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    Image dataset for benchmarking automated fish detection and classification algorithms

    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

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    Genetic diversity and structure in wild Robusta coffee (Coffea canephora A. Froehner) populations in Yangambi (DR Congo) and their relation to forest disturbance

    Aguilar R, Cristóbal-Pérez ED, Balvino-Olvera FJ, Aguilar-Aguilar MDJ, Aguirre-Acosta N, Ashworth L et al. (2019) Habitat fragmentation reduces plant progeny quality: a global synthesis. Ecol Lett 22:1163–1173Article 

    Google Scholar 
    Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqcBarlow J, Lennow GD, Ferreira J, Berenguer E, Lees AC, Nally RM et al. (2016) Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535:144–147Article 
    CAS 

    Google Scholar 
    Barret SC, Eckert CG (1990) Current issues in plant reproductive ecology. Isr J Plant Sci 39:5–12
    Google Scholar 
    Bawa KS, Bullock SH, Perry DR, Coville RE, Grayum MH (1985) Reproductive biology of tropical lowland rain forest trees II. Pollination systems. Am J Bot 72:346–356Article 

    Google Scholar 
    Bello C, Galetti M, Pizo MA, Magnago LFS, Roch MF, Lima RA, et al. (2015) Defaunation affects carbon storage in tropical forests. Sci Adv 1:e1501105. https://doi.org/10.1126/sciadv.1501105Blouin MS (2003) DNA-based methods for pedigree reconstruction and kinship analysis in natural populations. Trends Ecol Evol 18:503–511Article 

    Google Scholar 
    Born C, Kjellberg F, Chevallier M-H, Vignes H, Dikangadissi J-T, Sanguié J et al. (2008) Colonization processes and the maintenance of genetic diversity: insight from a pioneer rainforest tree, Aucoumea Klaineana. Proc R Soc B 275:2171–2179Article 

    Google Scholar 
    Braun M, Dantas L, Esposito T, Pedrosa-Harand A (2020) Strong genetic differentiation on a small geographic scale in the Neotropical rainforest understory tree Paypayrola blanchetiana (Violaceae). Tree Genet Genomes. https://doi.org/10.1007/s11295-020-01477-5Campbell AJ, Carvalheiro LG, Maués MM, Jaffé R, Giannini TC, Freitas MAB et al. (2018) Anthropogenic disturbance of tropical forests threatens pollination services to açai palm in the Amazon river delta. J Appl Ecol 55:1725–1736Article 

    Google Scholar 
    Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience https://doi.org/10.1186/s13742-015-0047-8Chiriboga-Arroyo F, Jansen M, Bardales-Lozano R, Ismail SA, Thomas E, Garcia M et al. (2021) Genetic threats to the Forest Giants of the Amazon: Habitat degradation effects on the socio-economically important Brazil nut tree (Bertholletia excelsa). Plants People Planet 3:194–210Article 

    Google Scholar 
    Cramer PJS, Wellman FL (1957) Review of literature of coffee research in Indonesia. SIC Editorial, Inter-American Institute of Agricultural SciencesCraparo ACW, Van Asten PJ, Läderach P, Jassogne LT, Grab SW (2015) Coffea arabica yields decline in Tanzania due to climate change: Global implications. Agric Meteorol 207:1–10Article 

    Google Scholar 
    Cubry P, De Bellis F, Pot D, Musoli P, Leroy P (2013) Global analysis of Coffea canephora Pierre ex Froehner (Rubiaceae) from the Guineo-Congolese region reveals impacts from climatic refuges and migration effects. Genet Resour Crop Evol 60:483–501Article 

    Google Scholar 
    Curtis PG, Slay CM, Harris NL, Tyukavina A, Hansen MC (2018) Classifying drivers of global forest loss. Science 361:1108–1111Article 
    CAS 

    Google Scholar 
    Da Silva JMC, Tabarelli M (2000) Tree species impoverishment and the future flora of the Atlantic forest of northeast Brazil. Nature 404:72–74Article 

    Google Scholar 
    Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158Article 
    CAS 

    Google Scholar 
    Davis AP, Gole TW, Baena S, Moat J (2012) The impact of climate change on indigenous arabica coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS One. https://doi.org/10.1371/journal.pone.0047981Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R, Pietrella M et al. (2014) The coffee genome provides insight into the convergent evolution of caffeine biosynthesis. Science 345:1181–1184Article 
    CAS 

    Google Scholar 
    Depecker J, Asimonyio JA, Miteho R, Hatangi Y, Kambale J-L, Verleysen L, et al. (2022) The association between rainforest disturbance and recovery, tree community composition, and community traits in the Yangambi area in the Democratic Republic of the Congo. J Trop Ecol. https://doi.org/10.1017/S0266467422000347Dick CW, Etchelecu G, Austerlitz F (2003) Pollen dispersal of tropical trees (Dinizia excelsa: Fabaceae) by native insects and African honeybees in pristine and fragmented Amazonian rainforest. Mol Ecol 12:753–764Article 

    Google Scholar 
    Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bull 19:11–15
    Google Scholar 
    Edwards DP, Socolar JB, Mills SC, Burivalova Z, Koh LP, Wilcove DS (2019) Conservation of tropical forests in the Anthropocene. Curr Biol 29:R1008–R1020Article 
    CAS 

    Google Scholar 
    El Mousadik A, Petit RJ (1996) High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor Appl Genet 92:832–839Article 
    CAS 

    Google Scholar 
    Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES et al. (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. https://doi.org/10.1371/journal.pone.0019379Ernst C, Mayaux P, Verhegghen A, Bodart C, Christophe M, Defourny P (2013) National forest cover change in Congo Basin: deforestation, reforestation, degradation and regeneration for the years 1990, 2000 and 2005. Glob Chang Biol 19:1173–1187Article 

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

    Google Scholar 
    FAO, UNEP (2020) The State of the World’s Forests 2020. In Forests, bio-diversity and people. FAO and UNEPFerrão RG, da Fonseca AFA, Ferrão MAG, De Mune LH (2019) Conilon Coffee: the Coffea canephora produced in Brazil. Incaper, Vitória-ES, Brasil
    Google Scholar 
    Gardner TA, Barlow J, Chazdon R, Ewers RM, Harvey CA, Peres CA et al. (2009) Prospects for tropical forest biodiversity in a human‐modified world. Ecol Lett 12:561–582Article 

    Google Scholar 
    García-Fernández C, Sánchez JA, Blanco G (2018) SNP-haplotypes: An accurate approach for parentage and relatedness inference in gilthead sea bream (Sparus aurata). Aquaculture 495:582–591Article 

    Google Scholar 
    Gomez C, Dussert S, Hamon P, Hamon S, De Kochko A, Poncert V (2009) Current genetic differentiation of Coffea canephora pierre ex a. Froehn in the guineo-Congolian african zone: Cumulative impact of ancient climatic changes and recent human activities. BMC Evol Biol 9:167Article 

    Google Scholar 
    Goudet J (2013) hierfstat: estimation and tests of hierarchical F-statistics. R Package version 0:04–10. http://CRAN.R-project.org/package=hierfstatHubbell SP, Foster RB (1986) Biology, chance and history and the structure of tropical rain forest tree communities. In: Diamond JM, Case TJ (eds) Community ecology. Harper and Row, New York, NY, p 314–329
    Google Scholar 
    ICO (2022) Coffee Market Report: August 2022. Donwloaded from International Coffee Organization https://www.ico.org/documents/cy2021-22/cmr-0822-e.pdfIsmail SA, Ghazoul J, Ravikanth G, Kushalappa CG, Uma Shaanker R, Kettle CJ (2017) Evaluating realized seed dispersal across fragmented tropical landscapes: A two‐fold approach using parentage analysis and the neighbourhood model. N Phytol 214:1307–1316Article 
    CAS 

    Google Scholar 
    Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405Article 
    CAS 

    Google Scholar 
    Jombart T, Collins C (2015) Analysing genome-wide SNP data using adegenet 2.0.0. https://adegenet.r-forge.r-project.org/files/tutorial-genomics.pdfJones AG, Small CM, Paczolt KA, Ratterman NL (2010) A practical guide to methods of parentage analysis. Mol Ecol Resour 10:6–30Article 

    Google Scholar 
    Jones OR, Wang J (2012) A comparison of four methods for detecting weak genetic structures from maker data. Ecol Evol 2:1048–1055Article 

    Google Scholar 
    Kalinowski ST, Wagner AP, Taper ML (2006) ML-Relate: a computer program for maximum likelihood estimation of relatedness and relationship. Mol Ecol Notes 6:576–579Article 
    CAS 

    Google Scholar 
    Kearsley E, Verbeeck H, Hufkens K, Van, de Perre F, doetterl S, Baert G et al. (2017) Functional community structure of African monodominant Gilbertiodendron dewevrei forest influenced by local environmental filtering. Ecol Evol 7:295–304Article 

    Google Scholar 
    Kier G, Mutke J, Dinerstein E, Ricketss TH, Küper W, Kreft H et al. (2005) Global patterns of plant diversity and floristic knowledge. J Biogeogr 32:1107–1116Article 

    Google Scholar 
    Kiwuka C, Goudsmit E, Tournebize R, Oliveir de Aquino S, Douma JC, Bellanger L et al. (2021) Genetic diversity of native and cultivated Ugandan Robusta coffee (Coffea canephora Pierre ex A. Froehner): Climate influences, breeding potential and diversity conservation. PLoS One 16:e0245965Article 
    CAS 

    Google Scholar 
    Kreft H, Jetz W (2007) Global patterns and determinants of vascular plant diversity. Proc Natl Acad Sci USA 104:5925–5930Article 
    CAS 

    Google Scholar 
    Lachenaud P, Zhang D (2008) Genetic diversity and population structure in wild stands of cacao trees (Theobroma cacao L.) in French Guiana. Ann For Sci. https://doi.org/10.1051/forest:2008011Lashermes P, Combes MC, Ribas A, Cenci A, Mahé L, Etienne H (2010) Genetic and physical mapping of the SH3 region that confers resistance to leaf rust in coffee tree (Coffea arabica L.). Tree Genet Genomes 6:973–980Article 

    Google Scholar 
    Leroy T, Marraccini P, Dufour M, Montagnon C, Lashermes P, Sabau X et al. (2005) Construction and characterization of a Coffea canephora BAC library to study the organization of sucrose biosynthesis genes. Theor Appl Genet 111:1031–1041Article 

    Google Scholar 
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 14:2078–2079Article 

    Google Scholar 
    Li YL, Liu JX (2018) StructureSelector: A web‐based software to select and visualize the optimal number of clusters using multiple methods. Mol Ecol Resour 18:176–177Article 

    Google Scholar 
    Makelele IA, Verheyen K, Boeckx P, Ntaboba LC, Bazirake BM, Ewango C et al. (2021) Afrotropical secondary forests exhibit fast diversity and functional recovery, but slow compositional and carbon recovery after shifting cultivation. J Veg Sci 32:1–13Article 

    Google Scholar 
    Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10–12Article 

    Google Scholar 
    Mateu-Andrés I, De Paco L (2006) Genetic diversity and the reproductive system in related species of Antirrhinum. Ann Bot 98:1053–1060Article 

    Google Scholar 
    Mayr E (1954) Change of genetic environment and evolution. In: Huxley A, Hardy AC, Ford EB (eds) Evolution as a process. Allen and Unwin, London, p 157–180
    Google Scholar 
    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A et al. (2010) The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303Article 
    CAS 

    Google Scholar 
    Merot-L’anthoene V, Tournebize R, Darracq O, Rattina V, Lepelley M, Bellanger L et al. (2019) Development and evaluation of a genome-wide Coffee 8.5K SNP array and its application for high-density genetic mapping and for investigating the origin of Coffea arabica L. Plant Biotechnol J 17:1418–1430Article 

    Google Scholar 
    Musoli P, Cubry P, Aluka P, Billot C, Dufour M, De Bellis F et al. (2009) Genetic differentiation of wild and cultivated populations: diversity of Coffea canephora Pierre in Uganda. Genome 52:634–646Article 
    CAS 

    Google Scholar 
    Neushulz EL, Mueller T, Schleuning M, Böhning-Gaese K (2016) Pollination and seed dispersal are the most threatened processes of plant regeneration. Sci Rep 6:1–6
    Google Scholar 
    Norden N, Chazdon RL, Chao A, Jiang YH, Vilchez-Alvarado B (2009) Resilience of tropical rain forests: tree community reassembly in secondary forests. Ecol Lett 12:385–394Article 

    Google Scholar 
    Nowak MD, Davis AP, Anthony F, Yoder AD (2011) Expression and trans-specific polymorphism of self-incompatibility RNases in Coffea (Rubiaceae). PLoS One. https://doi.org/10.1371/journal.pone.0021019Nyakaana S (2007) Microgeographical genetic structure of forest robusta coffee (Coffea canephora, Pierre), in Kibale National Park, Uganda. Afr J Ecol 45:71–75Article 

    Google Scholar 
    Oberleitner F, Egger C, Oberdorfer S, Dullinger S, Wanek W, Hietz P (2021) Recovery of aboveground biomass, species richness and composition in tropical secondary forests in SW Costa Rica. Ecol Manag 479:118580Article 

    Google Scholar 
    Olsson O, Nuñez-Iturri G, Smith HG, Ottosson U, Effium EO (2019) Competition, seed dispersal and hunting: what drives germination and seedling survival in an Afrotropical forest? AoB Plants https://doi.org/10.1093/aobpla/plz018Oryem-Origa H (1999) Fruit and seed ecology of wild Robusta coffee (Coffea canephora Froehner) in Kibale National Park. Uganda Afr J Ecol 37:439–448Article 

    Google Scholar 
    Peakall R, Smouse RPP (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539Article 
    CAS 

    Google Scholar 
    Podani J (2000) Introduction to the exploration of multivariate biological data. Backhuys Publishers, Kerkwere
    Google Scholar 
    Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome. https://doi.org/10.3835/plantgenome2012.05.0005Poorter L, Craven D, Jakovac CC, van der Sande MT, Amissah L, Bongers F et al. (2021) Multidimensional tropical forest recovery. Science 374:1370–1376Article 
    CAS 

    Google Scholar 
    Raj A, Stephens M, Pritchard JK (2014) fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197:573–589Article 

    Google Scholar 
    RStudio Team (2016) RStudio: Integrated Development for RSasaki N, Putz FE (2009) Critical need for new definitions of “forest” and “forest degradation” in global climate change agreements. Conserv Lett 2:226–232Article 

    Google Scholar 
    Sezen UU, Chazdon RL, Holsinger KE (2007) Multigenerational genetic analysis of tropical secondary regeneration in a canopy palm. Ecology 88:3065–3075Article 

    Google Scholar 
    Schaumont D, Veeckman E, Van der Jeugt F, Haegeman A, van Glabeke S, Bawin Y et al. (2022) Stack Mapping Anchor Points (SMAP): a versatile suite of tools for read-backed haplotyping. Preprint at bioRxiv https://doi.org/10.1101/2022.03.10.483555Shapiro AC, Grantham HS, Aguilar-Amuchastegui N, Murray NJ, Gond V, Bonfils D, et al. (2021) Forest condition in the Congo Basin for the assessment of ecosystem conservation status. Ecol Indic. https://doi.org/10.1016/j.ecolind.2020.107268Silva MDC, Várzea V, Guerra-Guimarães L, Azinheira HG, Fernandez D, Petitot AS et al. (2006) Coffee resistance to the main diseases: leaf rust and coffee berry disease. Braz J Plant Physiol 18:119–147Article 
    CAS 

    Google Scholar 
    Theim TJ, Shirk RY, Givnish TJ (2014) Spatial genetic structure in four understorey Psychotria species (Rubiaceae) and implications for tropical forest diversity. Am J Bot 101:1189–1199Article 

    Google Scholar 
    Torti SD, Coley PD, Kursar TA (2001) Causes and consequences of monodominance in tropical lowland forests. Am Nat 157:141–153Article 
    CAS 

    Google Scholar 
    Tyukavina A, Hansen MC, Potapov P, Parker D, Okpa C, Stehman SV, et al. (2018) Congo Basin forest loss dominated by increasing smallholder clearing. Sci Adv. https://doi.org/10.1126/sciadv.aat2993Vanden Abeele S, Janssens SB, Asimonyio Anio J, Bawin Y, Depecker J, Kambale B et al. (2021) Genetic diversity of wild and cultivated Coffea canephora in northeastern DR Congo and the implications for conservation. Am J Bot 108:2425–2434Article 

    Google Scholar 
    Vandepitte K, Gristina AS, De Hert K, Meekers T, Roldán-Ruiz I, Honnay O (2012) Recolonization after habitat restoration leads to decreased genetic variation in populations of a terrestrial orchid. Mol Ecol 21:4206–4215Article 
    CAS 

    Google Scholar 
    Van Vliet N, Muhindo J, Kbale Nyumu J, Mushagalusa O, Nasi R (2018) Mammal depletion processes as evidenced from spatially explicit and temporal local ecological knowledge. Trop Conserv Sci 11:1–16
    Google Scholar 
    Vekemans X, Hardy OJ (2004) New insights from fine-scale spatial genetic structure analyses in plant populations. Mol Ecol 13:921–935Article 
    CAS 

    Google Scholar 
    Vranckx G, Jacquemyn H, Muys B, Honnay O (2012) Meta‐analysis of susceptibility of woody plants to loss of genetic diversity through habitat fragmentation. Conserv Biol 26:228–237Article 

    Google Scholar 
    Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 

    Google Scholar 
    Wellman FL (1961) Coffee. Botany, cultivation, and utilization. Leonard Hill, London
    Google Scholar 
    Widmer A, Lexer C (2001) Glacial refugia: sanctuaries for allelic richness, but not for gene diversity. Trends Ecol Evol 16:267–269Article 
    CAS 

    Google Scholar 
    Wright S (1932) The role of mutation, inbreeding, crossbreeding and selection in evolution. In: Proceedings of the sixth international congress of genetics. pp 356–366.Zhang J, Kobert K, Flouri T, Stamatakis A (2014) PEAR: a fast and accurate Illumina Paired-End read merger. Bioinformatics 30:614–620Article 
    CAS 

    Google Scholar  More

  • in

    Prediction of tide level based on variable weight combination of LightGBM and CNN-BiGRU model

    LightGBMBefore explaining LightGBM23, it is necessary to introduce XGBoost24, which is also based on the gradient boosting decision tree (GBDT) algorithm30. XGBoost integrates multiple classification and regression trees (CART) to compensate for the lack of prediction accuracy of a single CART. It is an improved boosting algorithm based on GBDT, which is popular due to its high processing speed, high regression accuracy and ability to process large-scale data31. However, XGBoost uses a presorted algorithm to find data segmentation points, which takes up considerable memory in the calculation and seriously affects cache optimization.LightGBM is improved based on XGBoost. It uses a histogram algorithm to find the best data segmentation point, which occupies less memory and has a lower complexity of data segmentation. The flow of the histogram algorithm to find the optimal segmentation point is shown in Fig. 3.Figure 3Histogram algorithm.Full size imageMoreover, LightGBM abandons the levelwise decision tree growth strategy used by most GBDT tools and uses the leafwise algorithm with depth limitations. This leaf-by-leaf growth strategy can reduce more errors and obtain better accuracy. Decision trees in boosting algorithms may grow too deep while training, leading to model overfitting. Therefore, LightGBM adds a maximum depth limit to the leafwise growth strategy to prevent this from happening and maintains its high computational efficiency. To summarize, LightGBM can be better and faster used in industrial practice and is also very suitable as the base model in our tide level prediction task. The layer-by-layer growth strategy and leaf-by-leaf growth strategy are shown in Fig. 4.Figure 4Two GBDT growth strategies.Full size imageCNN-BiGRUConvolutional neural networkA convolutional neural network (CNN) is a deep feedforward neural network with the characteristics of local connection and weight sharing. It was first used in the field of computer vision and achieved great success32,33. In recent years, CNNs have also been widely used in time series processing. For example, Bai et al.34 proposed a temporal convolutional network (TCN) based on a convolutional neural network and residual connections, which is not worse than recurrent neural networks such as LSTM in some time series analysis tasks. At present, a convolutional neural network is generally composed of convolution layers, pooling layers and a fully connected layer. Its network structure is shown in Fig. 5. The pooling layer is usually added after the convolution layers. The maximum pooling layer can retain the strong features in the data after the convolution operation, eliminate the weak features to reduce the number of parameters in a network and avoid overfitting of the model.Figure 5Schematic diagram of a convolutional neural network.Full size imageBidirectional GRUIn previous attempts at tide level prediction by scholars, bidirectional long short-term memory networks35 have achieved good prediction results. However, in our subsequent experiments, the bidirectional gated recurrent unit achieved higher prediction accuracy than BiLSTM, so we used the BiGRU network for subsequent prediction tasks.The GRU network36 adds a gating mechanism to control information updating in a recurrent neural network. Different from the mechanism in LSTM, GRU consists of only two gates called the update gate ({z}_{t}) and the reset door ({r}_{t}).The recurrent unit structure of the GRU network is shown in Fig. 6.Figure 6Recurrent unit structure of the GRU network.Full size imageEach unit of GRU is calculated as follows:$${z}_{t}= sigma ({W}_{z}{x}_{t}+{U}_{z}{h}_{t-1}+{b}_{z})$$
    (7)
    $${r}_{t}= sigma ({W}_{r}{x}_{t}+{U}_{r}{h}_{t-1}+{b}_{r})$$
    (8)
    $${widetilde{h}}_{t}=tanh({W}_{h}{x}_{t}+{U}_{h}left({r}_{t}odot {h}_{t-1}right)+{b}_{h})$$
    (9)
    $${h}_{t}={z}_{t}odot {h}_{t-1}+left(1-{z}_{t}right)odot {widetilde{h}}_{t}$$
    (10)
    In the above formula, ({z}_{t}) represents the update gate, which controls how much information is retained from the previous state ({h}_{t-1}) (without nonlinear transformation) when calculating the current state ({h}_{t}). Meanwhile, it also controls how much information will be accepted by ({h}_{t}) from the candidate states ({widetilde{h}}_{t}). ({r}_{t}) represents the reset gate, which is used to ensure whether the calculation of the candidate state ({widetilde{h}}_{t}) depends on the previous state ({h}_{t-1}). (upsigma ) is the standard sigmoid activation function; (tanh(cdot )) is the hyperbolic tangent activation function; and (odot ) indicates the Hadamard product. The weight matrices of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({W}_{z},{W}_{r},{W}_{h}); the coefficient matrices of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({U}_{z},{U}_{r},{U}_{h}); and the offset vectors of the update gate, reset gate, and ({widetilde{h}}_{t}) calculation layer are expressed as ({b}_{z},{b}_{r},{b}_{h}).A bidirectional gated recurrent unit network37 is a combination of two GRUs whose information propagating directions are reversed, and it has independent parameters in each, which makes it able to fit both forward and backward data at first and then join up the results from two directions. BiGRU can capture sequence patterns that may be ignored by unidirectional GRU. The structure of BiGRU is shown in Fig. 7.Figure 7The structure of BiGRU.Full size imageTaking the BiGRU’s forward hidden state vector at time (t) as ({h}_{t}^{(1)}) and taking the BiGRU’s backward hidden state vector at time (t) as ({h}_{t}^{(2)}), (upsigma ) indicates the standard sigmoid activation function, and (oplus ) indicates a vector splicing operation. We can calculate the output ({y}_{t}) of a BiGRU network as follows:$${h}_{t}={h}_{t}^{(1)}oplus {h}_{t}^{(2)}$$
    (11)
    $${y}_{t}=sigma ({h}_{t} )$$
    (12)
    CNN-BiGRU prediction modelBecause CNN has significant advantages in extracting useful features from a picture or a sequence and BiGRU is good at processing time series, we combine CNN and BiGRU to build the CNN-BiGRU model. The model can be mainly divided into an input layer, a convolution layer, a BiGRU network layer, a dropout layer, a fully connected layer and an output layer. The CNN layer and BiGRU layer are the core structures of the model. The function of the dropout layer is to avoid model overfitting. The CNN layer consists of two one-dimensional convolution (Conv1D) layers and a one-dimensional maximum pooling (MaxPooling1D) layer. The input of BiGRU is the output sequence of the CNN layer, and the BiGRU network is set as a one-hidden-layer structure. The structure of the CNN-BiGRU combination model is shown in Fig. 8.Figure 8The structure of CNN-BiGRU.Full size imageVariable weight combination modelWhen we analyze and predict relatively stationary tide level time series, LightGBM can perform well. However, due to environmental factors such as air pressure, wind force and terrain in reality, most tide level observation sequences are sometimes not relatively stationary, which requires that our tide level prediction model be reasonably able to “extrapolate” based on the sample observations, that is, be capable of generating values that are not in the sample. LightGBM is a tree-based model, which leads to our prediction results being between the maximum and minimum values of sequences. Therefore, LightGBM will not be able to accurately predict the situation or tidal change trend that did not appear in previous observations. However, the CNN-BiGRU model, which is a kind of neural network, has no such problem in theory and will be able to find the trend information that may be hidden in the tide level series. Therefore, we consider providing an appropriate weight for a single base model to build a combination model to improve the accuracy of the tide level prediction task.Principle of the residual weight combination model and improved variable weight combination modelTo improve the prediction accuracy of the combination model, a simple and effective idea is to determine the base models’ weights in the combination model according to the error between the prediction value and the real value. This method is also called the residual weight method, and its calculation formulas for determining the weights are:$$gleft({x}_{t}right)= sum_{i=1}^{m}{omega }_{i}left(t-1right){f}_{i}({x}_{t})$$
    (13)
    $${omega }_{i}left(t-1right)=frac{frac{1}{overline{{varphi }_{i}}left(t-1right)}}{sum_{i=1}^{m}frac{1}{overline{{varphi }_{i}}left(t-1right)}}$$
    (14)
    $$sum_{i=1}^{m}{omega }_{i}left(t-1right)=1,{omega }_{i}left(t-1right)ge 0$$
    (15)

    where ({omega }_{i}left(t-1right)) denotes the weight of the (i) th model at the moment (t-1), ({f}_{i}left({x}_{t}right)) denotes the prediction value of the (i) th model at the moment (t), (gleft({x}_{t}right)) denotes the prediction value of the combination model at the moment (t), and (overline{{varphi }_{i}}left(t-1right)) is the square sum of the predictive errors of the (i) th model at the moment (t-1).Our LightGBM-CNN-BiGRU (combination model) is based on the improved residual weight method. We call it the variable weight combination model. We use the weights calculated by formula (9) and formula (11) to calculate a series of new weights. The new weights from formula (11) will take the residual weight changes in (d) time steps into consideration by averaging the old weights in (d) time steps to improve the stability of the residual weight method.$${omega }_{j}left(tright)=frac{1}{d}sum_{k=1}^{d}{omega }_{i}left(t-kright)left(d=4right)$$
    (16)
    After obtaining a series of weights through formula (9) and formula (11), we take the absolute value of the error between the prediction value and the true value of each combination model at the moment of (t) as ({delta }_{i,t}) and ({delta }_{j,t}), respectively:$${delta }_{i,t}=mid sum_{i=1}^{m}{omega }_{i}left(tright){f}_{i}left({x}_{t}right)-{y}_{t}mid $$
    (17)
    $${delta }_{j,t}=mid sum_{i=1}^{m}{omega }_{j}left(tright){f}_{i}left({x}_{t}right)-{y}_{t}mid $$
    (18)
    Comparing ({delta }_{i,t}) and ({delta }_{j,t}), if ({delta }_{i,t} >{delta }_{j,t}), the combination model uses the new weight ({omega }_{j}left(tright)) in place of the original weight ({omega }_{i}left(tright)). Otherwise, the weight of the combination model remains unchanged.Parameter optimization of the combination modelBecause the LightGBM-CNN-BiGRU (combination model) is a variable weight combination of the prediction results from two base models, the performance of the combination model can be directly improved by separately optimizing the super parameters of the two base models. We mainly use the grid search algorithm and K-fold cross validation method to optimize the parameters. The grid search algorithm is a method to improve the performance of a certain model by iterating over a given set of parameters. With the help of the K-fold cross validation method, we can calculate the performance score of the LightGBM model on the training set and easily optimize its superparameters. The final parameters of the LightGBM model are set to num_leaves = 26, learning_rate = 0.05, and n_estimators = 46.For the CNN-BiGRU network, we mainly improve the prediction accuracy of the model by adjusting the size and number of hidden layers in the BiGRU structure and prevent the model from overfitting by changing the dropout ratio and tracking the validation loss of the network while training.The LightGBM and CNN-BiGRU variable weight combination modelThe workflow of our tide level prediction model is shown in Fig. 9. It mainly includes data preprocessing; training, optimization and prediction of the base models; construction of a variable weight combination prediction model; and evaluation and analysis of the combination model’s performance.

    (1)

    Data preprocessing: The quality of the data directly determines the upper limit of the prediction and generalization ability of a certain machine learning model. Standard, clean and continuous data are conducive to model training. The data used in this study are from the Irish National Tide Gauge Network, and all of them are subject to quality control. We filled in a small number of missing values and normalized the data to speed up the model training.

    (2)

    Construction and optimization of base models: We divide the dataset into a training set, a validation set and a test set according to the proportion of 7:1:2 and train the LightGBM model and CNN-BiGRU model with data on the training set. We optimize the parameters and monitor whether the model has been overfitted by tracking the validation loss of the network while training. Finally, we put the data into two base models for training and then obtain the prediction results of a single base model.

    (3)

    Construction of the variable weight combination model. Based on the prediction results of two single base models obtained in step (2), we calculate the weight of each base model according to the principle of the improved variable weight combination method and then obtain the prediction results of the variable weight combination model.

    (4)

    Model evaluation and analysis: According to the indexes of the model evaluation, the variable weight combination model is compared with other basic models to analyze its prediction performance after being improved.

    Figure 9Prediction flow of the LightGBM-CNN-BiGRU variable weight combination model.Full size image More

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    Sulfoquinovose is a widespread organosulfur substrate for Roseobacter clade bacteria in the ocean

    Snow AJD, Burchill L, Sharma M, Davies GJ, Williams SJ. Sulfoglycolysis: Catabolic pathways for metabolism of sulfoquinovose. Chem Soc Rev. 2021;50:13628–45.Article 
    CAS 

    Google Scholar 
    Van Mooy BAS, Rocap G, Fredricks HF, Evans CT, Devol AH. Sulfolipids dramatically decrease phosphorus demand by picocyanobacteria in oligotrophic marine environments. Proc Natl Acad Sci USA 2006;103:8607–12.Article 

    Google Scholar 
    Wu J, Sunda W, Boyle EA, Karl DM. Phosphate depletion in the western North Atlantic. Ocean Sci 2000;289:759–62.CAS 

    Google Scholar 
    Goddard-Borger ED, Williams SJ. Sulfoquinovose in the biosphere: occurrence, metabolism and functions. Biochem J. 2017;474:827–49.Article 
    CAS 

    Google Scholar 
    Harwood JL, Nicholls RG. The plant sulpholipid- a major component of the sulphur cycle. Biochem Soc Trans. 1979;7:440–7.Article 
    CAS 

    Google Scholar 
    Moran MA, Durham BP. Sulfur metabolites in the pelagic ocean. Nat Rev Microbiol. 2019;17:665–78.Article 
    CAS 

    Google Scholar 
    Tang K. Chemical diversity and biochemical transformation of biogenic organic sulfur in the ocean. Front Mar Sci. 2020;7:68.Article 

    Google Scholar 
    Denger K, Weiss M, Felux AK, Schneider A, Mayer C, Spiteller D, et al. Sulphoglycolysis in Escherichia coli K-12 closes a gap in the biogeochemical sulphur cycle. Nature 2014;507:114–7.Article 
    CAS 

    Google Scholar 
    Hanson BT, Kits KD, Loffler J, Burrichter AG, Fiedler A, Denger K, et al. Sulfoquinovose is a select nutrient of prominent bacteria and a source of hydrogen sulfide in the human gut. ISME J. 2021;15:2779–91.Article 
    CAS 

    Google Scholar 
    Strickland TC, Fitzgerald JW. Mineralization of sulfur in sulfoquinovose by forest soils. Soil Biol Biochem. 1983;15:347–9.Article 
    CAS 

    Google Scholar 
    Felux AK, Spiteller D, Klebensberger J, Schleheck D. Entner-Doudoroff pathway for sulfoquinovose degradation in Pseudomonas putida SQ1. Proc Natl Acad Sci USA 2015;112:E4298–E305.Article 
    CAS 

    Google Scholar 
    Frommeyer B, Fiedler AW, Oehler SR, Hanson BT, Loy A, Franchini P, et al. Environmental and intestinal phylum Firmicutes bacteria metabolize the plant sugar sulfoquinovose via a 6-deoxy-6-sulfofructose transaldolase pathway. Iscience. 2020;23:101510.Article 
    CAS 

    Google Scholar 
    Roy AB, Hewlins MJE, Ellis AJ, Harwood JL, White GF. Glycolytic breakdown of sulfoquinovose in bacteria: A missing link in the sulfur cycle. Appl Environ Microbiol. 2003;69:6434–41.Article 
    CAS 

    Google Scholar 
    Liu J, Wei Y, Ma K, An J, Liu X, Liu Y, et al. Mechanistically diverse pathways for sulfoquinovose degradation in bacteria. ACS Catal. 2021;11:14740–50.Article 
    CAS 

    Google Scholar 
    Zhang S, Li Z, Yan Y, Zhang C, Li J, Zhao B. Bacillus urumqiensis sp. nov., a moderately haloalkaliphilic bacterium isolated from a salt lake. Int J Syst Evol Microbiol. 2016;66:2305–12.Article 
    CAS 

    Google Scholar 
    Durham BP, Sharma S, Luo H, Smith CB, Amin SA, Bender SJ, et al. Cryptic carbon and sulfur cycling between surface ocean plankton. Proc Natl Acad Sci USA 2015;112:453–7.Article 
    CAS 

    Google Scholar 
    Chen X, Liu L, Gao X, Dai X, Han Y, Chen Q, et al. Metabolism of chiral sulfonate compound 2,3-dihydroxypropane-1-sulfo-nate (DHPS) by Roseobacter bacteria in marine environment. Environ Int. 2021;157:106829.Article 
    CAS 

    Google Scholar 
    Liu J, Wei Y, Lin L, Teng L, Yin J, Lu Q, et al. Two Radical-dependent mechanisms for anaerobic degradation of the globally abundant Organosulfur Compound Dihydroxypropanesulfonate. Proc Natl Acad Sci USA 2020;117:15599.Article 
    CAS 

    Google Scholar 
    Xing M, Wei Y, Zhou Y, Zhang J, Lin L, Hu Y, et al. Radical-mediated C-S bond cleavage in C2 sulfonate degradation by anaerobic bacteria. Nat Commun. 2019;10:1609.Article 

    Google Scholar 
    Sharma M, Lingford JP, Petricevic M, Snow AJD, Zhang Y, Jarva MA, et al. Oxidative desulfurization pathway for complete catabolism of sulfoquinovose by bacteria. Proc Natl Acad Sci USA 2022;119:e2116022119.Article 
    CAS 

    Google Scholar 
    Scholz SS, Serif M, Schleheck D, Sayer MDJ, Cook AM, Kupper FC. Sulfoquinovose metabolism in marine algae. Bot Mar. 2021;64:301–12.Article 
    CAS 

    Google Scholar 
    Abayakoon P, Epa R, Petricevic M, Bengt C, Mui JWY, van der Peet PL, et al. Comprehensive synthesis of substrates, intermediates, and products of the sulfoglycolytic Embden-Meyerhoff-Parnas pathway. J Org Chem. 2019;84:2901–10.Article 
    CAS 

    Google Scholar 
    Denger K, Smits THM, Cook AM. L-Cysteate sulpho-lyase, a widespread pyridoxal 5 ‘-phosphate-coupled desulphonative enzyme purified from Silicibacter pomeroyi DSS-3. Biochem J. 2006;394:657–64.Article 
    CAS 

    Google Scholar 
    Guillard RRL. Culture of Phytoplankton for Feeding Marine Invertebrates. Smith WL, Chanley MH, (eds): Springer US; 1975. Boston, MA. pp 29–60.Moore LR, Coe A, Zinser ER, Saito MA, Sullivan MB, Lindell D, et al. Culturing the marine cyanobacterium Prochlorococcus. Limnol Oceanogr Methods. 2007;5:353–62.Article 
    CAS 

    Google Scholar 
    Waterbury J, Watson S, Valois F, Franks D. Biological and ecological characterization of the marine unicellular cyanobacterium Synechococcus. Platt T, Li WKW, (eds). Department of Fisheries and Oceans, Ottawa 1986. pp 71–120.Olenina I, Hajdu S, Edler L, Andersson A, Wasmund N, Busch S, et al. Biovolumes and size-classes of phytoplankton in the Baltic Sea. HELCOM Balt Sea Environ Proc. 2006;106:144.
    Google Scholar 
    Zheng Q, Wang Y, Lu J, Lin W, Chen F, Jiao N. Metagenomic and metaproteomic insights into photoautotrophic and heterotrophic interactions in a Synechococcus culture. mbio 2020;11:e03261–19.Article 
    CAS 

    Google Scholar 
    Partensky F, Hess WR, Vaulot D. Prochlorococcus, a marine photosynthetic prokaryote of global significance. Microbiol Mol Biol Rev. 1999;63:106–27.Article 
    CAS 

    Google Scholar 
    Han Y, Zhang M, Chen X, Zhai W, Tan E, Tang K. Transcriptomic evidences for microbial carbon and nitrogen cycles in the deoxygenated seawaters of Bohai Sea. Environ Int. 2022;158:106889.Article 
    CAS 

    Google Scholar 
    Li WKW. Primary production of prochlorophytes, cyanobacteria, and eukaryotic ultraphytoplankton – measurements from flow cytometric sorting. Limnol Oceanogr. 1994;39:169–75.Article 
    CAS 

    Google Scholar 
    Denger K, Ruff A, Rein U, Cook AM. Sulphoacetaldehyde sulpho-lyase (EC 4.4.1.12) from Desulfonispora thiosulfatigenes: purification, properties and primary sequence. Biochem J. 2001;357:581–6.Article 
    CAS 

    Google Scholar 
    Ismail R, Lee HY, Mahyudin NA, Abu, Bakar F. Linearity study on detection and quantification limits for the determination of avermectins using linear regression. J Food Drug Anal. 2014;22:407–12.Article 
    CAS 

    Google Scholar 
    Klemetsen T, Raknes IA, Fu J, Agafonov A, Balasundaram SV, Tartari G, et al. The MAR databases: development and implementation of databases specific for marine metagenomics. Nucleic Acids Res. 2018;46:D692–D9.Article 
    CAS 

    Google Scholar 
    Suzek BE, Huang H, McGarvey P, Mazumder R, Wu CH. UniRef: comprehensive and non-redundant UniProt reference clusters. Bioinformatics 2007;23:1282–8.Article 
    CAS 

    Google Scholar 
    Rozewicki J, Li S, Amada KM, Standley DM, Katoh K. MAFFT-DASH: Integrated protein sequence and structural alignment. Nucleic Acids Res. 2019;47:W5–W10.CAS 

    Google Scholar 
    Schuller DJ, Reisch CR, Moran MA, Whitman WB, Lanzilotta WN. Structures of dimethylsulfoniopropionate-dependent demethylase from the marine organism Pelagabacter ubique. Protein Sci. 2012;21:289–98.Article 
    CAS 

    Google Scholar 
    Bharath SR, Bisht S, Harijan RK, Savithri HS, Murthy MR. Structural and mutational studies on substrate specificity and catalysis of Salmonella typhimurium D-cysteine desulfhydrase. PLoS One. 2012;7:e36267.Article 
    CAS 

    Google Scholar 
    Chartron J, Carroll KS, Shiau C, Gao H, Leary JA, Bertozzi CR, et al. Substrate Recognition, Protein Dynamics, and Iron-Sulfur Cluster in Pseudomonas aeruginosa Adenosine 5′-Phosphosulfate Reductase. J Mol Biol. 2006;364:152–69.Article 
    CAS 

    Google Scholar 
    Davis KM, Altmyer M, Martinie RJ, Schaperdoth I, Krebs C, Bollinger JM Jr, et al. Structure of a Ferryl Mimic in the Archetypal Iron(II)- and 2-(Oxo)-glutarate-Dependent Dioxygenase, TauD. Biochemistry 2019;58:4218–23.Article 
    CAS 

    Google Scholar 
    Mirdita M, Schütze K, Moriwaki Y, Heo L, Ovchinnikov S, Steinegger M. ColabFold: Making protein folding accessible to all. Nat Methods. 2022;19:679–82.Article 
    CAS 

    Google Scholar 
    Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583–9.Article 
    CAS 

    Google Scholar 
    Zhang C, Shine M, Pyle AM, Zhang Y. US-align: universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nat Methods. 2022;19:1109–15.Article 
    CAS 

    Google Scholar 
    Xu J, Zhang Y. How significant is a protein structure similarity with TM-score = 0.5? Bioinformatics 2010;26:889–95.Article 
    CAS 

    Google Scholar 
    Nguyen L-T, Schmidt HA, von Haeseler A, Minh BQ. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2014;32:268–74.Article 

    Google Scholar 
    Villar E, Vannier T, Vernette C, Lescot M, Cuenca M, Alexandre A, et al. The Ocean Gene Atlas: exploring the biogeography of plankton genes online. Nucleic Acids Res. 2018;46:W289–W95.Article 
    CAS 

    Google Scholar 
    Vernette C, Henry N, Lecubin J, de Vargas C, Hingamp P, Lescot M. The Ocean barcode atlas: A web service to explore the biodiversity and biogeography of marine organisms. Mol Ecol Resour. 2021;21:1347–58.Article 
    CAS 

    Google Scholar 
    Paoli L, Ruscheweyh H-J, Forneris CC, Hubrich F, Kautsar S, Bhushan A, et al. Biosynthetic potential of the global ocean microbiome. Nature 2022;607:111–8.Article 
    CAS 

    Google Scholar 
    Sunagawa S, Acinas SG, Bork P, Bowler C, Acinas SG, Babin M, et al. Tara Oceans: towards global ocean ecosystems biology. Nat Rev Microbiol. 2020;18:428–45.Article 
    CAS 

    Google Scholar 
    Acinas SG, Sánchez P, Salazar G, Cornejo-Castillo FM, Sebastián M, Logares R, et al. Deep ocean metagenomes provide insight into the metabolic architecture of bathypelagic microbial communities. Commun Biol. 2021;4:604.Article 
    CAS 

    Google Scholar 
    Biller SJ, Berube PM, Dooley K, Williams M, Satinsky BM, Hackl T, et al. Marine microbial metagenomes sampled across space and time. Sci Data. 2018;5:180176.Article 
    CAS 

    Google Scholar 
    Pachiadaki MG, Brown JM, Brown J, Bezuidt O, Berube PM, Biller SJ, et al. Charting the Complexity of the Marine Microbiome through Single-Cell Genomics. Cell 2019;179:1623–35.Article 
    CAS 

    Google Scholar 
    Delmont TO, Quince C, Shaiber A, Esen ÖC, Lee STM, Rappé MS, et al. Nitrogen-fixing populations of Planctomycetes and Proteobacteria are abundant in surface ocean metagenomes. Nat Microbiol. 2018;3:804–13.Article 
    CAS 

    Google Scholar 
    Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30:2725–9.Article 
    CAS 

    Google Scholar 
    Subramanian B, Gao S, Lercher MJ, Hu S, Chen W-H. Evolview v3: A webserver for visualization, annotation, and management of phylogenetic trees. Nucleic Acids Res. 2019;47:W270–W5.Article 
    CAS 

    Google Scholar 
    Xing M, Wei Y, Zhou Y, Zhang J, Lin L, Hu Y, et al. Radical-mediated C-S bond cleavage in C2 sulfonate degradation by anaerobic bacteria. Nat Commun. 2019;10:1609.Article 

    Google Scholar 
    Biebl H, Allgaier M, Tindall BJ, Koblizek M, Lunsdorf H, Pukall R, et al. Dinoroseobacter shibae gen. nov., sp nov., a new aerobic phototrophic bacterium isolated from dinoflagellates. Int J Syst Evol Microbiol. 2005;55:1089–96.Article 
    CAS 

    Google Scholar 
    Fu H, Uchimiya M, Gore J, Moran MA. Ecological drivers of bacterial community assembly in synthetic phycospheres. Proc Natl Acad Sci USA 2020;117:3656–62.Article 
    CAS 

    Google Scholar 
    Chen I-MA, Chu K, Palaniappan K, Ratner A, Huang J, Huntemann M, et al. The IMG/M data management and analysis system v.7: content updates and new features. Nucleic Acids Res. 2022. https://doi.org/10.1093/nar/gkac976.Shiba T. Roseobacter litoralis gen. nov., sp. nov., and Roseobacter denitrificans sp. nov., aerobic pink-pigmented bacteria which contain bacteriochlorophyll a. Syst Appl Microbiol. 1991;14:140–5.Article 

    Google Scholar 
    Kopriva S, Calderwood A, Weckopp SC, Koprivova A. Plant sulfur and big data. Plant Sci. 2015;241:1–10.Article 
    CAS 

    Google Scholar 
    Simon J, Kroneck PMH. Microbial sulfite respiration. Adv Micro Physiol. 2013;62:45–117.Article 
    CAS 

    Google Scholar 
    Gonzalez JM, Covert JS, Whitman WB, Henriksen JR, Mayer F, Scharf B, et al. Silicibacter pomeroyi sp nov and Roseovarius nubinhibens sp nov., dimethylsulfoniopropionate-demethylating bacteria from marine environments. Int J Syst Evol Microbiol. 2003;53:1261–9.Article 
    CAS 

    Google Scholar 
    Liang KYH, Orata FD, Boucher YF, Case RJ. Roseobacters in a sea of poly- and paraphyly: whole genome-based taxonomy of the family Rhodobacteraceae and the proposal for the split of the “Roseobacter clade” into a novel family, Roseobacteraceae fam. nov. Front Microbiol. 2021;12:683109.Article 

    Google Scholar 
    Howard EC, Sun S, Biers EJ, Moran MA. Abundant and diverse bacteria involved in DMSP degradation in marine surface waters. Environ Microbiol. 2008;10:2397–410.Article 
    CAS 

    Google Scholar 
    Howard EC, Henriksen JR, Buchan A, Reisch CR, Buergmann H, Welsh R, et al. Bacterial taxa that limit sulfur flux from the ocean. Science. 2006;314:649–52.Article 
    CAS 

    Google Scholar 
    Durham BP, Boysen AK, Carlson LT, Groussman RD, Heal KR, Cain KR, et al. Sulfonate-based networks between eukaryotic phytoplankton and heterotrophic bacteria in the surface ocean. Nat Microbiol. 2019;4:1706–15.Article 
    CAS 

    Google Scholar 
    Smetacek V. Diatoms and the ocean carbon cycle. Protist 1999;150:25–32.Article 
    CAS 

    Google Scholar 
    Stoecker DK, Lavrentyev PJ. Mixotrophic plankton in the polar seas: A pan-Arctic review. Front Mar Sci. 2018;5:292.Article 

    Google Scholar 
    Turner SM, Malin G, Liss PS, Harbour DS, Holligan PM. The seasonal-variation of dimethyl sulfide and dimethylsulfoniopropionate concentrations in nearshore waters. Limnol Oceanogr. 1988;33:364–75.Article 
    CAS 

    Google Scholar 
    Belviso S, Kim S-K, Rassoulzadegan F, Krajka B, Nguyen BC, Mihalopoulos N, et al. Production of dimethylsulfonium propionate (DMSP) and dimethylsulfide (DMS) by a microbial food web. Limnol Oceanogr. 1990;35:1810–21.Article 
    CAS 

    Google Scholar 
    Simo R, Pedros-Alio C, Malin G, Grimalt JO. Biological turnover of DMS, DMSP and DMSO in contrasting open-sea waters. Mar Ecol Prog Ser. 2000;203:1–11.Article 
    CAS 

    Google Scholar 
    Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci USA 2013;110:9824–9.Article 
    CAS 

    Google Scholar 
    Gasparovic B, Penezic A, Frka S, Kazazic S, Lampitt RS, Holguin FO, et al. Particulate sulfur-containing lipids: Production and cycling from the epipelagic to the abyssopelagic zone. Deep Sea Res Part I Oceanogr Res Pap. 2018;134:12–22.Article 
    CAS 

    Google Scholar 
    Zhan P, Tang K, Chen X, Yu L. Complete genome sequence of Maribacter sp T28, a polysaccharide-degrading marine flavobacteria. J Biotechnol. 2017;259:1–5.Article 
    CAS 

    Google Scholar 
    Van Mooy BAS, Fredricks HF. Bacterial and eukaryotic intact polar lipids in the eastern subtropical South Pacific: Water-column distribution, planktonic sources, and fatty acid composition. Geochim Cosmochim Acta. 2010;74:6499–516.Article 

    Google Scholar 
    Popendorf KJ, Tanaka T, Pujo-Pay M, Lagaria A, Courties C, Conan P, et al. Gradients in intact polar diacylglycerolipids across the Mediterranean Sea are related to phosphate availability. Biogeosciences 2011;8:3733–45.Article 
    CAS 

    Google Scholar  More

  • in

    Forest conservation in Indigenous territories and protected areas in the Brazilian Amazon

    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

  • in

    Challenges and opportunities for achieving Sustainable Development Goals through restoration of Indonesia’s mangroves

    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

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    Plant–pollinator network change across a century in the subarctic

    Potts, S. G. et al. Global pollinator declines: trends, impacts and drivers. Trends Ecol. Evol. 25, 345–353 (2010).Article 
    PubMed 

    Google Scholar 
    Lautenbach, S., Seppelt, R., Liebscher, J. & Dormann, C. F. Spatial and temporal trends of global pollination benefit. PLoS ONE 7, e35954 (2012).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ollerton, J., Winfree, R. & Tarrant, S. How many flowering plants are pollinated by animals? Oikos 120, 321–326 (2011).Article 

    Google Scholar 
    Rodger, J. G. et al. Widespread vulnerability of flowering plant seed production to pollinator declines. Sci. Adv. 7, eabd3524 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).Article 
    CAS 
    PubMed 

    Google Scholar 
    Bennett, J. M. et al. Land use and pollinator dependency drives global patterns of pollen limitation in the Anthropocene. Nat. Commun. 11, 3999 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Tylianakis, J. M., Didham, R. K., Bascompte, J. & Wardle, D. A. Global change and species interactions in terrestrial ecosystems. Ecol. Lett. 11, 1351–1363 (2008).Article 
    PubMed 

    Google Scholar 
    Hegland, S. J., Nielsen, A., Lázaro, A., Bjerknes, A.-L. & Totland, Ø. How does climate warming affect plant–pollinator interactions? Ecol. Lett. 12, 184–195 (2009).Article 
    PubMed 

    Google Scholar 
    Thébault, E. & Fontaine, C. Stability of ecological communities and the architecture of mutualistic and trophic networks. Science 329, 853–856 (2010).Article 
    PubMed 

    Google Scholar 
    Lever, J. J., van Nes, E. H., Scheffer, M. & Bascompte, J. The sudden collapse of pollinator communities. Ecol. Lett. 17, 350–359 (2014).Article 
    PubMed 

    Google Scholar 
    Valdovinos, F. S. et al. Species traits and network structure predict the success and impacts of pollinator invasions. Nat. Commun. 9, 2153 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Waser, N. M., Chittka, L., Price, M. V., Williams, N. M. & Ollerton, J. Generalization in pollination systems, and why it matters. Ecology 77, 1043–1060 (1996).Article 

    Google Scholar 
    Brosi, B. J. Pollinator specialization: from the individual to the community. New Phytol. 210, 1190–1194 (2016).Article 
    PubMed 

    Google Scholar 
    Elmqvist, T. et al. Response diversity, ecosystem change, and resilience. Front. Ecol. Environ. 1, 488–494 (2003).Article 

    Google Scholar 
    Waser, N. M. & Ollerton, J. Plant–Pollinator Interactions: From Specialization to Generalization (Univ. of Chicago Press, 2006).Ashman, T.-L., Arceo-Gómez, G., Bennett, J. M. & Knight, T. M. Is heterospecific pollen receipt the missing link in understanding pollen limitation of plant reproduction? Am. J. Bot. 107, 845–847 (2020).Article 
    PubMed 

    Google Scholar 
    Garibaldi, L. A. et al. Trait matching of flower visitors and crops predicts fruit set better than trait diversity. J. Appl. Ecol. 52, 1436–1444 (2015).Article 

    Google Scholar 
    CaraDonna, P. J. et al. Seeing through the static: the temporal dimension of plant–animal mutualistic interactions. Ecol. Lett. 24, 149–161 (2021).Article 
    PubMed 

    Google Scholar 
    Burkle, L. A., Marlin, J. C. & Knight, T. M. Plant–pollinator interactions over 120 years: loss of species, co-occurrence, and function. Science 339, 1611–1615 (2013).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jacquemin, F. et al. Loss of pollinator specialization revealed by historical opportunistic data: insights from network-based analysis. PLoS ONE 15, e0235890 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Mathiasson, M. E. & Rehan, S. M. Wild bee declines linked to plant–pollinator network changes and plant species introductions. Insect Conserv. Divers. 13, 595–605 (2020).Article 

    Google Scholar 
    Bennett, J. M. et al. A review of European studies on pollination networks and pollen limitation, and a case study designed to fill in a gap. AoB Plants 10, ply068 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Doré, M., Fontaine, C. & Thébault, E. Relative effects of anthropogenic pressures, climate, and sampling design on the structure of pollination networks at the global scale. Glob. Change Biol. 27, 1266–1280 (2021).Article 

    Google Scholar 
    Rader, R. et al. Non-bee insects are important contributors to global crop pollination. Proc. Natl Acad. Sci. USA 113, 146–151 (2016).Article 
    CAS 
    PubMed 

    Google Scholar 
    Post, E. et al. Ecological dynamics across the arctic associated with recent climate change. Science 325, 1355–1358 (2009).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hung, K.-L. J., Kingston, J. M., Albrecht, M., Holway, D. A. & Kohn, J. R. The worldwide importance of honey bees as pollinators in natural habitats. Proc. R. Soc. B 285, 20172140 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kearns, C. A. Anthophilous fly distribution across an elevation gradient. Am. Midl. Nat. 127, 172–182 (1992).Article 

    Google Scholar 
    Kevan, P. G. Insect pollination of high arctic flowers. J. Ecol. 60, 831–847 (1972).Article 

    Google Scholar 
    Tiusanen, M., Hebert, P. D. N., Schmidt, N. M. & Roslin, T. One fly to rule them all—muscid flies are the key pollinators in the arctic. Proc. Roy. Soc. B 283, 20161271 (2016).Article 

    Google Scholar 
    Weiner, C., Werner, M., Linsenmair, K. E. & Blüthgen, N. Land use intensity in grasslands: changes in biodiversity, species composition and specialisation in flower visitor networks. Basic Appl. Ecol. 12, 292–299 (2011).Article 

    Google Scholar 
    Rader, R., Edwards, W., Westcott, D. A., Cunningham, S. A. & Howlett, B. G. Pollen transport differs among bees and flies in a human-modified landscape. Divers. Distrib. 17, 519–529 (2011).Article 

    Google Scholar 
    Bartley, T. J. et al. Food web rewiring in a changing world. Nat. Ecol. Evol. 3, 345–354 (2019).Article 
    PubMed 

    Google Scholar 
    Ghisbain, G., Gérard, M., Wood, T. J., Hines, H. M. & Michez, D. Expanding insect pollinators in the Anthropocene. Biol. Rev. 96, 2755–2770 (2021).Article 
    PubMed 

    Google Scholar 
    Silén, F. Blombiologiska iakttagelser i Kittilä Lappmark. Medd. Soc. Fauna Flora Fennica 31, 80–99 (1906).
    Google Scholar 
    Clavel, J., Julliard, R. & Devictor, V. Worldwide decline of specialist species: toward a global functional homogenization? Front. Ecol. Environ. 9, 222–228 (2011).Article 

    Google Scholar 
    Erhardt, A. Pollination of Dianthus superbus L. Flora 185, 99–106 (1991).Article 

    Google Scholar 
    Witt, T., Jürgens, A., Geyer, R. & Gottsberger, G. Nectar dynamics and sugar composition in flowers of Silene and Saponaria species (Caryophyllaceae). Plant Biol. 1, 334–345 (1999).Article 
    CAS 

    Google Scholar 
    Morales, C. L. & Traveset, A. Interspecific pollen transfer: magnitude, prevalence and consequences for plant fitness. Crit. Rev. Plant Sci. 27, 221–238 (2008).Article 
    CAS 

    Google Scholar 
    Ashman, T.-L. & Arceo-Gómez, G. Toward a predictive understanding of the fitness costs of heterospecific pollen receipt and its importance in co-flowering communities. Am. J. Bot. 100, 1061–1070 (2013).Article 
    PubMed 

    Google Scholar 
    Orford, K. A., Vaughan, I. P. & Memmott, J. The forgotten flies: the importance of non-syrphid Diptera as pollinators. Proc. R. Soc. B 282, 20142934 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stavert, J. R. et al. Hairiness: the missing link between pollinators and pollination. PeerJ 4, e2779 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Doyle, T. et al. Pollination by hoverflies in the Anthropocene. Proc. R. Soc. B 287, 20200508 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Albrecht, M., Schmid, B., Hautier, Y. & Müller, C. B. Diverse pollinator communities enhance plant reproductive success. Proc. R. Soc. B. 279, 4845–4852 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fründ, J., Dormann, C. F., Holzschuh, A. & Tscharntke, T. Bee diversity effects on pollination depend on functional complementarity and niche shifts. Ecology 94, 2042–2054 (2013).Article 
    PubMed 

    Google Scholar 
    Magrach, A., Molina, F. P. & Bartomeus, I. Niche complementarity among pollinators increases community-level plant reproductive success. Peer Commun. J. 1, e1 (2021).Article 

    Google Scholar 
    Giménez-Benavides, L., Dötterl, S., Jürgens, A., Escudero, A. & Iriondo, J. M. Generalist diurnal pollination provides greater fitness in a plant with nocturnal pollination syndrome: assessing the effects of a Silene–Hadena interaction. Oikos 116, 1461–1472 (2007).
    Google Scholar 
    Vázquez, D. P., Blüthgen, N., Cagnolo, L. & Chacoff, N. P. Uniting pattern and process in plant–animal mutualistic networks: a review. Ann. Bot. 103, 1445–1457 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Vizentin-Bugoni, J., Debastiani, V. J., Bastazini, V. A. G., Maruyama, P. K. & Sperry, J. H. Including rewiring in the estimation of the robustness of mutualistic networks. Methods Ecol. Evol. 11, 106–116 (2020).Article 

    Google Scholar 
    Brosi, B. J. & Briggs, H. M. Single pollinator species losses reduce floral fidelity and plant reproductive function. Proc. Natl Acad. Sci. USA 110, 13044–13048 (2013).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pekkarinen, A. & Teräs, I. Zoogeography of Bombus and Psithyrus in northwestern Europe (Hymenoptera, Apidae). Ann. Zool. Fennici 30, 187–208 (1993).
    Google Scholar 
    Arbetman, M. P., Gleiser, G., Morales, C. L., Williams, P. & Aizen, M. A. Global decline of bumblebees is phylogenetically structured and inversely related to species range size and pathogen incidence. Proc. R. Soc. B 284, 20170204 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kerr, J. T. et al. Climate change impacts on bumblebees converge across continents. Science 349, 177–180 (2015).Article 
    CAS 
    PubMed 

    Google Scholar 
    Arceo-Gómez, G., Barker, D., Stanley, A., Watson, T. & Daniels, J. Plant–pollinator network structural properties differentially affect pollen transfer dynamics and pollination success. Oecologia 192, 1037–1045 (2020).Article 
    PubMed 

    Google Scholar 
    de Santiago-Hernández, M. H. et al. The role of pollination effectiveness on the attributes of interaction networks: from floral visitation to plant fitness. Ecology 100, e02803 (2019).Article 
    PubMed 

    Google Scholar 
    Koch, V., Zoller, L., Bennett, J. M. & Knight, T. M. Pollinator dependence but no pollen limitation for eight plants occurring north of the Arctic Circle. Ecol. Evol. 10, 13664–13672 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Loboda, S., Savage, J., Buddle, C. M., Schmidt, N. M. & Høye, T. T. Declining diversity and abundance of High Arctic fly assemblages over two decades of rapid climate warming. Ecography 41, 265–277 (2018).Article 

    Google Scholar 
    Høye, T. T., Post, E., Schmidt, N. M., Trøjelsgaard, K. & Forchhammer, M. C. Shorter flowering seasons and declining abundance of flower visitors in a warmer Arctic. Nat. Clim. Change 3, 759–763 (2013).Article 

    Google Scholar 
    Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).Article 
    CAS 
    PubMed 

    Google Scholar 
    Zattara, E. E. & Aizen, M. A. Worldwide occurrence records suggest a global decline in bee species richness. One Earth 4, 114–123 (2021).Article 

    Google Scholar 
    Bartomeus, I., Stavert, J. R., Ward, D. & Aguado, O. Historical collections as a tool for assessing the global pollination crisis. Philos. Trans. R. Soc. B 374, 20170389 (2019).Article 

    Google Scholar 
    Rakosy, D., Ashman, T.-L., Zoller, L., Stanley, A. & Knight, T. M. Integration of historic collections can shed light on patterns of change in plant–pollinator interactions and pollination service. Func. Ecol. https://doi.org/10.1111/1365-2435.14211 (2022).Hyne, C. J. C. W. Through Arctic Lapland (A. and C. Black, 1898).Knuth, P. Handbuch der Blütenbiologie, unter Zugrundelegung von Herman Müllers Werk: ‘Die Befruchtung der Blumen durch Insekten’ (W. Engelmann, 1898).Zoller, L. & Knight, T. M. Historical records of plant-insect interactions in subarctic Finland.BMC Res. Notes 15, 317 (2022).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Zoller, L. & Knight, T. M. Historical records of plant–insect interactions in subarctic Finland. figshare https://doi.org/10.6084/m9.figshare.c.5828663.v4 (2022).Zoller, L., Bennett, J. M. & Knight, T. M. Diel-scale temporal dynamics in the abundance and composition of pollinators in the arctic summer. Sci. Rep. 10, 21187 (2020).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2020).Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: an R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).Article 

    Google Scholar 
    Klotz, S., Kühn, I. & Durka, W. Biolflor Database (UFZ—Centre for Environmental Research Leipzig-Halle, 2002); https://www.ufz.de/biolflor/index.jspOksanen, J. et al. vegan: Community ecology package. R version 2.5.7 (2020).Chao, A., Chazdon, R. L., Colwell, R. K. & Shen, T.-J. Abundance-based similarity indices and their estimation when there are unseen species in samples. Biometrics 62, 361–371 (2006).Article 
    PubMed 

    Google Scholar 
    Dormann, C. F. et al. bipartite: Visualising bipartite networks and calculating some (ecological) indices. R version 2.16 (2021).Blüthgen, N., Menzel, F. & Blüthgen, N. Measuring specialization in species interaction networks. BMC Ecol. 6, 9 (2006).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stefan, V. & Knight, T. M. bootstrapnet: Bootstrap network metrics. R version 1.0.0 https://valentinitnelav.github.io/bootstrapnet/ (2021).Poisot, T., Canard, E., Mouillot, D., Mouquet, N. & Gravel, D. The dissimilarity of species interaction networks. Ecol. Lett. 15, 1353–1361 (2012).Article 
    PubMed 

    Google Scholar 
    Poisot, T. Dissimilarity of species interaction networks: quantifying the effect of turnover and rewiring. Peer Community Journal 2, e35 (2022).Article 

    Google Scholar 
    Dormann, C. F. How to be a specialist? Quantifying specialisation in pollination networks. Netw. Biol. 1, 1 (2011).
    Google Scholar  More

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    A watershed moment for healthy watersheds

    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