Ecology

Orr, H. A. The genetic theory of adaptation: a brief history. Nat. Rev. Genet. 6, 119–127 (2005).Article 
CAS 

Google Scholar 
Barrett, R. D. H. & Schluter, D. Adaptation from standing genetic variation. Trends Ecol. Evol. 23, 38–44 (2008).Article 

Google Scholar 
Exposito-Alonso, M., Burbano, H. A., Bossdorf, O., Nielsen, R. & Weigel, D. Natural selection on the Arabidopsis thaliana genome in present and future climates. Nature 573, 126–129 (2019).Article 
CAS 

Google Scholar 
Han, G., Wang, W. & Dong, Y. Effects of balancing selection and microhabitat temperature variations on heat tolerance of the intertidal black mussel Septifer virgatus. Integr. Zool. 15, 416–427 (2020).Article 

Google Scholar 
Meester, L. D., Stoks, R. & Brans, K. I. Genetic adaptation as a biological buffer against climate change: Potential and limitations. Integr. Zool. 13, 372–391 (2018).Article 

Google Scholar 
Hoffmann, A. A. & Sgrò, C. M. Climate change and evolutionary adaptation. Nature 470, 479–485 (2011).Article 
CAS 

Google Scholar 
Günter, F. et al. Genotype-environment interactions rule the response of a widespread butterfly to temperature variation. J. Evol. Biol. 33, 920–929 (2020).Article 

Google Scholar 
Lowry, D. B. et al. QTL × environment interactions underlie adaptive divergence in switchgrass across a large latitudinal gradient. Proc. Natl Acad. Sci. USA 116, 12933–12941 (2019).Article 
CAS 

Google Scholar 
Bates, A. E. et al. Biologists ignore ocean weather at their peril. Nature 560, 299–301 (2018).Article 
CAS 

Google Scholar 
Plotkin, J. B. & Kudla, G. Synonymous but not the same: the causes and consequences of codon bias. Nat. Rev. Genet. 12, 32–42 (2011).Article 
CAS 

Google Scholar 
Zhao, F. et al. Genome-wide role of codon usage on transcription and identification of potential regulators. Proc. Natl Acad. Sci. USA 118, e2022590118 (2021).Hanson, G. & Coller, J. Codon optimality, bias and usage in translation and mRNA decay. Nat. Rev. Mol. Cell Bio. 19, 20–30 (2018).Article 
CAS 

Google Scholar 
Chen, S. et al. Codon-resolution analysis reveals a direct and context-dependent impact of individual synonymous mutations on mRNA level. Mol. Biol. Evol. 34, 2944–2958 (2017).Article 
CAS 

Google Scholar 
Wu, Z. et al. Expression level is a major modifier of the fitness landscape of a protein coding gene. Nat. Ecol. Evol. 6, 103–115 (2022).Article 

Google Scholar 
Lebeuf-Taylor, E., McCloskey, N., Bailey, S. F., Hinz, A. & Kassen, R. The distribution of fitness effects among synonymous mutations in a gene under directional selection. ELife. 8, e45952 (2019).Bailey, S. F., Hinz, A. & Kassen, R. Adaptive synonymous mutations in an experimentally evolved Pseudomonas fluorescens population. Nat. Commun. 5, 4076 (2014).Agashe, D. et al. Large-effect beneficial synonymous mutations mediate rapid and parallel adaptation in a bacterium. Mol. Biol. Evol. 33, 1542–1553 (2016).Article 
CAS 

Google Scholar 
Zhao, Y. et al. Synonymous mutation in growth regulating factor 15 of miR396a target sites enhances photosynthetic efficiency and heat tolerance in poplar. J. Exp. Bot. 72, 4502–4519 (2021).Article 
CAS 

Google Scholar 
Somero, G. N. The physiology of global change: linking patterns to mechanisms. Annu. Rev. Mar. Sci. 4, 39–61 (2012).Article 

Google Scholar 
Helmuth, B. et al. Climate change and latitudinal patterns of intertidal thermal stress. Science 298, 1015–1017 (2002).Article 
CAS 

Google Scholar 
Helmuth, B., Mieszkowska, N., Moore, P. & Hawkins, S. J. Living on the edge of two changing worlds: forecasting the responses of rocky intertidal ecosystems to climate change. Annu Rev. Ecol. Evol. Syst. 37, 373–404 (2006).Article 

Google Scholar 
Seabra, R., Wethey, D. S., Santos, A. M. & Lima, F. P. Side matters: microhabitat influence on intertidal heat stress over a large geographical scale. J. Exp. Mar. Biol. Ecol. 400, 200–208 (2011).Article 

Google Scholar 
Schmidt, P. S. & Rand, D. M. Intertidal microhabitat and selection at MPI: interlocus contrasts in the Northern Acorn Barnacle, Semibalanus balanoides. Evolution 53, 135 (1999).
Google Scholar 
Li, X., Tan, Y., Sun, Y., Wang, J. & Dong, Y. Microhabitat temperature variation combines with physiological variation to enhance thermal resilience of the intertidal mussel Mytilisepta virgata. Funct. Ecol. 35, 2497–2507 (2021).Article 
CAS 

Google Scholar 
Dong, Y. et al. Untangling the roles of microclimate, behaviour and physiological polymorphism in governing vulnerability of intertidal snails to heat stress. Proc. Royal. Soc. B. 284, (2017).Li, X. & Dong, Y. Living on the upper intertidal mudflat: different behavioral and physiological responses to high temperature between two sympatric Cerithidea snails with divergent habitat-use strategies. Mar. Environ. Res. 159, 105015 (2020).Article 
CAS 

Google Scholar 
Wang, J., Peng, X. & Dong, Y. High abundance and reproductive output of an intertidal limpet (Siphonaria japonica) in environments with high thermal predictability. Mar. Life. Sci. Tech. 2, 324–333 (2020).Article 

Google Scholar 
Dong, Y., Liao, M., Han, G. & Somero, G. N. An integrated, multi-level analysis of thermal effects on intertidal molluscs for understanding species distribution patterns. Biol. Rev. 97, 554–581 (2022).Article 

Google Scholar 
Georges, A., Gros, P. & Fodil, N. USP15: a review of its implication in immune and inflammatory processes and tumor progression. Genes Immun. 22, 12–23 (2021).Article 
CAS 

Google Scholar 
Vlasschaert, C., Xia, X., Coulombe, J. & Gray, D. A. Evolution of the highly networked deubiquitinating enzymes USP4, USP15, and USP11. BMC Evol. Biol. 15, 230 (2015).Mallard, F., Nolte, V., Tobler, R., Kapun, M. & Schlötterer, C. A simple genetic basis of adaptation to a novel thermal environment results in complex metabolic rewiring in Drosophila. Genome Biol. 19, 119 (2018).Cornelissen, T. et al. The deubiquitinase USP15 antagonizes Parkin-mediated mitochondrial ubiquitination and mitophagy. Hum. Mol. Genet. 23, 5227–5242 (2014).Article 
CAS 

Google Scholar 
Morton, B. The biology and functional morphology of Septifer bilocularis and Mytilisepta virgata (Bivalvia: Mytiloidea) from corals and the exposed rocky shores, respectively, of Hong Kong. Reg. Stud. Mar. Sci. 235, 485–500 (1995).
Google Scholar 
Boroda, A. V., Kipryushina, Y. O. & Odintsova, N. A. The effects of cold stress on Mytilus species in the natural environment. Cell Stress Chaperones 25, 821–832 (2020).Article 
CAS 

Google Scholar 
Thayer, C. W. Brachiopods versus mussels: competition, predation, and palatability. Science 228, 1527–1528 (1985).Article 
CAS 

Google Scholar 
Iorio, R., Celenza, G. & Petricca, S. Mitophagy: molecular mechanisms, new concepts on Parkin activation and the emerging role of AMPK/ULK1 Axis. Cells 11, 30 (2022).Article 
CAS 

Google Scholar 
Feidantsis, K. et al. Correlation between intermediary metabolism, Hsp gene expression, and oxidative stress-related proteins in long-term thermal-stressed Mytilus galloprovincialis. Am. J. Physiol. Regul. Integr. Comp. Physiol. 319, R264–R281 (2020).Article 
CAS 

Google Scholar 
Heise, K., Puntarulo, S., Portner, H. O. & Abele, D. Production of reactive oxygen species by isolated mitochondria of the Antarctic bivalve Laternula elliptica (King and Broderip) under heat stress. Comp. Biochem. Physiol. C. Toxicol. Pharmacol. 134, 79–90 (2003).Article 
CAS 

Google Scholar 
Abele, D., Heise, K., Portner, H. O. & Puntarulo, S. Temperature-dependence of mitochondrial function and production of reactive oxygen species in the intertidal mud clam Mya arenaria. J. Exp. Biol. 205, 1831–1841 (2002).Article 
CAS 

Google Scholar 
Xiao, Q. et al. Transcriptome analysis reveals the molecular mechanisms of heterosis on thermal resistance in hybrid abalone. BMC Genom. 22, 650 (2021).Li, L. et al. Heat stress induces apoptosis through a Ca2+-mediated mitochondrial apoptotic pathway in human umbilical vein endothelial cells. PLoS ONE 9, e111083 (2014).Article 

Google Scholar 
Gu, Z. T. et al. Heat stress induced apoptosis is triggered by transcription-independent p53, Ca2+ dyshomeostasis and the subsequent Bax mitochondrial translocation. Sci. Rep. 5, 11497 (2015).Article 
CAS 

Google Scholar 
Gerdol, M., De Moro, G., Venier, P. & Pallavicini, A. Analysis of synonymous codon usage patterns in sixty-four different bivalve species. Peer J. 3, e1520 (2015).Article 

Google Scholar 
Zhou, M. et al. Non-optimal codon usage affects expression, structure and function of clock protein FRQ. Nature 495, 111–115 (2013).Article 
CAS 

Google Scholar 
Yu, C. et al. Codon usage influences the local rate of translation elongation to tegulate co-translational protein folding. Mol. Cell 59, 744–754 (2015).Article 
CAS 

Google Scholar 
Spencer, P. S., Siller, E., Anderson, J. F. & Barral, J. M. Silent substitutions predictably alter translation elongation rates and protein folding efficiencies. J. Mol. Biol. 422, 328–335 (2012).Article 
CAS 

Google Scholar 
Pechmann, S., Chartron, J. W. & Frydman, J. Local slowdown of translation by nonoptimal codons promotes nascent-chain recognition by SRP in vivo. Nat. Struct. Mol. Biol. 21, 1100–1105 (2014).Article 
CAS 

Google Scholar 
Kimchi-Sarfaty, C. et al. A “silent” polymorphism in the MDR1 gene changes substrate specificity. Science 315, 525–528 (2007).Article 
CAS 

Google Scholar 
Zhou, Z. et al. Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc. Natl Acad. Sci. USA 113, E6117–E6125 (2016).Article 
CAS 

Google Scholar 
Shabalina, S. A., Spiridonov, N. A. & Kashina, A. Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Res. 41, 2073–2094 (2013).Article 
CAS 

Google Scholar 
Liao, M., Dong, Y. & Somero, G. N. Thermal adaptation of mRNA secondary structure: stability versus lability. Proc. Natl Acad. Sci. USA 118, e2113324118 (2021).Article 
CAS 

Google Scholar 
Wan, Y. et al. Genome-wide measurement of RNA folding energies. Mol. Cell. 48, 169–181 (2012).Article 

Google Scholar 
Seffens, W. & Digby, D. mRNAs have greater negative folding free energies than shuffled or codon choice randomized sequences. Nucleic Acids Res. 27, 1578–1584 (1999).Article 
CAS 

Google Scholar 
Faure, G., Ogurtsov, A. Y., Shabalina, S. A. & Koonin, E. V. Role of mRNA structure in the control of protein folding. Nucleic Acids Res. 44, 10898–10911 (2016).Article 
CAS 

Google Scholar 
Victor, M. P., Acharya, D., Begum, T. & Ghosh, T. C. The optimization of mRNA expression level by its intrinsic properties-Insights from codon usage pattern and structural stability of mRNA. Genomics 111, 1292–1297 (2019).Article 
CAS 

Google Scholar 
Backlund, M. & Kulozik, A. E. Differential analysis of the nuclear and the cytoplasmic RNA interactomes in living cells. Methods Mol. Biol. 2428, 291–304 (2022).Article 

Google Scholar 
Zaghlool, A. et al. Characterization of the nuclear and cytosolic transcriptomes in human brain tissue reveals new insights into the subcellular distribution of RNA transcripts. Sci. Rep. 11, 4076 (2021).Clark, M. S. et al. Life in the intertidal: cellular responses, methylation and epigenetics. Funct. Ecol. 32, 1982–1994 (2018).Article 

Google Scholar 
Jeremias, G. et al. Synthesizing the role of epigenetics in the response and adaptation of species to climate change in freshwater ecosystems. Mol. Ecol. 27, 2790–2806 (2018).Article 

Google Scholar 
Li, L. et al. Divergence and plasticity shape adaptive potential of the Pacific oyster. Nat. Ecol. Evol. 2, 1751–1760 (2018).Article 

Google Scholar 
Chu, D. & Wei, L. Nonsynonymous, synonymous and nonsense mutations in human cancer-related genes undergo stronger purifying selections than expectation. BMC Cancer. 19, 359 (2019).Lima, F. P. & Wethey, D. S. Robolimpets: measuring intertidal body temperatures using biomimetic loggers. Limol. Oceanogr. Methods 7, 347–353 (2009).Article 

Google Scholar 
Dong, Y. & Williams, G. A. Variations in cardiac performance and heat shock protein expression to thermal stress in two differently zoned limpets on a tropical rocky shore. Mar. Biol. 158, 1223–1231 (2011).Article 

Google Scholar 
Vito, M. Segmented: An R Package to fit regression models with broken-line relationships. R. N. 8, 20–25 (2008).
Google Scholar 
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2021).Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).Article 

Google Scholar 
Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).Article 

Google Scholar 
Danecek, P. et al. Twelve years of SAMtools and BCFtools. GigaScience. 10, giab008 (2021).Rochette, N. C., Rivera Colón, A. G. & Catchen, J. M. Stacks 2: analytical methods for paired-end sequencing improve RADseq-based population genomics. Mol. Ecol. 28, 4737–4754 (2019).Article 
CAS 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).Article 
CAS 

Google Scholar 
Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).Article 

Google Scholar 
Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).Article 
CAS 

Google Scholar 
Hao, Z. et al. RIdeogram: drawing SVG graphics to visualize and map genome-wide data on the idiograms. PeerJ Comput. Sci. 6, e251 (2020).Article 

Google Scholar 
Rozas, J. et al. DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302 (2017).Article 
CAS 

Google Scholar 
Letunic, I., Khedkar, S. & Bork, P. SMART: recent updates, new developments and status in 2020. Nucleic Acids Res. 49, D458–D460 (2021).Article 
CAS 

Google Scholar 
Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).Article 
CAS 

Google Scholar 
Suchard, M. A. et al. Bayesian phylogenetic and phylodynamic data integration using BEAST 1.10. Virus Evol. 4, vey016 (2018).Bailey, T. L., Johnson, J., Grant, C. E. & Noble, W. S. The MEME suite. Nucleic Acids Res. 43, W39–W49 (2015).Article 
CAS 

Google Scholar 
Mathews, D. H. et al. Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl Acad. Sci. USA 101, 7287–7292 (2004).Article 
CAS 

Google Scholar 
Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. J. Mol. Biol. 288, 911–940 (1999).Article 
CAS 

Google Scholar 
Moyen, N. E., Somero, G. N. & Denny, M. W. Mussels’ acclimatization to high, variable temperatures is lost slowly upon transfer to benign conditions. J. Exp. Biol. 223, Pt 13 (2020).
Google Scholar 
Havird, J. C. et al. Distinguishing between active plasticity due to thermal acclimation and passive plasticity due to Q10 effects: why methodology matters. Funct. Ecol. 34, 1015–1028 (2020).Article 

Google Scholar 
Panova, M. et al. DNA extraction protocols for whole-genome sequencing in marine organisms. Methods Mol. Biol. 1452, 13–44 (2016).Article 
CAS 

Google Scholar 
Bustin, S. A. et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 55, 611–622 (2009).Article 
CAS 

Google Scholar 
Gerdol, M. et al. The purplish bifurcate mussel Mytilisepta virgata gene expression atlas reveals a remarkable tissue functional specialization. BMC Genomics. 18, 590 (2017).Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method. Methods 25, 402–408 (2001).Article 
CAS 

Google Scholar  More

Cheung, W. W. L. et al. Shrinking of fishes exacerbates impacts of global ocean changes on marine ecosystems. Nat. Clim. Chang. 3, 254–258, https://doi.org/10.1038/nclimate1691 (2013).Article 
ADS 

Google Scholar 
Cheung, W. W. L., Watson, R. & Pauly, D. Signature of ocean warming in global fisheries catch. Nature 497, 365–368, https://doi.org/10.1038/nature12156 (2013).Article 
ADS 
CAS 

Google Scholar 
Hilborn, R. et al. Global status of groundfish stocks. Fish Fish. 00, 1–18, https://doi.org/10.1111/faf.12560 (2021).Article 

Google Scholar 
Aguzzi, J. et al. Challenges to the assessment of benthic populations and biodiversity as a result of rhythmic behaviour: video solutions from cabled observatories. Oceanography and Marine Biology: An Annual Review 50, 233–284 (2012).
Google Scholar 
Aguzzi, J. et al. Coastal observatories for monitoring of fish behaviour and their responses to environmental changes. Reviews in fish biology and fisheries 25, 463–483, https://doi.org/10.1007/s11160-015-9387-9 (2015).Article 

Google Scholar 
Doya, C. et al. Diel behavioral rhythms in sablefish (Anoplopoma fimbria) and other benthic species, as recorded by the Deep-sea cabled observatories in Barkley canyon (NEPTUNE-Canada). Journal of Marine Systems 130, 69–78, https://doi.org/10.1016/j.jmarsys.2013.04.003 (2014).Article 
ADS 

Google Scholar 
Aguzzi, J. et al. Ecological video monitoring of Marine Protected Areas by underwater cabled surveillance cameras. Marine Policy 119, 104052, https://doi.org/10.1016/j.marpol.2020.104052 (2020).Article 

Google Scholar 
Milligan, R. J. et al. Evidence for seasonal cycles in deep‐sea fish abundances: A great migration in the deep SE Atlantic? Journal of Animal Ecology 89, 1593–1603, https://doi.org/10.1111/1365-2656.13215 (2020).Article 

Google Scholar 
Hutchingson, G. E. Concluding remarks. Cold Spring Harbor Symp. 22, 415–427, https://doi.org/10.1101/SQB.1957.022.01.039 (1957).Article 

Google Scholar 
Hut, R. A., Kronfeld-Schor, N., Van Der Vinne, V. & De la Iglesia, H. In search of a temporal niche: environmental factors. Progress in brain research 199, 281–304, https://doi.org/10.1016/B978-0-444-59427-3.00017-4 (2012).Article 

Google Scholar 
Aguzzi, J. et al. The hierarchic treatment of marine ecological information from spatial networks of benthic platforms. Sensors 20, 1751, https://doi.org/10.3390/s20061751 (2020).Article 
ADS 

Google Scholar 
Danovaro, R. et al. A new international ecosystem-based strategy for the global deep ocean. Science 355, 452–454, https://doi.org/10.1126/science.aah7178 (2017).Article 
ADS 
CAS 

Google Scholar 
Aguzzi, J. et al. The potential of video imagery from worldwide cabled observatory networks to provide information supporting fish-stock and biodiversity assessment. ICES Journal of Marine Science 77, 2396–2410, https://doi.org/10.1093/icesjms/fsaa169 (2020).Article 

Google Scholar 
Aguzzi, J. et al. New high-tech flexible networks for the monitoring of deep-sea ecosystems. Environmental science and technology 53, 6616–6631, https://doi.org/10.1021/acs.est.9b00409 (2019).Article 
ADS 
CAS 

Google Scholar 
Rountree, R. A. et al. Towards an optimal design for ecosystem-level ocean observatories. In Oceanography and Marine Biology. Taylor and Francis, pp. 79–106 (2020).Aguzzi, J. et al. Developing technological synergies between deep-sea and space research. Elementa: Science of the Anthropocene 10, 00064, https://doi.org/10.1525/elementa.2021.00064 (2022).Article 

Google Scholar 
Aguzzi, J. et al. Multiparametric monitoring of fish activity rhythms in an Atlantic coastal cabled observatory. Journal of Marine Systems 212, 103424, https://doi.org/10.1016/j.jmarsys.2020.103424 (2020).Article 

Google Scholar 
Matabos et al. Expert, Crowd, Students or Algorithm: who holds the key to deep-sea imagery ‘big data’ processing? Methods in Ecology and Evolution 8, 996–1004, https://doi.org/10.1111/2041-210X.12746 (2017).Article 

Google Scholar 
Zuazo, A. et al. An automated pipeline for image processing and data treatment to track activity rhythms of Paragorgia arborea in relation to hydrographic conditions. Sensors 20, 6281, https://doi.org/10.3390/s20216281 (2020).Article 
ADS 

Google Scholar 
Dibattista, J. D. et al. Community-based citizen science projects can support the distributional monitoring of fishes. Aquatic Conservation: Marine and Freshwater Ecosystems 31, 3580–3593, https://doi.org/10.1002/aqc.3726 (2021).Article 

Google Scholar 
Malde, K., Handegard, N. O., Eikvil, L. & Salberg, A. B. Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science 77, 1274–1285, https://doi.org/10.1093/icesjms/fsz057 (2020).Article 

Google Scholar 
European Marine Board. Big Data in Marine Science. European Marine Broad Advencing Seas & Ocean Science. https://www.marineboard.eu/publications/big-data-marine-science (2020).Aguzzi, J. et al. The new SEAfloor OBservatory (OBSEA) for remote and long-term coastal ecosystem monitoring. Sensors-Basel 11, 5850–5872, https://doi.org/10.3390/s110605850 (2011).Article 
ADS 

Google Scholar 
Del Rio, J. et al. Obsea: a decadal balance for a cabled observatory deployment. IEEE Access 8, 33163–33177, https://doi.org/10.1109/ACCESS.2020.2973771 (2020).Article 

Google Scholar 
Condal, F. et al. Seasonal rhythm in a Mediterranean coastal fish community as monitored by a cabled observatory. Marine Biology 159, 2809–2817, https://doi.org/10.1007/s00227-012-2041-3 (2012).Article 

Google Scholar 
Naylor, E. Chronobiology of marine organisms (Cambridge University Press, 2010).Weis, J. S., Smith, G., Zhou, T., Santiago-Bass, C. & Weis, P. Effects of contaminants on behavior: biochemical mechanisms and ecological consequences: killifish from a contaminated site are slow to capture prey and escape predators; altered neurotransmitters and thyroid may be responsible for this behavior, which may produce population changes in the fish and their major prey, the grass shrimp. Bioscience 51, 209–217 https://doi.org/10.1641/0006-3568(2001)051[0209:EOCOBB]2.0.CO;2 (2001).Bellido, J. M. et al. Identifying essential fish habitat for small pelagic species in Spanish Mediterranean waters. In Essential Fish Habitat Mapping in the Mediterranean. Springer Netherlands, 171–184 https://doi.org/10.1007/978-1-4020-9141-4_13 (2008).Brander, K. Impacts of climate change on fisheries. Journal of Marine Systems 79, 389–402, https://doi.org/10.1016/j.jmarsys.2008.12.015 (2010).Article 
ADS 

Google Scholar 
Viehman, H. A. & Zydlewski, G. B. Multi-scale temporal patterns in fish presence in a high-velocity tidal channel. PLoS One 12, e0176405, https://doi.org/10.1371/journal.pone.0176405 (2017).Article 
CAS 

Google Scholar 
Van Der Walt, K. A., Porri, F., Potts, W. M., Duncan, M. I. & James, N. C. Thermal tolerance, safety margins and vulnerability of coastal species: Projected impact of climate change induced cold water variability in a temperate African region. Marine Environmental Research 169, 105346, https://doi.org/10.1016/j.marenvres.2021.105346 (2021).Article 
CAS 

Google Scholar 
Marini, S. et al. Tracking fish abundance by underwater image recognition. Scientific reports 8, 1–12, https://doi.org/10.1038/s41598-018-32089-8 (2018).Article 
ADS 
CAS 

Google Scholar 
Sbragaglia, V. et al. Annual rhythms of temporal niche partitioning in the Sparidae family are correlated to different environmental variables. Scientific reports 9, 1–11, https://doi.org/10.1038/s41598-018-37954-0 (2019).Article 
ADS 
CAS 

Google Scholar 
Francescangeli, M. et al. Long-Term Monitoring of Diel and Seasonal Rhythm of Dentex dentex at an Artificial Reef. Frontier in Marine Science 9, 1–17, https://doi.org/10.3389/fmars.2022.801033 (2022).Article 

Google Scholar 
Knausgård, K. M. et al. Temperate fish detection and classification: a deep learning based approach. Applied Intelligence 52, 6988–7001, https://doi.org/10.1007/s10489-020-02154-9 (2022).Article 

Google Scholar 
Wu, J. et al. Multi-Label Active Learning Algorithms for Image Classification: Overview and Future Promise. ACM Computing Surveys (CSUR) 53, 1–35, https://doi.org/10.1145/3379504 (2020).Article 

Google Scholar 
He J., Mao R., Shao Z. & Zhu F. Incremental Learning in Online Scenario. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13923–13932 https://doi.org/10.1109/CVPR42600.2020.01394 (2020).Zhou, D. W., Yang, Y., & Zhan, D. C. Learning to Classify with Incremental New Class. In IEEE Transactions on Neural Networks and Learning Systems https://doi.org/10.1109/TNNLS.2021.3104882 (2021).Hashmani, M. A., Jameel, S. M., Alhussain, H., Rehman, M. & Budiman, A. Accuracy performance degradation in image classification models due to concept drift. International Journal of Advanced Computer Science and Applications 10, 422–425, https://doi.org/10.14569/ijacsa.2019.0100552 (2019).Article 

Google Scholar 
Langenkämper, D., van Kevelaer, R., Purser, A. & Nattkemper, T. W. Gear-Induced Concept Drift in Marine Images and Its Effect on Deep Learning Classification. Front. Mar. Sci. 7, 506, https://doi.org/10.3389/fmars.2020.00506 (2020).Article 

Google Scholar 
Kloster, M., Langenkämper, D., Zurowietz, M., Beszteri, B. & Nattkemper, T. W. Deep learning-based diatom taxonomy on virtual slides. Scientific Reports 10, 1–13, https://doi.org/10.1038/s41598-020-71165-w (2020).Article 
CAS 

Google Scholar 
Ottaviani, E. et al. Assessing the image concept drift at the OBSEA coastal underwater cabled observatory. Frontiers in Marine Science 9, 1–13, https://doi.org/10.3389/fmars.2022.840088 (2022).Article 

Google Scholar 
Katija, K. et al. FathomNet: A global image database for enabling artificial intelligence in the ocean. Scientific reports 12, 1–14, https://doi.org/10.1038/s41598-022-19939-2 (2022).Article 
ADS 
CAS 

Google Scholar 
Kohavi, R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence 14, 1137–1145 (1995).
Google Scholar 
Tharwat, A. Classification assessment methods. Applied Computing and Informatics 17, 168–192, https://doi.org/10.1016/j.aci.2018.08.003 (2018).Article 

Google Scholar 
Qi, C., Diao, J. & Qiu, L. On estimating model in feature selection with cross-validation. IEEE Access 7, 33454–33463, https://doi.org/10.1109/ACCESS.2019.2892062 (2019).Article 

Google Scholar 
Lopez-Vazquez, V. et al. Video image enhancement and machine learning pipeline for underwater animal detection and classification at cabled observatories. Sensors 20, 726, https://doi.org/10.3390/s20030726 (2020).Article 
ADS 

Google Scholar 
Francescangeli, M. et al. Underwater camera photos with manual tagging of fish species at OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.pangaea.de/10.1594/PANGAEA.946149 (2022).Marini, S. Source code for: simoneMarinIsmar/Image-Tagging-tool: Image Tagging (v1.0). Zenodo https://doi.org/10.5281/zenodo.6566282 (2022).Froese, R. & Pauly, D. FishBase. www.fishbase.org (2019).Martinez Padro, E. et al. CTD data acquired at the OBSEA seafloor observatory from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.946015 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Vilanova i la Geltrú (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945911 (2022).Martinez Padro, E. et al. Meteorological data from a weather station at Sant Pere de Ribes (Catalonia, Spain) from 2013 to 2014. PANGAEA https://doi.org/10.1594/PANGAEA.945906 (2022).Redmon, J., Divvala, S., Girshick, R. & Farhadi, A. You Only Look Once: Unified, Real-Time Object Detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788 https://doi.org/10.1109/CVPR.2016.91 (2016).Marrable, D. et al. Accelerating species recognition and labelling of fish from underwater video with machine-assisted deep learning. Frontiers in Marine Science 9, 944582, https://doi.org/10.3389/fmars.2022.944582 (2022).Article 

Google Scholar 
Zabala, M., García-Rubies, A., & Corbera, J. Els peixos de les illes Medes i del litoral català: guia per observar-los al seu ambient (Centre d’Estudis Marins de Badalona, 1992).Corbera, J., Sabatés, A., & García-Rubies, A. Peces de mar de la península ibérica (Ed. Planeta, 1996).Mercader, L., Lloris, D., & Rucabado, J. Tots els peixos del mar Català: Diagnosis i claus d’identificació (Institut d’Estudis Catalans, 2001).Aguzzi, J. et al. Daily activity rhythms in temperate coastal fishes: insights from cabled observatory video monitoring. Marine Ecology Progress Series 486, 223–236, https://doi.org/10.3354/meps10399 (2013).Article 
ADS 

Google Scholar 
Campos‐Candela, A. et al. A camera‐based method for estimating absolute density in animals displaying home range behaviour. Journal of Animal Ecology 87, 825–837, https://doi.org/10.1111/1365-2656.12787 (2018).Article 

Google Scholar 
Jang, J. & Yoon, S. Feature concentration for supervised and semisupervised learning with unbalanced datasets in visual inspection. IEEE Transactions on Industrial Electronics 68, 7620–7630, https://doi.org/10.1109/TIE.2020.3003622 (2020).Article 

Google Scholar 
Zhang, J. et al. Adaptive Vertical Federated Learning on Unbalanced Features. IEEE Transactions on Parallel and Distributed Systems 33, 4006–4018, https://doi.org/10.1109/TPDS.2022.3178443 (2022).Article 

Google Scholar 
Lin, C. H., Lin, C. S., Chou, P. Y. & Hsu, C. C. An Efficient Data Augmentation Network for Out-of-Distribution Image Detection. IEEE Access 9, 35313–35323, https://doi.org/10.1109/ACCESS.2021.3062187 (2021).Article 

Google Scholar 
Lu, Y., Chen, D., Olaniyi, E. & Huang, Y. Generative adversarial networks (GANs) for image augmentation in agriculture: A systematic review. Computers and Electronics in Agriculture 200, 107208, https://doi.org/10.1016/j.compag.2022.107208 (2022).Article 

Google Scholar 
Waqas, N., Safie, S. I., Kadir, K. A., Khan, S. & Khel, M. H. K. DEEPFAKE Image Synthesis for Data Augmentation. IEEE Access 10, 80847–80857, https://doi.org/10.1109/ACCESS.2022.3193668 (2022).Article 

Google Scholar  More

Mcleod, E. et al. A blueprint for blue carbon: toward an improved understanding of the role of vegetated coastal habitats in sequestering CO 2. Front. Ecol. Environ. 9, 552–560 (2011).Article 

Google Scholar 
Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).
Google Scholar 
Lovelock, C. E. & Duarte, C. M. Dimensions of Blue Carbon and emerging perspectives. Biol. Lett. 15, 20180781 (2019).Article 

Google Scholar 
Macreadie, P. I. et al. Blue carbon as a natural climate solution. Nat Rev Earth Environ 2, 826–839 (2021).Al‐Haj, A. N. & Fulweiler, R. W. A synthesis of methane emissions from shallow vegetated coastal ecosystems. Glob. Chang. Biol. 26, 2988–3005 (2020).Article 
ADS 

Google Scholar 
Rosentreter, J. A. et al. Half of global methane emissions come from highly variable aquatic ecosystem sources. Nat. Geosci. https://doi.org/10.1038/s41561-021-00715-2 (2021).Bastviken, D., Tranvik, L. J., Downing, J. A., Crill, P. M. & Enrich-Prast, A. Freshwater methane emissions offset the continental carbon sink. Science (80-) 331, 50–50 (2011).Article 
ADS 
CAS 

Google Scholar 
Rosentreter, J. A., Maher, D. T., Erler, D. V., Murray, R. H. & Eyre, B. D. Methane emissions partially offset “blue carbon” burial in mangroves. Sci. Adv. 4, eaao4985 (2018).Article 
ADS 

Google Scholar 
Rosentreter, J. A., Al‐Haj, A. N., Fulweiler, R. W. & Williamson, P. Methane and nitrous oxide emissions complicate coastal blue carbon assessments. Glob. Biogeochem. Cycles 35, e2020GB006858 (2021).Duarte, C. M., Middelburg, J. J. & Caraco, N. Major role of marine vegetation on the oceanic carbon cycle. Biogeosciences 2, 1–8 (2005).Article 
ADS 
CAS 

Google Scholar 
Snelgrove, P. V. R. et al. Global carbon cycling on a heterogeneous seafloor. Trends Ecol. Evol. 33, 96–105 (2018).Article 

Google Scholar 
Ortega, A. et al. Important contribution of macroalgae to oceanic carbon sequestration. Nat. Geosci. 12, 748–754 (2019).Article 
ADS 
CAS 

Google Scholar 
Barnes, R. O. & Goldberg, E. D. Methane production and consumption in anoxic marine sediments. Geology 4, 297 (1976).Article 
ADS 
CAS 

Google Scholar 
Reeburgh, W. S. Rates of biogeochemical processes in anoxic sediments. Annu. Rev. Earth Planet. Sci. 11, 269–298 (1983).Article 
ADS 
CAS 

Google Scholar 
Wallenius, A. J., Dalcin Martins, P., Slomp, C. P. & Jetten, M. S. M. Anthropogenic and environmental constraints on the microbial methane cycle in coastal sediments. Front. Microbiol. 12, 631621 (2021).Tokoro, T. et al. Net uptake of atmospheric CO2 by coastal submerged aquatic vegetation. Glob. Chang. Biol. 20, 1873–1884 (2014).Article 
ADS 

Google Scholar 
Gallagher, J. B., Shelamoff, V. & Layton, C. Seaweed ecosystems may not mitigate CO2 emissions. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsac011 (2022).Oremland, R. S. & Taylor, B. F. Sulfate reduction and methanogenesis in marine sediments. Geochim. Cosmochim. Acta 42, 209–214 (1978).Article 
ADS 
CAS 

Google Scholar 
Egger, M., Riedinger, N., Mogollón, J. M. & Jørgensen, B. B. Global diffusive fluxes of methane in marine sediments. Nat. Geosci. 11, 421–425 (2018).Article 
ADS 
CAS 

Google Scholar 
Weber, T., Wiseman, N. A. & Kock, A. Global ocean methane emissions dominated by shallow coastal waters. Nat. Commun. 10, 1–10 (2019).Article 

Google Scholar 
Neubauer, S. C. & Megonigal, J. P. Moving beyond global warming potentials to quantify the climatic role of ecosystems. Ecosystems 18, 1000–1013 (2015).Article 

Google Scholar 
Neubauer, S. C. Global warming potential is not an ecosystem property. Ecosystems https://doi.org/10.1007/s10021-021-00631-x (2021).Howard, J., Hoyt, S., Isensee, K., Telszewski, M. & Pidgeon, E. Coastal blue carbon: methods for assessing carbon stocks and emissions factors in mangroves, tidal salt marshes, and seagrasses. 1–181 (2014). https://unesdoc.unesco.org/ark:/48223/pf0000372868.Berg, P., Huettel, M., Glud, R. N., Reimers, C. E. & Attard, K. M. Aquatic eddy covariance: the method and its contributions to defining oxygen and carbon fluxes in marine environments. Ann. Rev. Mar. Sci. 14, 431–455 (2022).Article 

Google Scholar 
Tokoro, T., Watanabe, K., Tada, K. & Kuwae, T. Air–water CO2 flux in shallow coastal waters: theory, methods, and empirical studies. in Blue Carbon in Shallow Coastal Ecosystems 153–184 (Springer Singapore, 2019).Saintilan, N., Rogers, K., Mazumder, D. & Woodroffe, C. Allochthonous and autochthonous contributions to carbon accumulation and carbon store in southeastern Australian coastal wetlands. Estuar. Coast. Shelf Sci. 128, 84–92 (2013).Article 
ADS 
CAS 

Google Scholar 
Ollivier, Q. R., Maher, D. T., Pitfield, C. & Macreadie, P. I. Net drawdown of greenhouse gases (CO2, CH4 and N2O) by a temperate australian seagrass meadow. Estuaries Coasts https://doi.org/10.1007/s12237-022-01068-8 (2022).Maher, D. T. et al. Novel use of cavity ring-down spectroscopy to investigate aquatic carbon cycling from microbial to ecosystem scales. Environ. Sci. Technol. 47, 12938–12945 (2013).Article 
ADS 
CAS 

Google Scholar 
Call, M. et al. Spatial and temporal variability of carbon dioxide and methane fluxes over semi-diurnal and spring–neap–spring timescales in a mangrove creek. Geochim. Cosmochim. Acta 150, 211–225 (2015).Article 
ADS 
CAS 

Google Scholar 
Maher, D. T., Cowley, K., Santos, I. R., Macklin, P. & Eyre, B. D. Methane and carbon dioxide dynamics in a subtropical estuary over a diel cycle: Insights from automated in situ radioactive and stable isotope measurements. Mar. Chem. 168, 69–79 (2015).Article 
CAS 

Google Scholar 
Attard, K. M. et al. Seasonal metabolism and carbon export potential of a key coastal habitat: The perennial canopy-forming macroalga Fucus vesiculosus. Limnol. Oceanogr. 64, 149–164 (2019).Article 
ADS 

Google Scholar 
Attard, K. M. et al. Seasonal ecosystem metabolism across shallow benthic habitats measured by aquatic eddy covariance. Limnol. Oceanogr. Lett. 4, 79–86 (2019).Article 

Google Scholar 
Trevathan-Tackett, S. M. et al. Comparison of marine macrophytes for their contributions to blue carbon sequestration. Ecology 96, 3043–3057 (2015).Article 

Google Scholar 
Pessarrodona, A. et al. Global seaweed productivity. Sci. Adv. 8, eabn2465 (2022).Machado, L., Magnusson, M., Paul, N. A., de Nys, R. & Tomkins, N. Effects of marine and freshwater macroalgae on in vitro total gas and methane production. PLoS ONE 9, e85289 (2014).Article 
ADS 

Google Scholar 
Hansson, G. Methane production from marine, green macro-algae. Resour. Conserv. 8, 185–194 (1983).Article 
CAS 

Google Scholar 
Björk, M., Rosenqvist, G., Gröndahl, F. & Bonaglia, S. Methane emissions from macrophyte beach wrack on Baltic seashores. Ambio 52, 171–181 (2023).Article 

Google Scholar 
Lundevall-Zara, M., Lundevall-Zara, E. & Brüchert, V. Sea-air exchange of methane in shallow inshore areas of the Baltic sea. Front. Mar. Sci. 8, 1–20 (2021).Article 

Google Scholar 
Yvon-Durocher, G. et al. Methane fluxes show consistent temperature dependence across microbial to ecosystem scales. Nature 507, 488–491 (2014).Article 
ADS 
CAS 

Google Scholar 
Roth, F. et al. High spatiotemporal variability of methane concentrations challenges estimates of emissions across vegetated coastal ecosystems. Glob. Chang. Biol. https://doi.org/10.1111/gcb.16177 (2022).Koweek, D. A. et al. A year in the life of a central California kelp forest: physical and biological insights into biogeochemical variability. Biogeosciences 14, 31–44 (2017).Article 
ADS 
CAS 

Google Scholar 
Watanabe, K. et al. Macroalgal metabolism and lateral carbon flows can create significant carbon sinks. Biogeosciences 17, 2425–2440 (2020).Article 
ADS 
CAS 

Google Scholar 
Möller, P., Pihl, L. & Rosenberg, R. Benthic faunal energy flow and biological interaction in some shallow marine soft bottom habitats. Mar. Ecol. Prog. Ser. 27, 109–121 (1985).Article 
ADS 

Google Scholar 
Frigstad, H. et al. Blue Carbon – Climate Adaptation, CO2 Uptake And Sequestration Of Carbon In Nordic Blue Forests – Results From The Nordic Blue Carbon Project. (Nordic Council of Ministers, 2021).Ikawa, H. & Oechel, W. C. Temporal variations in air-sea CO 2 exchange near large kelp beds near San Diego, California. J. Geophys. Res. Ocean. 120, 50–63 (2015).Article 
ADS 
CAS 

Google Scholar 
Reeburgh, W. S. Oceanic methane biogeochemistry. Chem. Rev. 107, 486–513 (2007).Article 
CAS 

Google Scholar 
Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).Article 
ADS 
CAS 

Google Scholar 
Oreska, M. P. J. et al. The greenhouse gas offset potential from seagrass restoration. Sci. Rep. 10, 1–15 (2020).Article 

Google Scholar 
Asplund, M. E. et al. Methane emissions from nordic seagrass meadow sediments. Front. Mar. Sci. 8, 811533 (2022).Article 

Google Scholar 
Schorn, S., Ahmerkamp, S., Bullock, E., Weber, M. & Lott, C. Diverse methylotrophic methanogenic archaea cause high methane emissions from seagrass meadows. Proc. Natl Acad. Sci. USA. https://doi.org/10.1073/pnas.2106628119/-/DCSupplemental.Published (2022).Koebsch, F., Glatzel, S. & Jurasinski, G. Vegetation controls methane emissions in a coastal brackish fen. Wetl. Ecol. Manag. 21, 323–337 (2013).Article 
CAS 

Google Scholar 
Sansone, F. J. & Martens, C. S. Methane production from acetate and associated methane fluxes from anoxic coastal sediments. Science (80-). 211, 707–709 (1981).Article 
ADS 
CAS 

Google Scholar 
Egger, M. et al. Rapid sediment accumulation results in high methane effluxes from coastal sediments. PLoS ONE 11, e0161609 (2016).Article 

Google Scholar 
Hamdan, L. J. & Wickland, K. P. Methane emissions from oceans, coasts, and freshwater habitats: New perspectives and feedbacks on climate. Limnol. Oceanogr. 61, S3–S12 (2016).Article 
ADS 

Google Scholar 
Cai, M. et al. Metatranscriptomics reveals different features of methanogenic archaea among global vegetated coastal ecosystems. Sci. Total Environ. 802, 149848 (2022).Article 
ADS 
CAS 

Google Scholar 
Evans, P. N. et al. Methane metabolism in the archaeal phylum Bathyarchaeota revealed by genome-centric metagenomics. Science (80-). 350, 434–438 (2015).Article 
ADS 
CAS 

Google Scholar 
Zhang, C.-J., Pan, J., Liu, Y., Duan, C.-H. & Li, M. Genomic and transcriptomic insights into methanogenesis potential of novel methanogens from mangrove sediments. Microbiome 8, 94 (2020).Article 
CAS 

Google Scholar 
Hilt, S., Grossart, H., McGinnis, D. F. & Keppler, F. Potential role of submerged macrophytes for oxic methane production in aquatic ecosystems. Limnol. Oceanogr. https://doi.org/10.1002/lno.12095 (2022).Söllinger, A. & Urich, T. Methylotrophic methanogens everywhere — physiology and ecology of novel players in global methane cycling. Biochem. Soc. Trans. 47, 1895–1907 (2019).Article 

Google Scholar 
Karl, D. M. et al. Aerobic production of methane in the sea. Nat. Geosci. 1, 473–478 (2008).Article 
ADS 
CAS 

Google Scholar 
McGenity, T. J. & Sorokin, D. Y. Handbook of Hydrocarbon and Lipid Microbiology. p. 665–680 (Springer, 2010).Murray, B. C., Pendleton, L., Jenkins, W. A. & Sifleet, S. Green Payments for Blue Carbon Economic Incentives for Protecting Threatened Coastal Habitats (Nicholas Institute for Environmental Policy Solutions, 2011).Kuwae, T., Watanabe, A., Yoshihara, S., Suehiro, F. & Sugimura, Y. Implementation of blue carbon offset crediting for seagrass meadows, macroalgal beds, and macroalgae farming in Japan. Mar. Policy 138, 104996 (2022).Article 

Google Scholar 
Medvedev, I. P., Rabinovich, A. B. & Kulikov, E. A. Tides in three enclosed basins: the Baltic, Black, and Caspian Seas. Front. Mar. Sci. 3, 46 (2016).Article 

Google Scholar 
Haugen, D. A. Workshop on Micrometeorology (American Meteorological Society, 1973).Weiss, R. F. Carbon dioxide in water and seawater: the solubility of a non-ideal gas. Mar. Chem. 2, 203–215 (1974).Article 
CAS 

Google Scholar 
Wiesenburg, D. A. & Guinasso, N. L. Equilibrium solubilities of methane, carbon monoxide, and hydrogen in water and sea water. J. Chem. Eng. Data 24, 356–360 (1979).Article 
CAS 

Google Scholar 
Wanninkhof, R. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Oceanogr. Methods 12, 351–362 (2014).Article 

Google Scholar 
Gülzow, W. et al. One year of continuous measurements constraining methane emissions from the Baltic Sea to the atmosphere using a ship of opportunity. Biogeosciences 10, 81–99 (2013).Article 
ADS 

Google Scholar 
Jähne, B. et al. On the parameters influencing air-water gas exchange. J. Geophys. Res. 92, 1937 (1987).Article 
ADS 

Google Scholar 
Bonaglia, S. et al. Meiofauna improve oxygenation and accelerate sulfide removal in the seasonally hypoxic seabed. Mar. Environ. Res. 159, 104968 (2020).Article 
CAS 

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

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).Article 

Google Scholar 
St John, J. SeqPrep. https://github.com/jstjohn/SeqPrep (2011).Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).Article 
CAS 

Google Scholar 
R Core Team. R: A Language And Environment For Statistical Computing (R Foundation for Statistical Computing, 2021).Andrews, S. FastQC: A Quality Control Tool For High Throughput Sequence Data (2010).Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).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 (2012).Article 

Google Scholar 
Robertson, C. E. et al. Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data. Bioinformatics 29, 3100–3101 (2013).Article 
CAS 

Google Scholar 
Coolen, M. J. L. et al. Evolution of the methane cycle in Ace Lake (Antarctica) during the Holocene: response of methanogens and methanotrophs to environmental change. Org. Geochem. 35, 1151–1167 (2004).Article 
CAS 

Google Scholar 
Wobbrock, J. O., Findlater, L., Gergle, D. & Higgins, J. J. The aligned rank transform for nonparametric factorial analyses using only anova procedures. in Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 143–146 (ACM, 2011).Hammer, Ø., Harper, D. & Ryan, P. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 1–9 (2001). More

Agostini, S. et al. Ocean acidification drives community shifts towards simplified non-calcified habitats in a subtropical–temperate transition zone. Sci. Rep. 8, 11354 (2018).Article 
PubMed 
PubMed Central 

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

Google Scholar 
Gilbert, B. & Levine, J. M. Ecological drift and the distribution of species diversity. Proc. Biol. Sci. 284, 20170507 (2017).PubMed 
PubMed Central 

Google Scholar 
White, E. P. et al. A comparison of the species–time relationship across ecosystems and taxonomic groups. Oikos 112, 185–195 (2006).Article 

Google Scholar 
Sax, D. F. & Gaines, S. D. Species invasions and extinction: the future of native biodiversity on islands. Proc. Natl Acad. Sci. USA 105, 11490–11497 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Thompson, J. N. Rapid evolution as an ecological process. Trends Ecol. Evol. 13, 329–332 (1998).Article 
CAS 
PubMed 

Google Scholar 
Post, D. M. & Palkovacs, E. P. Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play. Philos. Trans. R. Soc. Lond. B 364, 1629–1640 (2009).Article 

Google Scholar 
Reznick, D. N. & Travis, J. Experimental studies of evolution and eco-evo dynamics in guppies (Poecilia reticulata). Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110218-024926 (2019).Hendry, A. P. Eco-evolutionary Dynamics (Princeton Univ. Press, 2020).Schoener, T. W. The newest synthesis: understanding the interplay of evolutionary and ecological dynamics. Science 331, 426–429 (2011).Article 
CAS 
PubMed 

Google Scholar 
Yoshida, T., Jones, L. E., Ellner, S. P., Fussmann, G. F. & Hairston, N. G. Rapid evolution drives ecological dynamics in a predator–prey system. Nature 424, 303–306 (2003).Article 
CAS 
PubMed 

Google Scholar 
Hansen, S. K., Rainey, P. B., Haagensen, J. A. J. & Molin, S. Evolution of species interactions in a biofilm community. Nature 445, 533–536 (2007).Article 
CAS 
PubMed 

Google Scholar 
Hillesland, K. L. & Stahl, D. A. Rapid evolution of stability and productivity at the origin of a microbial mutualism. Proc. Natl Acad. Sci. USA 107, 2124–2129 (2010).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Turcotte, M. M., Reznick, D. N. & Hare, J. D. The impact of rapid evolution on population dynamics in the wild: experimental test of eco-evolutionary dynamics. Ecol. Lett. 14, 1084–1092 (2011).Article 
PubMed 

Google Scholar 
Lawrence, D. et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 10, e1001330 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Celiker, H. & Gore, J. Clustering in community structure across replicate ecosystems following a long-term bacterial evolution experiment. Nat. Commun. 5, 4643 (2014).Article 
CAS 
PubMed 

Google Scholar 
Andrade-Domínguez, A. et al. Eco-evolutionary feedbacks drive species interactions. ISME J. 8, 1041–1054 (2014).Article 
PubMed 

Google Scholar 
Reznick, D. Hard and soft selection revisited: how evolution by natural selection works in the real world. J. Hered. 107, 3–14 (2016).Article 
PubMed 

Google Scholar 
Matthews, B., Aebischer, T., Sullam, K. E., Lundsgaard-Hansen, B. & Seehausen, O. Experimental evidence of an eco-evolutionary feedback during adaptive divergence. Curr. Biol. 26, 483–489 (2016).Article 
CAS 
PubMed 

Google Scholar 
Harcombe, W. R., Chacón, J. M., Adamowicz, E. M., Chubiz, L. M. & Marx, C. J. Evolution of bidirectional costly mutualism from byproduct consumption. Proc. Natl Acad. Sci. USA 115, 12000–12004 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Preussger, D., Giri, S., Muhsal, L. K., Oña, L. & Kost, C. Reciprocal fitness feedbacks promote the evolution of mutualistic cooperation. Curr. Biol. https://doi.org/10.1016/j.cub.2020.06.100 (2020).Adamowicz, E. M., Muza, M., Chacón, J. M. & Harcombe, W. R. Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica. PLoS Pathog. 16, e1008700 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rodríguez-Verdugo, A. & Ackermann, M. Rapid evolution destabilizes species interactions in a fluctuating environment. ISME J. 15, 450–460 (2021).Article 
PubMed 

Google Scholar 
Barber, J. N. et al. The evolution of coexistence from competition in experimental co-cultures of Escherichia coli and Saccharomyces cerevisiae. ISME J. 15, 746–761 (2021).Article 
CAS 
PubMed 

Google Scholar 
Hart, S. F. M., Chen, C.-C. & Shou, W. Pleiotropic mutations can rapidly evolve to directly benefit self and cooperative partner despite unfavorable conditions. eLife 10, e57838 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Kokko, H. et al. Can evolution supply what ecology demands? Trends Ecol. Evol. 32, 187–197 (2017).Article 
PubMed 

Google Scholar 
Nuismer, S. Introduction to Coevolutionary Theory (Macmillan Learning, 2017).Stoltzfus, A. & McCandlish, D. M. Mutational biases influence parallel adaptation. Mol. Biol. Evol. 34, 2163–2172 (2017).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Payne, J. L. et al. Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis. PLoS Biol. 17, e3000265 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Storz, J. F. et al. The role of mutation bias in adaptive molecular evolution: insights from convergent changes in protein function. Philos. Trans. R. Soc. Lond. B 374, 20180238 (2019).Article 
CAS 

Google Scholar 
Gomez, K., Bertram, J. & Masel, J. Mutation bias can shape adaptation in large asexual populations experiencing clonal interference. Proc. Biol. Sci. 287, 20201503 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Venkataram, S., Monasky, R., Sikaroodi, S. H., Kryazhimskiy, S. & Kacar, B. Evolutionary stalling and a limit on the power of natural selection to improve a cellular module. Proc. Natl Acad. Sci. USA 117, 18582–18590 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hom, E. F. Y. & Murray, A. W. Niche engineering demonstrates a latent capacity for fungal–algal mutualism. Science 345, 94–98 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wolfe, B. E. & Dutton, R. J. Fermented foods as experimentally tractable microbial ecosystems. Cell 161, 49–55 (2015).Article 
CAS 
PubMed 

Google Scholar 
Chacón, J. M., Hammarlund, S. P., Martinson, J. N. V., Smith, L. B. & Harcombe, W. R. The ecology and evolution of model microbial mutualisms. Annu. Rev. Ecol. Evol. Syst. 52, 363–384 (2021).Article 

Google Scholar 
Blasche, S., Kim, Y., Oliveira, A. P. & Patil, K. R. Model microbial communities for ecosystems biology. Curr. Opin. Syst. Biol. 6, 51–57 (2017).Article 

Google Scholar 
Levy, S. F. et al. Quantitative evolutionary dynamics using high-resolution lineage tracking. Nature 519, 181–186 (2015).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Venkataram, S. et al. Development of a comprehensive genotype-to-fitness map of adaptation-driving mutations in yeast. Cell 166, 1585–1596.e22 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Jones, E. I., Bronstein, J. L. & Ferrière, R. The fundamental role of competition in the ecology and evolution of mutualisms. Ann. N. Y. Acad. Sci. 1256, 66–88 (2012).Article 
PubMed 

Google Scholar 
Boyer, S., Hérissant, L. & Sherlock, G. Adaptation is influenced by the complexity of environmental change during evolution in a dynamic environment. PLoS Genet. 17, e1009314 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Blundell, J. R. et al. The dynamics of adaptive genetic diversity during the early stages of clonal evolution. Nat. Ecol. Evol. 3, 293–301 (2019).Article 
PubMed 

Google Scholar 
Good, B. H., Rouzine, I. M., Balick, D. J., Hallatschek, O. & Desai, M. M. Distribution of fixed beneficial mutations and the rate of adaptation in asexual populations. Proc. Natl Acad. Sci. USA 109, 4950–4955 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Good, B. H., Martis, S. & Hallatschek, O. Adaptation limits ecological diversification and promotes ecological tinkering during the competition for substitutable resources. Proc. Natl Acad. Sci. USA 115, E10407–E10416 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zhu, Y. O., Siegal, M. L., Hall, D. W. & Petrov, D. A. Precise estimates of mutation rate and spectrum in yeast. Proc. Natl Acad. Sci. USA 111, E2310–E2318 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dunham, M. J. et al. Characteristic genome rearrangements in experimental evolution of Saccharomyces cerevisiae. Proc. Natl Acad. Sci. USA 99, 16144–16149 (2002).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yona, A. H. et al. Chromosomal duplication is a transient evolutionary solution to stress. Proc. Natl Acad. Sci. USA 109, 21010–21015 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sunshine, A. B. et al. The fitness consequences of aneuploidy are driven by condition-dependent gene effects. PLoS Biol. 13, e1002155 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Gerrish, P. J. & Lenski, R. E. The fate of competing beneficial mutations in an asexual population. Genetica 102-103, 127–144 (1998).Article 
CAS 
PubMed 

Google Scholar 
Desai, M. M. & Fisher, D. S. Beneficial mutation–selection balance and the effect of linkage on positive selection. Genetics 176, 1759–1798 (2007).Article 
PubMed 
PubMed Central 

Google Scholar 
Schiffels, S., Szöllosi, G. J., Mustonen, V. & Lässig, M. Emergent neutrality in adaptive asexual evolution. Genetics 189, 1361–1375 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Nguyen, Ba,A. N. et al. High-resolution lineage tracking reveals travelling wave of adaptation in laboratory yeast. Nature 575, 494–499 (2019).Article 

Google Scholar 
Foster, K. R., Shaulsky, G., Strassmann, J. E., Queller, D. C. & Thompson, C. R. L. Pleiotropy as a mechanism to stabilize cooperation. Nature 431, 693–696 (2004).Article 
CAS 
PubMed 

Google Scholar 
Sachs, J. L., Mueller, U. G., Wilcox, T. P. & Bull, J. J. The evolution of cooperation. Q. Rev. Biol. 79, 135–160 (2004).Article 
PubMed 

Google Scholar 
Harcombe, W. Novel cooperation experimentally evolved between species. Evolution 64, 2166–2172 (2010).PubMed 

Google Scholar 
Vasi, F., Travisano, M. & Lenski, R. E. Long-term experimental evolution in Escherichia coli. II. Changes in life-history traits during adaptation to a seasonal environment. Am. Nat. 144, 432–456 (1994).Article 

Google Scholar 
MacArthur, R. H. & Wilson, E. O. The Theory of Island Biogeography (Princeton Univ. Press, 2001).Reznick, D., Bryant, M. J. & Bashey, F. r- and K-selection revisited: the role of population regulation in life-history evolution. Ecology 83, 1509–1520 (2002).Article 

Google Scholar 
Mueller, L. D. & Ayala, F. J. Trade-off between r-selection and K-selection in Drosophila populations. Proc. Natl Acad. Sci. USA 78, 1303–1305 (1981).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Novak, M., Pfeiffer, T., Lenski, R. E., Sauer, U. & Bonhoeffer, S. Experimental tests for an evolutionary trade-off between growth rate and yield in E. coli. Am. Nat. 168, 242–251 (2006).Article 
PubMed 

Google Scholar 
Bachmann, H. et al. Availability of public goods shapes the evolution of competing metabolic strategies. Proc. Natl Acad. Sci. USA 110, 14302–14307 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Lipson, D. A. The complex relationship between microbial growth rate and yield and its implications for ecosystem processes. Front. Microbiol. 6, 615 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Orivel, J. et al. Trade-offs in an ant–plant–fungus mutualism. Proc. Biol. Sci. 284, 20161679 (2017).PubMed 
PubMed Central 

Google Scholar 
Fritts, R. K. et al. Enhanced nutrient uptake is sufficient to drive emergent cross-feeding between bacteria in a synthetic community. ISME J. 14, 2816–2828 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wortel, M. T., Noor, E., Ferris, M., Bruggeman, F. J. & Liebermeister, W. Metabolic enzyme cost explains variable trade-offs between microbial growth rate and yield. PLoS Comput. Biol. 14, e1006010 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Cheng, C. et al. Laboratory evolution reveals a two-dimensional rate-yield tradeoff in microbial metabolism. PLoS Comput. Biol. 15, e1007066 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Luckinbill, L. S. r and K selection in experimental populations of Escherichia coli. Science 202, 1201–1203 (1978).Article 
CAS 
PubMed 

Google Scholar 
Oxman, E., Alon, U. & Dekel, E. Defined order of evolutionary adaptations: experimental evidence. Evolution 62, 1547–1554 (2008).Article 
PubMed 

Google Scholar 
Jasmin, J.-N., Dillon, M. M. & Zeyl, C. The yield of experimental yeast populations declines during selection. Proc. Biol. Sci. 279, 4382–4388 (2012).PubMed 
PubMed Central 

Google Scholar 
Laan, L., Koschwanez, J. H. & Murray, A. W. Evolutionary adaptation after crippling cell polarization follows reproducible trajectories. eLife 4, e09638 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Blount, Z. D., Lenski, R. E. & Losos, J. B. Contingency and determinism in evolution: replaying life’s tape. Science 362, eaam5979 (2018).Article 
PubMed 

Google Scholar 
Fukami, T. Historical contingency in community assembly: integrating niches, species pools, and priority effects. Annu. Rev. Ecol. Evol. Syst. 46, 1–23 (2015).Article 

Google Scholar 
Rainey, P. B. & Travisano, M. Adaptive radiation in a heterogeneous environment. Nature 394, 69–72 (1998).Article 
CAS 
PubMed 

Google Scholar 
Meyer, J. R. et al. Repeatability and contingency in the evolution of a key innovation in phage lambda. Science 335, 428–432 (2012).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herron, M. D. & Doebeli, M. Parallel evolutionary dynamics of adaptive diversification in Escherichia coli. PLoS Biol. 11, e1001490 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Hillesland, K. L. et al. Erosion of functional independence early in the evolution of a microbial mutualism. Proc. Natl Acad. Sci. USA 111, 14822–14827 (2014).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Meroz, N., Tovi, N., Sorokin, Y. & Friedman, J. Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales. Nat. Commun. 12, 2891 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
MacLean, R. C. The tragedy of the commons in microbial populations: insights from theoretical, comparative and experimental studies. Heredity 100, 471–477 (2008).Article 
CAS 
PubMed 

Google Scholar 
Dunn, B. et al. Recurrent rearrangement during adaptive evolution in an interspecific yeast hybrid suggests a model for rapid introgression. PLoS Genet. 9, e1003366 (2013).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Barillot, E., Lacroix, B. & Cohen, D. Theoretical analysis of library screening using a N-dimensional pooling strategy. Nucleic Acids Res. 19, 6241–6247 (1991).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baym, M., Shaket, L., Anzai, I. A., Adesina, O. & Barstow, B. Rapid construction of a whole-genome transposon insertion collection for Shewanella oneidensis by Knockout Sudoku. Nat. Commun. 7, 13270 (2016).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Baym, M. et al. Inexpensive multiplexed library preparation for megabase-sized genomes. PLoS ONE 10, e0128036 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Venkataram, S., Kuo, H., Hom, E., Kryazhimskiy, S. Early adaptation in a microbial community is dominated by mutualism-enhancing mutations. Dryad https://doi.org/10.6076/D14K5X (2022). More

Qin, Y. et al. Improved estimates of forest cover and loss in the Brazilian Amazon in 2000–2017. Nat. Sustain. 2, 764–772 (2019).Article 

Google Scholar 
Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).Article 

Google Scholar 
Jenkins, C., Pimm, S. & Joppa, L. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).Article 
CAS 

Google Scholar 
Nogueira, E., Yanai, A., de Vasconcelos, S., de Alencastro, G. & Fearnside, P. Brazil’s Amazonian protected areas as a bulwark against regional climate change. Reg. Environ. Change 18, 573–579 (2018).Article 

Google Scholar 
Ochoa-Quintero, J., Gardner, T., Rosa, I., Ferraz, S. & Sutherland, W. Thresholds of species loss in Amazonian deforestation frontier landscapes. Conserv. Biol. 29, 440–451 (2015).Article 

Google Scholar 
Cabral, A., Saito, C., Pereira, H. & Laques, A. Deforestation pattern dynamics in protected areas of the Brazilian Legal Amazon using remote sensing data. Appl. Geogr. 100, 101–115 (2018).Article 

Google Scholar 
Nepstad, D. et al. Inhibition of Amazon deforestation and fire by parks and Indigenous lands. Conserv. Biol. 20, 65–73 (2006).Article 
CAS 

Google Scholar 
Ricketts, T. et al. Indigenous lands, protected areas, and slowing climate change. PLoS Biol. 8, e1000331 (2010).Article 

Google Scholar 
Herrera, D., Pfaff, A. & Robalino, J. Impacts of protected areas vary with the level of government: comparing avoided deforestation across agencies in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 116, 14916–14925 (2019).Article 
CAS 

Google Scholar 
Jusys, T. Changing patterns in deforestation avoidance by different protection types in the Brazilian Amazon. PLoS ONE 13, e0195900 (2018).Article 

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

Google Scholar 
Silva, C. et al. Benchmark maps of 33 years of secondary forest age for Brazil. Sci. Data 7, 269 (2020).Article 

Google Scholar 
Laurance, W. et al. The future of the Brazilian Amazon. Science 291, 438–439 (2001).Article 
CAS 

Google Scholar 
Laurance, W. et al. Development of the Brazilian Amazon. Response. Science 292, 1652–1654 (2001).
Google Scholar 
Silveira, J. Development of the Brazilian Amazon. Science 292, 1651–1654 (2001).Article 
CAS 

Google Scholar 
Kauano, É., Silva, J., Diniz, J. & Michalski, F. Do protected areas hamper economic development of the Amazon region? An analysis of the relationship between protected areas and the economic growth of Brazilian Amazon municipalities. Land Use Policy 92, 104473 (2020).Article 

Google Scholar 
Silveira, F., Ferreira, M., Perillo, L., Carmo, F. & Neves, F. Brazil’s protected areas under threat. Science 361, 459–459 (2018).Article 
CAS 

Google Scholar 
Begotti, R. & Peres, C. Brazil’s indigenous lands under threat. Science 363, 592–592 (2019).Article 

Google Scholar 
Fearnside, P. Deforestation of the Brazilian Amazon. Oxford Research Encyclopedias: Environmental Science (Oxford Univ. Press, 2017); https://doi.org/10.1093/acrefore/9780199389414.013.102Ferreira, J. et al. Brazil’s environmental leadership at risk. Science 346, 706–707 (2014).Article 
CAS 

Google Scholar 
Villén-Pérez, S., Anaya-Valenzuela, L., Conrado da Cruz, D. & Fearnside, P. Mining threatens isolated indigenous peoples in the Brazilian Amazon. Glob. Environ. Change 72, 102398 (2022).Article 

Google Scholar 
Tollefson, J. Illegal mining in the Amazon hits record high amid Indigenous protests. Nature 598, 15–16 (2021).Article 
CAS 

Google Scholar 
Silva, C. et al. The Brazilian Amazon deforestation rate in 2020 is the greatest of the decade. Nat. Ecol. Evol. 5, 144–145 (2021).Article 

Google Scholar 
Vale, M. et al. The COVID-19 pandemic as an opportunity to weaken environmental protection in Brazil. Biol. Conserv. 255, 108994 (2021).Article 

Google Scholar 
Charlier, P. & Varison, L. Is COVID-19 being used as a weapon against Indigenous Peoples in Brazil? Lancet 396, 1069–1070 (2020).Article 
CAS 

Google Scholar 
Davidson, E. et al. The Amazon basin in transition. Nature 481, 321–328 (2012).Article 
CAS 

Google Scholar 
Ferrante, L. & Fearnside, P. Brazil’s new president and ‘ruralists’ threaten Amazonia’s environment, traditional peoples and the global climate. Environ. Conserv. 46, 261–263 (2019).Article 

Google Scholar 
PRODES Legal Amazon Deforestation Monitoring System (INPE, 2020); http://www.obt.inpe.br/OBT/assuntos/programas/amazonia/prodesHansen, M. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).Article 
CAS 

Google Scholar 
Qin, Y. et al. Annual dynamics of forest areas in South America during 2007–2010 at 50 m spatial resolution. Remote Sens. Environ. 201, 73–87 (2017).Article 

Google Scholar 
Collection 6 of the Annual Land Use Land Cover Maps of Brazil (MapBiomas Project, accessed 10 July 2022); https://mapbiomas.org/enTree Cover Loss (Global Forest Watch, 2021); https://www.globalforestwatch.org/map/?modalMeta=tree_cover_lossFuller, C., Ondei, S., Brook, B. & Buettel, J. Protected-area planning in the Brazilian Amazon should prioritize additionality and permanence, not leakage mitigation. Biol. Conserv. 248, 108673 (2020).Article 

Google Scholar 
Nolte, C., Agrawal, A., Silvius, K. & Soares, B. Governance regime and location influence avoided deforestation success of protected areas in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 110, 4956–4961 (2013).Article 
CAS 

Google Scholar 
Tesfaw, A. et al. Land-use and land-cover change shape the sustainability and impacts of protected areas. Proc. Natl Acad. Sci. USA 115, 2084–2089 (2018).Article 
CAS 

Google Scholar 
OECD Environmental Performance Reviews: Brazil (OECD, 2015).Campos-Silva, J. et al. Sustainable-use protected areas catalyze enhanced livelihoods in rural Amazonia. Proc. Natl Acad. Sci. USA 118, e2105480118 (2021).Article 
CAS 

Google Scholar 
Fearnside, P., Nogueira, E. & Yanai, A. Maintaining carbon stocks in extractive reserves in Brazilian Amazonia. Desenvolv. Meio. Ambie. 48, 446–476 (2018).
Google Scholar 
Nelson, A. & Chomitz, K. Effectiveness of strict vs. multiple use protected areas in reducing tropical forest fires: a global analysis using matching methods. PLoS ONE 6, e22722 (2011).Article 
CAS 

Google Scholar 
BenYishay, A., Heuser, S., Runfola, D. & Trichler, R. Indigenous land rights and deforestation: evidence from the Brazilian Amazon. J. Environ. Econ. Manag. 86, 29–47 (2017).Article 

Google Scholar 
Bonilla-Mejía, L. & Higuera-Mendieta, I. Protected areas under weak institutions: evidence from Colombia. World Dev. 122, 585–596 (2019).Article 

Google Scholar 
Baragwanath, K. & Bayi, E. Collective property rights reduce deforestation in the Brazilian Amazon. Proc. Natl Acad. Sci. USA 117, 20495–20502 (2020).Article 
CAS 

Google Scholar 
Mangonnet, J., Kopas, J. & Urpelainen, J. Playing politics with environmental protection: the political economy of designating protected areas. J. Politics 84, 1453–1468 (2022).Article 

Google Scholar 
Nepstad, D. et al. Slowing Amazon deforestation through public policy and interventions in beef and soy supply chains. Science 344, 1118–1123 (2014).Article 
CAS 

Google Scholar 
Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).Article 
CAS 

Google Scholar 
West, T. & Fearnside, P. Brazil’s conservation reform and the reduction of deforestation in Amazonia. Land Use Policy 100, 105072 (2021).Article 

Google Scholar 
Soares-Filho, B. et al. Cracking Brazil’s forest code. Science 344, 363–364 (2014).Article 
CAS 

Google Scholar 
Ferrante, L. & Fearnside, P. Military forces and COVID-19 as smokescreens for Amazon destruction and violation of indigenous rights. J. Geogr. Soc. 151, 258–263 (2020).
Google Scholar 
Jiménez-Muñoz, J. et al. Record-breaking warming and extreme drought in the Amazon rainforest during the course of El Niño 2015–2016. Sci. Rep. 6, 33130 (2016).Article 

Google Scholar 
Ferrante, L. & Fearnside, P. The Amazon’s road to deforestation. Science 369, 634–634 (2020).Article 

Google Scholar 
Feng, X. et al. How deregulation, drought and increasing fire impact Amazonian biodiversity. Nature 597, 516–521 (2021).Article 
CAS 

Google Scholar 
Aragão, L. et al. 21st century drought-related fires counteract the decline of Amazon deforestation carbon emissions. Nat. Commun. 9, 536 (2018).Article 

Google Scholar 
Silva, J., Barbosa, L., Topf, J., Vieira, I. & Scarano, F. Minimum costs to conserve 80% of the Brazilian Amazon. Perspect. Ecol. Conserv. 20, 216–222 (2022).
Google Scholar 
Lovejoy, T. & Nobre, C. Amazon tipping point. Sci. Adv. 4, eaat2340 (2018).Article 

Google Scholar 
Xiao, X., Biradar, C., Czarnecki, C., Alabi, T. & Keller, M. A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia. Remote Sens. 1, 355–374 (2009).Article 

Google Scholar 
Natural Protected Areas and Indigenous Territories Maps in Brazil (RAISG, 2018); https://www.amazoniasocioambiental.org/en/ More

Restoration opportunity area and costsMangrove restoration programmes have a greater chance of being successful when implemented in areas where mangroves have previously grown15. These areas have either been subject to deforestation or degradation and may be under government management or private ownership. They are locations that have undergone forest conversion into other land uses, including aquaculture, crops or plantations and urban settlements. Land ownership status is an important factor to consider for determining the availability of land for mangrove restoration7. For example, a higher opportunity and priority would be given to unproductive aquaculture ponds located in the protected and production forest areas which are under government management or leasehold, rather than in areas with other land uses that may be under private ownership (Methods gives detailed forest land tenure classifications in Indonesia). Therefore, managing mangrove rehabilitation should consider factors that include land tenure status and land-cover type as well as biogeomorphology (for example, ensuring that the correct mangrove species are used in hydrologically suitable locations) across landscape scales.We calculated that ~193,367 ha of land may be feasible for implementation of mangrove rehabilitation programmes (Fig. 4). This conservative assessment suggests that the potential for restoration may be only 30% of the current mangrove rehabilitation area target (600,000 ha). Depending on the challenges and opportunities for each of the biogeomorphological categories of land use and the forest land status we considered (see Methods for detailed mapping methodology), we identified that 9% of the potential restorable area was categorized as being within the high opportunity scenario, 33% as medium and 58% as areas falling within the low opportunity scenario. Among these scenarios, ~75% of identified areas have non-protected forest status, implying a greater tenurial challenge to establishing a rehabilitation programme. We identified the five provinces that are among the top ranked of high potential for mangrove restoration in Indonesia, namely East Kalimantan (20% of national restoration potential area), North Kalimantan (20%), South Sumatra (12%), West Kalimantan (5%) and Riau provinces (5%) (Fig. 1c). All of these provinces, except South Sumatra, are among the areas already identified in the current mangrove rehabilitation programme by the BRGM as having high opportunity for rehabilitation4. At the subprovincial scale, we identified the top six regencies with restoration area opportunity >10,000 ha, namely Banyuasin, Bulungan, Tana Tidung, Paser, Berau and Nunukan (Supplementary Table 1). Mangroves across these regions were commonly deforested after 2010 and converted into aquaculture ponds despite being designated as protected forest areas (Supplementary Table 1).Fig. 4: The distribution of mangrove loss area (in hectares) between 2001 and 2020 in Indonesia.Also shown are mangrove loss proportions within different biogeomorphological typology, loss drivers (land-use types), forest land status and identified scenarios of restoration opportunity (low, medium and high).Full size imageConsidering that previous successful (85% survival rates) mangrove rehabilitation around the world has been achieved only at small landscape scales (10–400 ha) with costs varying between US$1,500 ha−1 and US$9,000 ha−1 (refs. 8,16), the large-scale mangrove rehabilitation ambition of Indonesia must be carefully planned. Rehabilitating ~200,000 ha of degraded mangroves will require between US$0.29 billion and US$1.74 billion. The 2021 annual government budget allocation for mangrove rehabilitation under BRGM alone is ~US$0.10 billion17, which is 66–94% lower than the estimated total required budget but with additional international investment18 there is potential for scalable mangrove rehabilitation success.Lessons learned from the past failuresIn Indonesia, unproductive aquaculture ponds have become targets for mangrove rehabilitation programmes (Supplementary Fig. 1). However, metrics of rehabilitation success in these settings reveal low survival rates of planted seedlings, highlighting an urgency to develop new strategies for mangrove rehabilitation and strategies to assess the effectiveness of ecosystem rehabilitation6. For example, a silviculture approach—nursery-based mangrove planting using Rhizophora species—has been adopted for mangrove restoration and management for a long time in Indonesia19. When seedlings are directly planted in unused ponds (Supplementary Fig. 1), dense monoculture plantations often form, which despite providing some ecosystem services (for example, carbon sequestration20) have limited biodiversity value21 and may be less resilient to stressors compared to a diverse assemblages of tree species22.Mangrove restoration projects have often suffered low success rates due to inadequate hydrological site assessments before revegetation23. For example, mangrove planting programmes initiated after the 2004 tsunami were focused on mono-species planting and on reporting the number of seedlings being planted in a given area24. These planting projects most often occurred on undisputed land, such as mudflats, which are inappropriate locations for long-term mangrove growth because of high inundation frequency, high water flow rates and hypersaline conditions that limit seedling establishment and survival24. Planting has also focused in mangrove areas where low canopy cover is observed. While some mangrove areas with low canopy cover may respond to plantings because they are degraded, many sites naturally support low canopy cover, reflecting suboptimal environmental conditions for growth of Rhizophora species, instead favouring growth of highly salt tolerant species such as Avicennia spp.24. Such failures in mangrove rehabilitation efforts, however, have been under-reported with more than 50% of rehabilitation studies not monitored over time (Supplementary Fig. 1).Alternative restoration approaches through repairing hydrology, including excavation and removal of pond walls and tidal gates, have also been introduced15, although this approach has been only practiced in Indonesia at limited scales, mostly in unused aquaculture ponds25. A comprehensive understanding of the opportunity for mangrove rehabilitation in Indonesia is largely unquantified. Additionally, with limited monitoring of mangrove rehabilitation projects, the effectiveness and functionality of mangrove rehabilitation in Indonesia remains largely unknown and therefore it remains challenging to assess rehabilitation effectiveness between approaches and locations in Indonesia. Yet such assessments provide important data to achieve the ambitious mangrove rehabilitation goals of Indonesia.Mangrove governance in IndonesiaMangrove conservation in Indonesia was formally adopted in 1990 (Extended Data Fig. 1 and Supplementary Table 2), when mangroves were designated as protected forests under Law 5/1990 and the Presidential Decree 32/1990. When the Asian tsunami hit Aceh province in 2004, the role of mangroves in wave attenuation and therefore minimizing disaster risks for coastal communities was recognized26. As a result, nearly 30,000 ha of damaged mangroves were rehabilitated to recover coastal resiliency through planting of nearly 24 million seedlings over 60 projects24. However, the success of these programmes was low due to a lack of planning, monitoring and critical supplemental actions24,27. Despite the failure of many mangrove rehabilitation projects post-tsunami, the implementation of the subsequent programmes have not fully adopted best-practice mangrove rehabilitation principles6,7,15,23. In 2007, similar approaches to mangrove rehabilitation and conservation were adopted at a larger, national scale under the Spatial Planning Law (Law 26/2007) and the Coastal Area and Small Islands Management Law (Law 27/2007).In 2012, the National Mangrove Management Strategy (STRANAS Mangrove) was first established and followed by the formalization of the National and Regional Mangrove Working Group whose task was to guide mangrove conservation and rehabilitation. Its main goal was to involve more stakeholders, including civil society organizations and subnational government bodies, in mangrove conservation and rehabilitation28. Until 2017, the technical regulation of strategy and performance indicators for mangrove management was implemented with targets set to rehabilitate 3.49 Mha of mangroves by 204529. In 2020, however, the Mangrove Working Group and its supporting regulations were abolished and the mangrove rehabilitation strategy was subsequently managed by BRGM4. This effectively removed the regional governments (subnational working groups) from decisions related to mangrove management and concentrated development of policy at the level of the national government. The new strategy includes a tenfold increase in the annual rehabilitation target (from 11,250 to ~120,000 ha yr−1) with an overall target of 600,000 ha to be achieved within a shorter timeline (2020–2024). Without clear planning and appropriate strategies, these ambitious targets may not be feasible. For example, the annual mangrove rehabilitation area reached between 2017 and 2020 was only 5,318 ha (50% of the target) despite 2.6 million seedlings being planted (Supplementary Table 3). Given the lessons from the previous mangrove rehabilitation and the emerging processes of mangrove governance, it is timely to set an achievable restoration framework with improved planning, evaluation and monitoring.Implication for international environmental agendasA successful mangrove rehabilitation programme can directly contribute to reducing poverty (SDG 1) and maintaining food security and livelihoods (SDG 2), thereby increasing the health and well-being of 74 million coastal people in Indonesia (see Supplementary Table 1 for total population of regions with restoration potential area >5 ha). Additionally, mangrove rehabilitation will directly contribute to other relevant SDGs, such as improving water quality (SDG 6), providing healthy coastal habitats for fish and other marine biodiversity (SDG 14), contributing to emissions reductions and improving coastal resilience from sea level rise (SDG 13) and sustainably managing and protecting terrestrial ecosystems (SDG 15). Mangrove rehabilitation contributions to SDG 1 and 2 are particularly relevant as the current rehabilitation programme is delivered as cash-for-works activities under the National Economic Recovery strategy (PEN) as part of the social welfare payments to alleviate economic impacts of the COVID-19 pandemic17. With the current annual mangrove rehabilitation budget of US$0.10 billion17, further implementation of scalable community-based mangrove restoration with technical support from subnational and non-government stakeholders could increase the benefits to local communities, if administered properly. Therefore, the large investments planned for coastal communities via a national mangrove restoration programme will not only contribute to the economy of coastal communities, potentially reducing poverty across 199 regencies but will also help in securing nearly 4% of the national greenhouse gas emissions reduction target from the land sector.Restoring 193,367 ha of mangroves in the next 5 years (2021–2025) may contribute to carbon sequestration of 22 ± 10 MtCO2e by 2030 (see Methods for detailed estimate calculation and assumptions). Moreover, stopping the current annual rates of mangrove loss of 7,436 ha yr−1 between 2021 and 2030 will reduce up to 58 ± 37 MtCO2e or 12% of the national land sector emissions reduction targets. Clearly, climate benefits from mangrove rehabilitation and conservation in Indonesia are substantial if rehabilitation and conservation can be implemented appropriately and large annual rehabilitation targets are achieved. Indonesia has submitted its updated Nationally Determined Contributions (NDCs) to the United Nations Framework Convention on Climate Change, within which integrated management and rehabilitation of mangroves is a component of the actions to enhance the resilience of coastal ecosystems30. Further ecological aquaculture practices such as silvofisheries which are commonly applied in Indonesia31,32 may provide promising potential for climate change mitigation through mangrove biomass enhancement. With the increased potential for international investment to support mangrove rehabilitation in Indonesia, there is an opportunity for Indonesia to take the lead and show the world how mangrove conservation and rehabilitation can contribute to multiple international environmental agendas.In the past three decades, the governance of mangrove conservation and rehabilitation in Indonesia has been highly variable in approach (Extended Data Fig. 1). The current approach is top-down4 which has risks and may be ineffective at achieving landscape-scale increases in mangrove extent, as was demonstrated post-tsunami24,29. This top-down approach set by national-level agencies, which are responsible for achieving rehabilitation targets, has limited involvement (or investment) by subnational governments. While we have identified key factors that determine land available for mangrove rehabilitation, the success of mangrove rehabilitation is not necessarily assured because of the limited involvement of subnational mangrove working groups. A current ‘one size fits all’ strategy of the national government may not be appropriate to achieve successful mangrove rehabilitation and thus more flexible, localized approaches may increase the likelihood of success. More

Patterson, J. et al. Nat. Sustain. 4, 841–850 (2021).Article 

Google Scholar 
Reid, A. J. et al. Biol. Rev. 94, 849–873 (2019).Article 

Google Scholar 
Vollmer, D. & Harrison, I. J. Environ. Res. Lett. 16, 011005 (2021).Article 

Google Scholar 
Zeitoun, M. et al. Glob. Environ. Change 39, 143–154 (2016).Article 

Google Scholar 
Bezerra, M. O. et al. Environ. Manage. 69, 815–834 (2022).Article 

Google Scholar 
Souter, N. J. et al. Water 12, 788 (2020).Article 
CAS 

Google Scholar 
Akhmouch, A., Clavreul, D. & Glas, P. Water Int. 43, 5–12 (2018).Article 

Google Scholar 
Andersson, E. Ambio 51, 1–8 (2022).Article 

Google Scholar 
Huntington, H. P. et al. Nat. Sustain. 4, 672–679 (2021).Article 

Google Scholar 
Soames Job, R. F. Am. J. Public Health 78, 163–167 (1988).Article 
CAS 

Google Scholar 
Poff, N. L. et al. Nat. Clim. Change 6, 25–34 (2016).Article 

Google Scholar 
Diaz-Kope, L. & Miller-Stevens, K. Public Works Management and Policy 20, 29–48 (2015).Article 

Google Scholar 
OECD Financing a Water Secure Future (OECD Publishing, 2022).Cardascia, S. Financing Water Infrastructure and Landscape Approaches in Asia and the Pacific. Background Paper for 5th Roundtable on Financing Water (OECD Publishing, 2019).Schlager, E. & Blomquist, W. Embracing Watershed Politics (University Press of Colorado, 2008).Wehn, U., Collins, K., Anema, K., Basco-Carrera, L. & Lerebours, A. Water Int. 43, 34–59 (2018).Article 

Google Scholar 
Shaad, K., Souter, N. J., Vollmer, D., Regan, H. M. & Bezerra, M. O. Environ. Manage. 69, 752–767 (2022).Article 

Google Scholar  More

Plant species and pollinatorsMedicago sativa L. (Fabaceae), also called alfalfa or lucerne, is a perennial legume with flowers arranged in a cluster or raceme. It is a self-compatible plant with fairly high outcrossing rate (5.3–30%)46, and it requires insect visits for seed production47. No plant material was collected for this study. Honey bees, Apis mellifera, and alfalfa leafcutting bees, Megachile rotundata, are used as managed pollinators in alfalfa seed-production fields in the USA while bumble bees are commonly used in alfalfa breeding47.Experimental design and pollinator observationsFive 11 m × 11 m patches of M. sativa plants were set up in an east–west linear arrangement at the West Madison Agricultural Research Station in Madison, Wisconsin, USA. Within each patch, we transplanted 169 young plants grown from seeds in the greenhouse, each placed 90 cm apart. These plants grew and, at flowering, a plant had an average of 30.65 ± 16.4 stems per plant, with 4.93 ± 3.41 racemes per stem, and 7.53 ± 2.44 open flowers per raceme.A honey bee hive was placed approximately 100 m from the patches and a bumble bee hive was set up at the center of the southern edge of the patches. For leafcutting bees, a 60 × 30 × 7.6 cm bee board was set up in each of two boxes placed 1/3 and 2/3 along the southern edge of the patches and a half gallon of bees was released at periodic intervals throughout the alfalfa flowering season.Over two consecutive summers, observers followed bees foraging in the alfalfa patches, marked each raceme visited in succession within a foraging bout with a numbered clip, and recorded the number of flowers visited per raceme. After a bee had left a patch, observers went back to the marked racemes and measured the distance and direction traveled between consecutive racemes. Directions were recorded as one of the cardinal directions: North (N), South (S), East (E) or West (W), or inter-cardinal directions: Northeast (NE), Southeast (SE), Northwest (NW) and Southwest (SW). The frequency distributions of distances and directions traveled between two successive racemes are presented for each bee species each year in Figs. 1 (distances) and 2 (directions). The low pollinator abundance permitted observers to follow individual bees foraging in a patch. Little interference among bee species was observed in the patches.Figure 1Frequency distributions for distances traveled between consecutive racemes (cm) for each bee species each year.Full size imageFigure 2Frequency distributions of directions traveled between consecutive racemes for each bee species each year.Full size imageModel for the distance traveled between consecutive racemesWe first determined whether a statistical model best described the distance traveled between consecutive racemes (Modeled Distance), and examined whether the model differed among bee species. We used mixed effect linear models (proc Mixed in SAS 9.3)48 to identify the model that best described the distance traveled by pollinators between consecutive racemes. The model included loge distance as a linear function of loge flower number and bee species as fixed effects. The distance traveled between consecutive racemes and the number of flowers visited per raceme were log transformed prior to analyses in order to improve the models’ residuals. In addition, we included patch and foraging bout as random effects in the model. A foraging bout includes the racemes visited in succession from the time a bee is spotted in a patch to the time it leaves that patch. We used foraging bout instead of individual bee as the random effect because bees were not individually marked in this study. Moreover, to take into consideration the potential correlation between successive observations within a foraging bout, we added clip to the model. Clip 1 represents the first and second racemes visited in the foraging bout; clip 2, the second and third, and so on. Clip was added to the model either as a random effect or as a repeated measure with an AR(1) structure. The combination of random clip and random foraging bout creates a model that is sometimes called the “compound symmetry” model. The AR(1) structure represents correlations that decline exponentially as the gap between measurements increases such that measurements closer together in time are more strongly correlated than measurements further apart. Because we expected bees to visit flowers at close proximity when resources are abundant, we chose this correlation structure as a good potential descriptor of the way distances might be correlated within foraging bouts. We started with a full model which included loge flower number, bee species, patch, foraging bout, and clip either as a random effect or as a repeated measure with an AR(1) structure. We then removed variables and compared models by inspecting AIC values and the p values for each term in the model. We considered both low AIC and statistically significant (p  More