 9, 850368 (2022).Article 

Google Scholar 
Assis, J. et al. Major shifts at the range edge of marine forests: The combined effects of climate changes and limited dispersal. Sci. Rep. 7(44348), 1–10 (2017).CAS 

Google Scholar 
O’Leary, J. K. et al. The resilience of marine ecosystems to climatic disturbances. BioScience. https://doi.org/10.1093/biosci/biw161 (2017).Article 

Google Scholar 
Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).Article 

Google Scholar 
Filbee-Dexter, K. & Scheibling, R. E. Detrital kelp subsidy supports high reproductive condition of deep-living sea urchins in a sedimentary basin. Aquat. Biol. 23, 71–86 (2014).Article 

Google Scholar 
Filbee-Dexter, K. Ocean forests hold unique solutions to our current environmental crisis. One Earth https://doi.org/10.1016/j.oneear.2020.05.004 (2020).Article 

Google Scholar 
Krumhansl, K. A. & Scheibling, R. E. Production and fate of kelp detritus. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps09940 (2012).Article 

Google Scholar 
Edwards, M. S. & Hernández-Carmona, G. Delayed recovery of giant kelp near its southern range limit in the North Pacific following El Niño. Mar. Biol. 147, 273–279 (2005).Article 

Google Scholar 
Cavanaugh, K. C., Reed, D. C., Bell, T. W., Castorani, M. C. N. & Beas-Luna, R. Spatial variability in the resistance and resilience of giant kelp in southern and Baja California to a multiyear heatwave. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00413 (2019).Article 

Google Scholar 
Butler, C. L., Lucieer, V. L., Wotherspoon, S. J. & Johnson, C. R. Multi-decadal decline in cover of giant kelp Macrocystis pyrifera at the southern limit of its Australian range. Mar. Ecol. Prog. Ser. 653, 1–18 (2020).Article 
ADS 

Google Scholar 
Martínez, B. et al. Distribution models predict large contractions of habitat-forming seaweeds in response to ocean warming. Divers. Distrib. 24, 1350–1366 (2018).Article 

Google Scholar 
Bell, T. W., Allen, J. G., Cavanaugh, K. C. & Siegel, D. A. Three decades of variability in California’s giant kelp forests from the Landsat satellites. Remote Sens. Environ. 238, 110811 (2020).Article 
ADS 

Google Scholar 
Mann, M. E. & Emanuel, K. A. Atlantic Hurricane trends linked to climate change. Eos 87, 233–241 (2006).Article 
ADS 

Google Scholar 
Jensen, J. R., Estes, J. E. & Tinney, L. Remote sensing techniques for kelp surveys. Photogramm. Eng Remote Sens. 46, 743–755 (1980).
Google Scholar 
Cavanaugh, K. C. et al. A review of the opportunities and challenges for using remote sensing for management of surface-canopy forming kelps. Front. Mar. Sci. https://doi.org/10.3389/fmars.2021.753531 (2021).Article 

Google Scholar 
Cavanaugh, K. C., Siegel, D. A., Reed, D. C. & Dennison, P. E. Environmental controls of giant-kelp biomass in the Santa Barbara Channel, California. Mar. Ecol. Prog. Ser. 429, 1–17 (2011).Article 
ADS 

Google Scholar 
Kadhim, M. A. & Abed, M. H. Convolutional neural network for satellite image classification. Stud. Comput. Intell. 830, 165–178 (2020).Article 

Google Scholar 
Segal-Rozenhaimer, M., Li, A., Das, K. & Chirayath, V. Cloud detection algorithm for multi-modal satellite imagery using convolutional neural-networks (CNN). Remote Sens. Environ. 237, 111446 (2020).Article 
ADS 

Google Scholar 
Canonico, G. et al. Global observational needs and resources for marine biodiversity. Front. Mar. Sci. 6, 367 (2019).Article 

Google Scholar 
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).Article 
ADS 
CAS 

Google Scholar 
Yu, L. & Gong, P. Google Earth as a virtual globe tool for Earth science applications at the global scale: Progress and perspectives. Int. J. Remote Sens. 33, 3966–3986 (2012).Article 

Google Scholar 
Guirado, E., Tabik, S., Rivas, M. L., Alcaraz-Segura, D. & Herrera, F. Whale counting in satellite and aerial images with deep learning. Sci. Rep. 9, 14259 (2019).Article 
ADS 

Google Scholar 
Borowicz, A. et al. Aerial-trained deep learning networks for surveying cetaceans from satellite imagery. PLoS ONE 14, 1–15 (2019).Article 

Google Scholar 
Lorencin, I., Anđelić, N., Mrzljak, V. & Car, Z. Marine objects recognition using convolutional neural networks. Nase More 66, 112–119 (2019).Article 

Google Scholar 
Ridge, J. T., Gray, P. C., Windle, A. E. & Johnston, D. W. Deep learning for coastal resource conservation: Automating detection of shellfish reefs. Remote Sens. Ecol. Conserv. 6, 431–440 (2020).Article 

Google Scholar 
Wang, Y. et al. Machine learning-based ship detection and tracking using satellite images for maritime surveillance. J. Ambient Intell. Smart Environ. 13, 361–371 (2021).Article 

Google Scholar 
Han, Q., Yin, Q., Zheng, X. & Chen, Z. Remote sensing image building detection method based on Mask R-CNN. Complex Intell. Syst. https://doi.org/10.1007/s40747-021-00322-z (2021).Article 

Google Scholar 
Girshick, R. Fast R-CNN. In 2015 IEEE International Conference on Computer Vision (ICCV) 1440–1448. https://doi.org/10.1109/ICCV.2015.169 (2015).Ren, S., He, K., Girshick, R. & Sun, J. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal. Mach. Intell. 39, 28 (2017).Article 

Google Scholar 
Shelhamer, E., Long, J. & Darrell, T. Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39, 3431–3440 (2017).Article 

Google Scholar 
He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask R-CNN. In Proceedings of the IEEE international Conference on Computer Vision (2017).Arafeh-Dalmau, N. et al. Extreme Marine Heatwaves alter kelp forest community near its equatorward distribution limit. Front. Mar. Sci. 6, 1–18 (2019).Article 
ADS 

Google Scholar 
Nie, X., Duan, M., Ding, H., Hu, B. & Wong, E. K. Attention Mask R-CNN for ship detection and segmentation from remote sensing images. IEEE Access 8, 9325–9334 (2020).Article 

Google Scholar 
Abdulla, W. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow. GitHub Repository (2017).Fragkopoulou, E. et al. Global biodiversity patterns of marine forests of brown macroalgae. Glob. Ecol. Biogeogr. https://doi.org/10.1111/geb.13450 (2022).Article 

Google Scholar 
Markham, B. L., Storey, J. C., Williams, D. L. & Irons, J. R. Landsat sensor performance: History and current status. IEEE Trans. Geosci. Remote Sens. https://doi.org/10.1109/TGRS.2004.840720 (2004).Article 

Google Scholar 
Gorelick, N. et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).Article 
ADS 

Google Scholar 
Aghamohamadnia, M. & Abedini, A. A morphology-stitching method to improve Landsat SLC-off images with stripes. Geodesy Geodyn. 5, 27–33 (2014).Article 

Google Scholar 
Houskeeper, H. F. et al. Automated satellite remote sensing of giant kelp at the Falkland Islands (Islas Malvinas). PLoS ONE 17, e0257933 (2022).Article 
CAS 

Google Scholar 
Mantha, K. B. et al. From Fat Droplets to Floating Forests: Cross-Domain Transfer Learning Using a PatchGAN-Based Segmentation Model (2022).Finger, D. J. I., McPherson, M. L., Houskeeper, H. F. & Kudela, R. M. Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring. Remote Sens. Environ. 254, 112243 (2021).Article 
ADS 

Google Scholar 
Siegel, D. A., Wang, M., Maritorena, S. & Robinson, W. Atmospheric correction of satellite ocean color imagery: The black pixel assumption. Appl. Opt. 39, 3582–3591 (2000).Article 
ADS 
CAS 

Google Scholar 
Loisel, H., Nicolas, J. M., Sciandra, A., Stramski, D. & Poteau, A. Spectral dependency of optical backscattering by marine particles from satellite remote sensing of the global ocean. J. Geophys. Res. Oceans https://doi.org/10.1029/2005JC003367 (2006).Article 

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

Google Scholar 
Dutta, A. & Zisserman, A. The VIA annotation software for images, audio and video. In MM 2019: Proceedings of the 27th ACM International Conference on Multimedia. https://doi.org/10.1145/3343031.3350535 (2019).Pfister, C. A., Berry, H. D. & Mumford, T. The dynamics of Kelp Forests in the Northeast Pacific Ocean and the relationship with environmental drivers. J. Ecol. 106, 1520–1533 (2018).Article 

Google Scholar 
Cavanaugh, K. C., Cavanaugh, K. C., Bell, T. W. & Hockridge, E. G. An automated method for mapping giant kelp canopy dynamics from UAV. Front. Environ. Sci. 8, 587354 (2021).Article 

Google Scholar 
Castorani, M. C. N. et al. Connectivity structures local population dynamics: A long-term empirical test in a large metapopulation system. Ecology 96, 3141–3152 (2015).Article 

Google Scholar 
Irmak, E. Implementation of convolutional neural network approach for COVID-19 disease detection. Physiol. Genom. 52, 590–601 (2020).Article 
CAS 

Google Scholar 
Assis, J., Araújo, M. B. & Serrão, E. A. Projected climate changes threaten ancient refugia of kelp forests in the North Atlantic. Glob. Change Biol. 24, 1365–2486 (2017).
Google Scholar 
Cao, C. et al. An improved faster R-CNN for small object detection. IEEE Access 7, 106838–106846 (2019).Article 

Google Scholar 
Konar, J., Khandelwal, P. & Tripathi, R. Comparison of various learning rate scheduling techniques on convolutional neural network. In 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science, SCEECS 2020. https://doi.org/10.1109/SCEECS48394.2020.94 (2020).LeCun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc. IEEE 86, 2278–2324 (1998).Article 

Google Scholar 
Johnson, J. W. Automatic nucleus segmentation with mask-RCNN. Adv. Intell. Syst. Comput. 944, 399–407 (2020).
Google Scholar 
Lin, T. Y. et al. Microsoft COCO: Common objects in context. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 8693 LNCS (2014).McKnight, P. E. & Najab, J. Mann-Whitney U Test. Corsini Encycl. Psychol. https://doi.org/10.1002/9780470479216.corpsy0524 (2010).Article 

Google Scholar 
R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).
Google Scholar 
Haklay, M. & Weber, P. OpenStreet map: User-generated street maps. IEEE Pervasive Comput. 7, 12–18 (2008).Article 

Google Scholar 
Wäldchen, J. & Mäder, P. Machine learning for image based species identification. Methods Ecol. Evol. https://doi.org/10.1111/2041-210X.13075 (2018).Article 
MATH 

Google Scholar 
Weinstein, B. G. A computer vision for animal ecology. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.12780 (2018).Article 

Google Scholar 
Chilson, C. et al. Automated detection of bird roosts using NEXRAD radar data and Convolutional Neural Networks. Remote Sens. Ecol. Conserv. 5, 20–32 (2019).Article 

Google Scholar 
O’Gara, S. & McGuinness, K. Comparing data augmentation strategies for deep image classification. Ir. Mach. Vis. Image Process. Conf. https://doi.org/10.21427/148b-ar75 (2019).Article 

Google Scholar 
Li, W., Chen, C., Zhang, M., Li, H. & Du, Q. Data augmentation for hyperspectral image classification with deep CNN. IEEE Geosci. Remote Sens. Lett. 16, 593–597 (2019).Article 
ADS 

Google Scholar 
Bharati, P. & Pramanik, A. Deep learning techniques—R-CNN to Mask R-CNN: A survey. In Computational Intelligence in Pattern Recognition (eds Das, A. K. et al.) 657–668 (Springer, 2020).Chapter 

Google Scholar 
Li, A. S., Chirayath, V., Segal-Rozenhaimer, M., Torres-Perez, J. L. & van den Bergh, J. NASA NeMO-Net’s convolutional neural network: Mapping marine habitats with spectrally heterogeneous remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 13, 5115–5133 (2020).Article 
ADS 

Google Scholar 
Hamilton, S. L., Bell, T. W., Watson, J. R., Grorud-Colvert, K. A. & Menge, B. A. Remote sensing: generation of long-term kelp bed data sets for evaluation of impacts of climatic variation. Ecology 101, e03031 (2020).Article 

Google Scholar 
Bell, T. W., Cavanaugh, K. C. & Siegel, D. A. Remote monitoring of giant kelp biomass and physiological condition: An evaluation of the potential for the Hyperspectral Infrared Imager (HyspIRI) mission. Remote Sens. Environ. 167, 218–228 (2015).Article 
ADS 

Google Scholar 
Schroeder, S. B., Dupont, C., Boyer, L., Juanes, F. & Costa, M. Passive remote sensing technology for mapping bull kelp (Nereocystis luetkeana): A review of techniques and regional case study. Glob. Ecol. Conserv. https://doi.org/10.1016/j.gecco.2019.e00683 (2019).Article 

Google Scholar 
Kristollari, V. & Karathanassi, V. Convolutional neural networks for detecting challenging cases in cloud masking using Sentinel-2 imagery. Remote Sens. Geoinf. Environ. https://doi.org/10.1117/12.2571111 (2020).Article 

Google Scholar 
Wilson, M. J. & Oreopoulos, L. Enhancing a simple MODIS cloud mask algorithm for the landsat data continuity mission. IEEE Trans. Geosci. Remote Sens. 51, 723–731 (2013).Article 
ADS 

Google Scholar 
Zhuge, X. Y., Zou, X. & Wang, Y. A fast cloud detection algorithm applicable to monitoring and nowcasting of daytime cloud systems. IEEE Trans. Geosci. Remote Sens. 55, 6111–6119 (2017).Article 
ADS 

Google Scholar 
Lin, T. Y. et al. Feature pyramid networks for object detection. In Proceedings: 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 (2017).Jacox, M. G. et al. Impacts of the 2015–2016 El Niño on the California Current System: Early assessment and comparison to past events. Geophys. Res. Lett. https://doi.org/10.1002/2016GL069716 (2016).Article 

Google Scholar 
Chavez, F. P. et al. Biological and chemical consequences of the 1997–1998 El Niño in central California waters. Prog. Oceanogr. https://doi.org/10.1016/S0079-6611(02)00050-2 (2002).Article 

Google Scholar 
Tegner, M. J. & El Dayton, P. K. Niño effects on Southern California kelp forest communities. Adv. Ecol. Res. 17, 243–279 (1987).Article 

Google Scholar 
Bartsch, I. et al. Changes in kelp forest biomass and depth distribution in Kongsfjorden, Svalbard, between 1996–1998 and 2012–2014 reflect Arctic warming. Polar Biol. 39, 2021–2036 (2016).Article 

Google Scholar 
Simonson, E. J., Scheibling, R. E. & Metaxas, A. Kelp in hot water: I. Warming seawater temperature induces weakening and loss of kelp tissue. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps11438 (2015).Article 

Google Scholar 
Oliver, E. C. J. et al. Projected marine heatwaves in the 21st century and the potential for ecological impact. Front. Mar. Sci. https://doi.org/10.3389/fmars.2019.00734 (2019).Article 

Google Scholar  More

Monte Carlo analysisSeaweed production costs and net costs of climate benefits were estimated on the basis of outputs of the biophysical and technoeconomic models described below. The associated uncertainties and sensitivities were quantified by repeatedly sampling from uniform distributions of plausible values for each cost and economic parameter (n = 5,000 for each nutrient scenario from the biophysical model, for a total of n = 10,000 simulations; see Supplementary Figs. 14 and 15)47,48,49,50,51,52. Parameter importance across Monte Carlo simulations (Fig. 3 and Supplementary Fig. 9) was determined using decision trees in LightGBM, a gradient-boosting machine learning framework.Biophysical modelG-MACMODS is a nutrient-constrained, biophysical macroalgal growth model with inputs of temperature, nitrogen, light, flow, wave conditions and amount of seeded biomass30,53, that we used to estimate annual seaweed yield per area (either in tons of carbon or tons of dry weight biomass per km2 per year)33,34. In the model, seaweed takes up nitrogen from seawater, and that nitrogen is held in a stored pool before being converted to structural biomass via growth54. Seaweed biomass is then lost via mortality, which includes breakage from variable ocean wave intensity. The conversion from stored nitrogen to biomass is based on the minimum internal nitrogen requirements of macroalgae, and the conversion from biomass to units of carbon is based on an average carbon content of macroalgal dry weight (~30%)55. The model accounts for farming intensity (sub-grid-scale crowding) and employs a conditional harvest scheme, where harvest is optimized on the basis of growth rate and standing biomass33.The G-MACMODS model is parameterized for four types of macroalgae: temperate brown, temperate red, tropical brown and tropical red. These types employed biophysical parameters from genera that represent over 99.5% of present-day farmed macroalgae (Eucheuma, Gracilaria, Kappahycus, Sargassum, Porphyra, Saccharina, Laminaria, Macrocystis)39. Environmental inputs were derived from satellite-based and climatological model output mapped to 1/12-degree global resolution, which resolves continental shelf regions. Nutrient distributions were derived from a 1/10-degree resolution biogeochemical simulation led by the National Center for Atmospheric Research (NCAR) and run in the Community Earth System Model (CESM) framework35.Two nutrient scenarios were simulated with G-MACMODS and evaluated using the technoeconomic model analyses described below: the ‘ambient nutrient’ scenario where seaweed growth was computed using surface nutrient concentrations without depletion or competition, and ‘limited nutrient’ simulations where seaweed growth was limited by an estimation of the nutrient supply to surface waters (computed as the flux of deep-water nitrate through a 100 m depth horizon). For each Monte Carlo simulation in the economic analysis, the technoeconomic model randomly selects either the 5th, 25th, 50th, 75th or 95th percentile G-MACMODS seaweed yield map from a normal distribution to use as the yield map for that simulation. Figures and numbers reported in the main text are based on the ambient-nutrient scenario; results based on the limited-nutrient scenario are shown in Supplementary Figures.Technoeconomic modelAn interactive web tool of the technoeconomic model is available at https://carbonplan.org/research/seaweed-farming.We estimated the net cost of seaweed-related climate benefits by first estimating all costs and emissions related to seaweed farming, up to and including the point of harvest at the farm location, then estimating costs and emissions related to the transportation and processing of harvested seaweed, and finally estimating the market value of seaweed products and either carbon sequestered or GHG emissions avoided.Production costs and emissionsSpatially explicit costs of seaweed production ($ tDW−1) and production-related emissions (tCO2 tDW−1) were calculated on the basis of ranges of capital costs ($ km−2), operating costs (including labour, $ km−2), harvest costs ($ km−2) and transport emissions per distance travelled (tCO2 km−1) in the literature (Table 1, Supplementary Tables 1 and 2); annual seaweed biomass (tDW km−2, for the preferred seaweed type in each grid cell), line spacing and number of harvests (species-dependent) from the biophysical model; as well as datasets of distances to the nearest port (km), ocean depth (m) and significant wave height (m).Capital costs were calculated as:$$c_{cap} = c_{capbase} + left( {c_{capbase} times left( {k_d + k_w} right)} right) + c_{sl}$$
(1)
where ccap is the total annualized capital costs per km2, ccapbase is the annualized capital cost per km2 (for example, cost of buoys, anchors, boats, structural rope) before applying depth and wave impacts, kd and kw are the impacts of depth and waviness on capital cost, respectively, each expressed as a multiplier between 0 and 1 modelled using our Monte Carlo method and applied only to grid cells with depth >500 m and/or significant wave height >3 m, respectively, and csl is the total annual cost of seeded line calculated as:$$c_{sl} = c_{slbase} times p_{sline}$$
(2)
where cslbase is the cost per metre of seeded line, and psline is the total length of line per km2, based on the optimal seaweed type grown in each grid cell.Operating and maintenance costs were calculated as:$$c_{op} = c_{ins} + c_{lic} + c_{lab} + c_{opbase}$$
(3)
where cop is the total annualized operating and maintenance costs per km2, cins is the annual insurance cost per km2, clic is the annual cost of a seaweed aquaculture license per km2, clab is the annual cost of labour excluding harvest labour, and copbase is all other operating and maintenance costs.Harvest costs were calculated as:$$c_{harv} = c_{harvbase} times n_{harv}$$
(4)
where charv is the total annual costs associated with harvesting seaweed per km2, charvbase is the cost per harvest per km2 (including harvest labour but excluding harvest transport), and nharv is the total number of harvests per year.Costs associated with transporting equipment to the farming location were calculated as:$$c_{eqtrans} = c_{transbase} times m_{eq} times d_{port}$$
(5)
where ceqtrans is total annualized cost of transporting equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.The total production cost of growing and harvesting seaweed was therefore calculated as:$$c_{prod} = frac{{left( {c_{cap}} right) + left( {c_{op}} right) + left( {c_{harv}} right) + (c_{eqtrans})}}{{s_{dw}}}$$
(6)
where cprod is total annual cost of seaweed production (growth + harvesting), ccap is as calculated in equation (1), cop is as calculated in equation (3), charv is as calculated in equation (4), ceqtrans is as calculated in equation (5) and sdw is the DW of seaweed harvested annually per km2.Emissions associated with transporting equipment to the farming location were calculated as:$$e_{eqtrans} = e_{transbase} times m_{eq} times d_{port}$$
(7)
where eeqtrans is the total annualized CO2 emissions in tons from transporting equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons and dport is the ocean distance to the nearest port in km.Emissions from maintenance trips to/from the seaweed farm were calculated as:$$e_{mnt} = left( {left( {2 times d_{port}} right) times e_{mntbase} times left( {frac{{n_{mnt}}}{{a_{mnt}}}} right)} right) + (e_{mntbase} times d_{mnt})$$
(8)
where emnt is total annual CO2 emissions from farm maintenance, dport is the ocean distance to the nearest port in km, nmnt is the number of maintenance trips per km2 per year, amnt is the area tended to per trip, dmnt is the distance travelled around each km2 for maintenance and emntbase is the CO2 emissions from travelling 1 km on a typical fishing maintenance vessel (for example, a 14 m Marinnor vessel with 2 × 310 hp engines) at an average speed of 9 knots (16.67 km h−1), resulting in maintenance vessel fuel consumption of 0.88 l km−1 (refs. 28,56).Total emissions from growing and harvesting seaweed were therefore calculated as:$$e_{prod} = frac{{(e_{eqtrans}) + (e_{mnt})}}{{s_{dw}}}$$
(9)
where eprod is total annual emissions from seaweed production (growth + harvesting), eeqtrans is as calculated in equation (7), emnt is as calculated in equation (8) and sdw is the DW of seaweed harvested annually per km2.Market value and climate benefits of seaweedFurther transportation and processing costs, economic value and net emissions of either sinking seaweed in the deep ocean for carbon sequestration or converting seaweed into usable products (biofuel, animal feed, pulses, vegetables, fruits, oil crops and cereals) were calculated on the basis of ranges of transport costs ($ tDW−1 km−1), transport emissions (tCO2-eq t−1 km−1), conversion cost ($ tDW−1), conversion emissions (tCO2-eq tDW−1), market value of product ($ tDW−1) and the emissions avoided by product (tCO2-eq tDW−1) in the literature (Table 1). Market value was treated as globally homogeneous and does not vary by region. Emissions avoided by products were determined by comparing estimated emissions related to seaweed production to emissions from non-seaweed products that could potentially be replaced by seaweed (including non-CO2 greenhouse gas emissions from land use)24. Other parameters used are distance to nearest port (km), water depth (m), spatially explicit sequestration fraction (%)57 and distance to optimal sinking location (km; cost-optimized for maximum emissions benefit considering transport emissions combined with spatially explicit sequestration fraction; see ‘Distance to sinking point calculation’ below). Each Monte Carlo simulation calculated the cost of both CDR via sinking seaweed and GHG emissions mitigation via seaweed products.For seaweed CDR, after the seaweed is harvested, it can either be sunk in the same location that it was grown, or be transported to a more economically favourable sinking location where more of the seaweed carbon would remain sequestered for 100 yr (see ‘Distance to optimal sinking point’ below). Immediately post-harvest, the seaweed still contains a large amount of water, requiring a conversion from dry mass to wet mass for subsequent calculations33:$$s_{ww} = frac{{s_{dw}}}{{0.1}}$$
(10)
where sww is the annual wet weight of seaweed harvested per km2 and sdw is the annual DW of seaweed harvested per km2.The cost to transport harvested seaweed to the optimal sinking location was calculated as:$$c_{swtsink} = c_{transbase} times d_{sink} times s_{ww}$$
(11)
where cswtsink is the total annual cost to transport harvested seaweed to the optimal sinking location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10).The costs associated with transporting replacement equipment (for example, lines, buoys,anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type) in the sinking CDR pathway were calculated as:$$c_{eqtsink} = left( {c_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (c_{transbase} times d_{port} times m_{eq})$$
(12)
where ceqtsink is the total annualized cost to transport both used and replacement equipment, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port (see Supplementary Fig. 16). These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).The total value of seaweed that is sunk for CDR was therefore calculated as:$$v_{sink} = frac{{left( {v_{cprice} – left( {c_{swtsink} + c_{eqtsink}} right)} right)}}{{s_{dw}}}$$
(13)
where vsink is the total value (cost, if negative) of seaweed farmed for CDR in $ tDW−1, vcprice is a theoretical carbon price, cswtsink is as calculated in equation (11), ceqtsink is as calculated in equation (12) and sdw is the annually harvested seaweed DW in t km−2. We did not assume any carbon price in our Monte Carlo simulations (vcprice is equal to zero), making vsink negative and thus representing a net cost.To calculate net carbon impacts, our model included uncertainty in the efficiency of using the growth and subsequent deep-sea deposition of seaweed as a CDR method. The uncertainty is expected to include the effects of reduced phytoplankton growth from nutrient competition, the relationship between air–sea gas exchange and overturning circulation (hereafter collectively referred to as the ‘atmospheric removal fraction’) and the fraction of deposited seaweed carbon that remains sequestered for at least 100 yr. The total amount of atmospheric CO2 removed by sinking seaweed was calculated as:$$e_{seqsink} = k_{atm} times k_{fseq} times frac{{tC}}{{tDW}} times frac{{tCO_2}}{{tC}}$$
(14)
where eseqsink is net atmospheric CO2 sequestered annually in t km−2, katm is the atmospheric removal fraction and kfseq is the spatially explicit fraction of sunk seaweed carbon that remains sequestered for at least 100 yr (see ref. 57).The emissions from transporting harvested seaweed to the optimal sinking location were calculated as:$$e_{swtsink} = e_{transbase} times d_{sink} times s_{ww}$$
(15)
where eswtsink is the total annual CO2 emissions from transporting harvested seaweed to the optimal sinking location in tCO2 km−2, etransbase is the CO2 emissions (tons) from transporting 1 ton of material 1 km on a barge (tCO2 per t-km), dsink is the distance in km to the economically optimized sinking location and sww is the annually harvested seaweed wet weight in t km−2 as in equation (10). Since the unit for etransbase is tCO2 per t-km, the emissions from transporting seaweed to the optimal sinking location are equal to (e_{mathrm{transbase}} times d_{mathrm{sink}} times s_{mathrm{ww}}), and the emissions from transporting seaweed from the optimal sinking location back to the farm are equal to 0 (since the seaweed has already been deposited, the seaweed mass to transport is now 0). Note that this does not yet include transport emissions from transport of equipment post-seaweed-deposition (see equation 16 below and Supplementary Fig. 16).The emissions associated with transporting replacement equipment (for example, lines, buoys, anchors) to the farming location and hauling back used equipment at the end of its assumed lifetime (1 yr for seeded line, 5–20 yr for capital equipment by equipment type)28,41 in the sinking CDR pathway were calculated as:$$e_{eqtsink} = left( {e_{transbase} times left( {2 times d_{sink}} right) times m_{eq}} right) + (e_{transbase} times d_{port} times m_{eq})$$
(16)
where eeqtsink is the total annualized CO2 emissions in tons from transporting both used and replacement equipment, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dsink is the distance in km to the economically optimized sinking location and dport is the ocean distance to the nearest port in km. We assumed that the harvesting barge travels from the farming location directly to the optimal sinking location with harvested seaweed and replaced (used) equipment in tow (including used seeded line and annualized mass of used capital equipment), sinks the harvested seaweed, returns to the farm location and then returns to the nearest port. These calculations assumed the shortest sea-route distance (see Distance to optimal sinking point).Net CO2 emissions removed from the atmosphere by sinking seaweed were thus calculated as:$$e_{remsink} = frac{{left( {e_{seqsink} – left( {e_{swtsink} + e_{eqtsink}} right)} right)}}{{s_{dw}}}$$
(17)
where eremsink is the net atmospheric CO2 removed per ton of seaweed DW, eseqsink is as calculated in equation (14), eswtsink is as calculated in equation (15), eeqtsink is as calculated in equation (16) and sdw is the annually harvested seaweed DW in t km−2.Net cost of climate benefitsSinkingTo calculate the total net cost and emissions from the production, harvesting and transport of seaweed for CDR, we combined the cost and emissions from the sinking-pathway cost and value modules. The total net cost of seaweed CDR per DW ton of seaweed was calculated as:$$c_{sinknet} = c_{prod} – v_{sink}$$
(18)
where csinknet is the total net cost of seaweed for CDR per DW ton harvested, cprod is the net production cost per DW ton as calculated in equation (6) and vsink is the net value (or cost, if negative) per ton seaweed DW as calculated in equation (13).The total net CO2 emissions removed per DW ton of seaweed were calculated as:$$e_{sinknet} = e_{remsink} – e_{prod}$$
(19)
where esinknet is the total net atmospheric CO2 removed per DW ton of seaweed harvested annually (tCO2 tDW−1 yr−1), eremsink is the net atmospheric CO2 removed via seaweed sinking annually as calculated in equation (17) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where esinknet is negative (that is, net emissions rather than net removal) were not included in subsequent calculations since they would not be contributing to CDR in that location under the given scenario. Note that these net emissions cases only occur in areas far from port in specific high-emissions scenarios. Even in such cases, most areas still contribute to CO2 removal (negative emissions), hence costs from locations with net removal were included.Total net cost was then divided by total net emissions to get a final value for cost per ton of atmospheric CO2 removed:$$c_{pertonsink} = frac{{c_{sinknet}}}{{e_{sinknet}}}$$
(20)
where cpertonsink is the total net cost per ton of atmospheric CO2 removed via seaweed sinking ($ per tCO2 removed), csinknet is total net cost per ton seaweed DW harvested as calculated in equation (18) ($ tDW−1) and esinknet is the total net atmospheric CO2 removed per ton seaweed DW harvested as calculated in equation (19) (tCO2 tDW−1).GHG emissions mitigationInstead of sinking seaweed for CDR, seaweed can be used to make products (including but not limited to food, animal feed and biofuels). Replacing convention products with seaweed-based products can result in ‘avoided emissions’ if the emissions from growing, harvesting, transporting and converting seaweed into products are less than the total greenhouse gas emissions (including non-CO2 GHGs) embodied in conventional products that seaweed-based products replace.When seaweed is used to make products, we assumed it is transported back to the nearest port immediately after being harvested. The annualized cost to transport the harvested seaweed and replacement equipment (for example, lines, buoys, anchors) was calculated as:$$c_{transprod} = frac{{left( {c_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
(21)
where ctransprod is the annualized cost per ton seaweed DW to transport seaweed and equipment back to port from the farm location, ctransbase is the cost to transport 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.The total value of seaweed that is used for seaweed-based products was calculated as:$$v_{product} = v_{mkt} – left( {c_{transprod} + c_{conv}} right)$$
(22)
where vproduct is the total value (cost, if negative) of seaweed used for products ($ tDW−1), vmkt is how much each ton of seaweed would sell for, given the current market price of conventional products that seaweed-based products replace ($ tDW−1), ctransprod is as calculated in equation (21) and cconv is the cost to convert each ton of seaweed to a usable product ($ tDW−1).The annualized CO2 emissions from transporting harvested seaweed and equipment back to port were calculated as:$$e_{transprod} = frac{{left( {e_{transbase} times d_{port} times left( {s_{ww} + m_{eq}} right)} right)}}{{s_{dw}}}$$
(23)
where etransprod is the annualized CO2 emissions per ton seaweed DW to transport seaweed and equipment back to port from the farm location, etransbase is the CO2 emissions from transporting 1 ton of material 1 km on a barge, meq is the annualized equipment mass in tons, dport is the ocean distance to the nearest port in km, sww is the annual wet weight of seaweed harvested per km2 as calculated in equation (10) and sdw is the annual DW of seaweed harvested per km2.Total emissions avoided by each ton of harvested seaweed DW were calculated as:$$e_{avprod} = e_{subprod} – left( {e_{transprod} + e_{conv}} right)$$
(24)
where eavprod is total CO2-eq emissions avoided per ton of seaweed DW per year (including non-CO2 GHGs using a GWP time period of 100 yr), esubprod is the annual CO2-eq emissions avoided per ton seaweed DW by replacing a conventional product with a seaweed-based product, etransprod is as calculated in equation (23) and econv is the annual CO2 emissions per ton seaweed DW from converting seaweed into usable products. esubprod was calculated by converting seaweed DW to caloric content58 for food/feed and comparing emissions intensity per kcal to agricultural products24, or by converting seaweed DW into equivalent biofuel content with a yield of 0.25 tons biofuel per ton DW59 and dividing the CO2 emissions per ton fossil fuel by the seaweed biofuel yield.To calculate the total net cost and emissions from the production, harvesting, transport and conversion of seaweed for products, we combined the cost and emissions from the product-pathway cost and value modules. The total net cost of seaweed for products per ton DW was calculated as:$$c_{prodnet} = c_{prod} – v_{product}$$
(25)
where cprodnet is the total net cost per ton DW of seaweed harvested for use in products, cprod is the net production cost per ton DW as calculated in equation (6) and vproduct is the net value (or cost, if negative) per ton DW as calculated in equation (22).The total net CO2-eq emissions avoided per ton DW of seaweed used in products were calculated as:$$e_{prodnet} = e_{avprod} – e_{prod}$$
(26)
where eprodnet is the total net CO2-eq emissions avoided per ton DW of seaweed harvested annually (tCO2 tDW−1 yr−1), eavprod is the net CO2-eq emissions avoided by seaweed products annually as calculated in equation (24) and eprod is the net CO2 emitted from production and harvesting of seaweed annually as calculated in equation (9). For each Monte Carlo simulation, locations where eprodnet is negative (that is, net emissions rather than net emissions avoided) were not included in subsequent calculations since they would not be avoiding any emissions in that scenario.Total net cost was then divided by total net emissions avoided to get a final value for cost per ton of CO2-eq emissions avoided:$$c_{pertonprod} = frac{{c_{prodnet}}}{{e_{prodnet}}}$$
(27)
where cpertonprod is the total net cost per ton of CO2-eq emissions avoided by seaweed products ($ per tCO2-eq avoided), cprodnet is total net cost per ton seaweed DW harvested for products as calculated in equation (25) ($ tDW−1) and eprodnet is total net CO2-eq emissions avoided per ton seaweed DW harvested for products as calculated in equation (26) (tCO2 tDW−1).Parameter ranges for Monte Carlo simulationsFor technoeconomic parameters with two or more literature values (see Supplementary Table 1), we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions. For parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. Values at each end of parameter ranges were then rounded before Monte Carlo simulations as follows: capital costs, operating costs and harvest costs to the nearest $10,000 km−2, labour costs and insurance costs to the nearest $1,000 km−2, line costs to the nearest $0.05 m−1, transport costs to the nearest $0.05 t−1 km−1, transport emissions to the nearest 0.000005 tCO2 t−1 km−1, maintenance transport emissions to the nearest 0.0005 tCO2 km−1, product-avoided emissions to the nearest 0.1 tCO2-eq tDW−1, conversion cost down to the nearest $10 tDW−1 on the low end of the range and up to the nearest $10 tDW−1 on the high end of the range, and conversion emissions to the nearest 0.01 tCO2 tDW−1.We extended the minimum range values of capital costs to $10,000 km−2 and transport emissions to 0 to reflect potential future innovations, such as autonomous floating farm setups that would lower capital costs and net-zero emissions boats that would result in 0 transport emissions. To calculate the minimum value of $10,000 km−2 for a potential autonomous floating farm, we assumed that the bulk of capital costs for such a system would be from structural lines and flotation devices, and we therefore used the annualized structural line (system rope) and buoy costs from ref. 41 rounded down to the nearest $5,000 km−2. The full ranges used for our Monte Carlo simulations and associated literature values are shown in Supplementary Table 1.Distance to optimal sinking pointDistance to the optimal sinking point was calculated using a weighted distance transform (path-finding algorithm, modified from code in ref. 60) that finds the shortest ocean distance from each seaweed growth pixel to the location at which the net CO2 removed is maximized (including impacts of both increased sequestration fraction and transport emissions for different potential sinking locations) and the net cost is minimized. This is not necessarily the location in which the seaweed was grown, since the fraction of sunk carbon that remains sequestered for 100 yr is spatially heterogeneous (see ref. 57). For each ocean grid cell, we determined the cost-optimal sinking point by iteratively calculating equations (11–20) and assigning dsink as the distance calculated by weighted distance transform to each potential sequestration fraction (0.01–1.00) in increments of 0.01. Except for transport emissions, the economic parameter values used for these calculations were the averages of unrounded literature value ranges; we assumed that the maximum literature value reflected the 95th percentile and the minimum literature value represented the 5th percentile of potential costs or emissions, or for parameters with only one literature value, we added ±50% to the literature value to represent greater uncertainty within the modelled parameter range. For transport and maintenance transport emissions, we extended the minimum values of the literature ranges to zero to reflect potential net-zero emissions transport options and used the mean values of the resulting ranges. The dsink that resulted in minimum net cost per ton CO2 for each ocean grid cell was saved as the final dsink map, and the associated sequestration fraction value that the seaweed is transported to via dsink was assigned to the original cell where the seaweed was farmed and harvested (Supplementary Fig. 19). If the cost-optimal location to sink using this method was the same cell where the seaweed was harvested, then dsink was 0 km and the sequestration fraction was not modified from its original value (Supplementary Fig. 18).Comparison of gigaton-scale sequestration area to previous estimatesPrevious related work estimating the ocean area suitable for macroalgae cultivation13 and/or the area that might be required to reach gigaton-scale carbon removal via macroalgae cultivation13,19,36 has yielded a wide range of results, primarily due to differences in modelling methods. For example, Gao et al. (2022)36 estimate that 1.15 million km2 would be required to sequester 1 GtCO2 annually when considering carbon lost from seaweed biomass/sequestered as particulate organic carbon (POC) and refractory dissolved organic carbon (rDOC), and assume that the harvested seaweed is sold as food such that the carbon in the harvested seaweed is not sequestered. The area (0.31 million km2) required to sequester 1 GtCO2 in our study assumes that all harvested seaweed is sunk to the deep ocean to sequester carbon.Additionally, Wu et al.19 estimates that roughly 12 GtCO2 could be sequestered annually via macroalgae cultivation in approximately 20% of the world ocean area (that is, 1.67% ocean area per GtCO2), which is a much larger area per GtCO2 than our estimate of 0.085% ocean area. This notable difference arises for several reasons (including differences in yields, which in Wu et al. are around 500 tDW yr−1 in the highest-yield areas, whereas yields in our cheapest sequestration areas from G-MACMODS average 3,400 tDW km−2 yr−1) that arise from differences in model methodology. First, Wu et al. model temperate brown seaweeds, while our study considers different seaweed types, many of which have higher growth rates, and uses the most productive seaweed type for each ocean grid cell. The G-MACMODS seaweed growth model we use also has a highly optimized harvest schedule, includes luxury nutrient uptake (a key feature of macroalgal nutrient physiology) and does not directly model competition with phytoplankton during seaweed growth. Finally, tropical red seaweeds (the seaweed type in our cheapest sequestration areas) grow year-round, while others, such as the temperate brown seaweeds modelled by Wu et al., only grow seasonally. These differences all contribute to higher productivity in our model, leading to a smaller area required for gigaton-scale CO2 sequestration compared with Wu et al.Conversely, the ocean areas we model for seaweed-based CO2 sequestration or GHG emissions avoided are much larger than the 48 million km2 that Froehlich et al.13 estimate to be suitable for macroalgae farming globally. Although our maps show productivity and costs everywhere, the purpose of our modelling was to evaluate where different types of seaweed grow best and how production costs and product values vary over space, to highlight the lowest-cost areas (which are often the highest-producing areas) under various technoeconomic assumptions.Comparison of seaweed production costs to previous estimatesAlthough there are not many estimates of seaweed production costs in the scientific literature, our estimates for the lowest-cost 1% area of the ocean ($190–$2,790 tDW−1) are broadly consistent with previously published results: seaweed production costs reported in the literature range from $120 to $1,710 tDW−1 (refs. 40,41,61,62), but are highly dependent on assumed seaweed yields. For example, Camus et al.41 calculate a cost of $870 tDW−1 assuming a minimum yield of 12.4 kgDW m−1 of cultivation line (equivalent to 8.3 kgDW m−2 using 1.5 m spacing between lines). Using the economic values from Camus et al. but with our estimates of average yield for the cheapest 1% production cost areas (2.6 kgDW m−2) gives a much higher average cost of $2,730 tDW−1. Contrarily, van den Burg et al.40 calculate a cost of $1,710 tDW−1 using a yield of 20 tDW ha−1 (that is, 2.0 kg m−2). Instead assuming the average yield to be that from our lowest-cost areas (that is, 2.6 kgDW m−2 or 26 tDW ha−1) would decrease the cost estimated by van den Burg et al. (2016) to $1,290 tDW−1. Most recently, Capron et al.62 calculate an optimistic scenario cost of $120 tDW−1 on the basis of an estimated yield of 120 tDW ha−1 (12 kg m−2; over 4.5 times higher than the average yield in our lowest-cost areas). Again, instead assuming the average yield to be that in our lowest-cost areas would raise Capron et al.’s production cost to $540 tDW−1 (between the $190–$880 tDW−1 minimum to median production costs in the cheapest 1% areas from our model; Fig. 1a,b).Data sourcesSeaweed biomass harvestedWe used spatially explicit data for seaweed harvested globally under both ambient and limited-nutrient scenarios from the G-MACMODS seaweed growth model33.Fraction of deposited carbon sequestered for 100 yrWe used data from ref. 57 interpolated to our 1/12-degree grid resolution.Distance to the nearest portWe used the Distance from Port V1 dataset from Global Fishing Watch (https://globalfishingwatch.org/data-download/datasets/public-distance-from-port-v1) interpolated to our 1/12-degree grid resolution.Significant wave heightWe used data for annually averaged significant wave height from the European Center for Medium-range Weather Forecasts (ECMWF) interpolated to our 1/12-degree grid resolution.Ocean depthWe used data from the General Bathymetric Chart of the Oceans (GEBCO).Shipping lanesWe used data of Automatic Identification System (AIS) signal count per ocean grid cell, interpolated to our 1/12-degree grid resolution. We defined a major shipping lane grid cell as any cell with >2.25 × 108 AIS signals, a threshold that encompasses most major trans-Pacific and trans-Atlantic shipping lanes as well as major shipping lanes in the Indian Ocean, the North Sea, and coastal routes worldwide.Marine protected areas (MPAs)We used data from the World Database on Protected Areas (WDPA) and defined an MPA as any ocean MPA >20 km2.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article. More