Philippa Kaur

Understanding the source and fate of microplastics (plastic debris  More

Zou, K. et al. eDNA metabarcoding as a promising conservation tool for monitoring fish diversity in a coastal wetland of the Pearl River Estuary compared to bottom trawling. Sci. Total Environ. 702, 134704 (2020).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Almond, R., Grooten, M. & Peterson, T. Living Planet Report 2020-Bending the Curve of Biodiversity Loss (World Wildlife Fund, 2020).
Google Scholar 
Beverton, R. Fish resources; threats and protection. Neth. J. Zool. 42, 139–175 (1991).Article 

Google Scholar 
Jackson, S. & Head, L. Australia’s mass fish kills as a crisis of modern water: Understanding hydrosocial change in the Murray-Darling Basin. Geoforum 109, 44–56 (2020).Article 

Google Scholar 
Rees, H. C. et al. REVIEW: The detection of aquatic animal species using environmental DNA—a review of eDNA as a survey tool in ecology. J. Appl. Ecol. 51, 1450–1459 (2014).CAS 
Article 

Google Scholar 
Rees, H. C. et al. The application of eDNA for monitoring of the Great Crested Newt in the UK. Ecol. Evol. 4, 4023–4032 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, C. et al. Research on the biodiversity of Qinhuai River based on environmental DNA metabacroding. Acta Ecol. Sin. 42, 611–624 (2022).Article 

Google Scholar 
Deiner, K., Walser, J.-C., Mächler, E. & Altermatt, F. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol. Cons. 183, 53–63 (2015).Article 

Google Scholar 
Thomsen, P. F. et al. Monitoring endangered freshwater biodiversity using environmental DNA. Mol. Ecol. 21, 2565–2573 (2012).CAS 
PubMed 
Article 

Google Scholar 
Miralles, L., Parrondo, M., Hernandez de Rojas, A., Garcia-Vazquez, E. & Borrell, Y. J. Development and validation of eDNA markers for the detection of Crepidula fornicata in environmental samples. Mar. Pollut. Bull. 146, 827–830 (2019).CAS 
PubMed 
Article 

Google Scholar 
Takahara, T., Minamoto, T., Yamanaka, H., Doi, H. & Kawabata, Z. Estimation of fish biomass using environmental DNA. PLoS ONE 7, e35868 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Aglieri, G. et al. Environmental DNA effectively captures functional diversity of coastal fish communities. Mol. Ecol. 30, 3127–3139 (2020).PubMed 
Article 

Google Scholar 
Yang, H. et al. Effectiveness assessment of using riverine water eDNA to simultaneously monitor the riverine and riparian biodiversity information. Sci. Rep. 11, 24241 (2021).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Altermatt, F. et al. Uncovering the complete biodiversity structure in spatial networks: the example of riverine systems. Oikos 129, 607–618 (2020).Article 

Google Scholar 
Stat, M. et al. Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv. Biol. 33, 196–205 (2019).PubMed 
Article 

Google Scholar 
Hallam, J., Clare, E. L., Jones, J. I. & Day, J. J. Biodiversity assessment across a dynamic riverine system: A comparison of eDNA metabarcoding versus traditional fish surveying methods. Environ. DNA 3, 1247–1266 (2021).Article 

Google Scholar 
Gao, W. Beijing Vertebrate Key (Beijing Publishing House, 1994).
Google Scholar 
Wang, H. Beijing Fish and Amphibians and Reptiles (Beijing Publishing House, 1994).
Google Scholar 
Chen, W., Hu, D. & Fu, B. Research on Biodiversity of Beijing Wetland (Science Press, 2007).
Google Scholar 
Zhang, C. et al. Fish species diversity and conservation in Beijing and adjacent areas. Biodivers. Sci. 19, 597–604 (2011).Article 

Google Scholar 
Yamamoto, S. et al. Environmental DNA metabarcoding reveals local fish communities in a species-rich coastal sea. Sci. Rep. 7, 40368 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shaw, J. L. A. et al. Comparison of environmental DNA metabarcoding and conventional fish survey methods in a river system. Biol. Cons. 197, 131–138 (2016).Article 

Google Scholar 
Fu, M., Xiao, N., Zhao, Z., Gao, X. & Li, J. Effects of Urbanization on Ecosystem Services in Beijing. Res. Soil Water Conserv. 23, 235–239 (2016).
Google Scholar 
Hao, L. & Sun, G. Impacts of urbanization on watershed ecohydrological processes: progresses and perspectives. Acta Ecol. Sin. 41, 13–26 (2021).
Google Scholar 
Su, G. et al. Human impacts on global freshwater fish biodiversity. Science 371, 835–838 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Yan, B. et al. Effects of urban development on soil microbial functional diversity in Beijing. Res. Environ. Sci. 29, 1325–1335 (2016).CAS 

Google Scholar 
Xiao, N., Gao, X., Li, J. & Bai, J. Evaluation and Conservation Measures of Beijing Biodiversity (China Forestry Publishing House, 2018).
Google Scholar 
Xu, S., Wang, Z., Liang, J. & Zhang, S. Use of different sampling tools for comparison of fish-aggregating effects along horizontal transect at two artificial reef sites in Shengsi. J. Fish. China 40, 820–831 (2016).
Google Scholar 
Miya, M. et al. MiFish, a set of universal PCR primers for metabarcoding environmental DNA from fishes: detection of more than 230 subtropical marine species. R. Soc. Open Sci. 2, 150088 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics (Oxford, England) 30, 614–620 (2014).CAS 
Article 

Google Scholar 
Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics (Oxford, England) 34, 884–890 (2018).Article 
CAS 

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

Google Scholar 
Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Iwasaki, W. et al. MitoFish and MitoAnnotator: A mitochondrial genome database of fish with an accurate and automatic annotation pipeline. Mol. Biol. Evol. 30, 2531–2540 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, H. Beijing Fish Records (Beijing Publishing House, 1984).
Google Scholar 
Du, L. et al. Fish community characteristics and spatial pattern in major rivers of Beijing City. Res. Environ. Sci. 32, 447–457 (2019).
Google Scholar 
Shen, W. & Ren, H. TaxonKit: A practical and efficient NCBI taxonomy toolkit. J. Genet. Genomics 48, 844–850 (2021).PubMed 
Article 

Google Scholar 
Karr, J. R. Assessment of biotic integrity using fish communities. Fisheries 6, 21–27 (1981).Article 

Google Scholar 
Zhang, C. & Zhao, Y. Fishes in Beijing and Adjacent Areas (China. Science Press, 2013).
Google Scholar 
Wu, H. & Zhong, J. Fauna Sinica, Osteichthyes, Perciformess(Five),Gobioidei (Science Press, 2008).
Google Scholar 
Di, Y. et al. Distribution of fish communities and its influencing factors in the Nansha and Beijing sub-center reaches of the Beiyun River. Acta Sci. Circumst. 41, 156–163 (2020).
Google Scholar 
Walters, D. M., Freeman, M. C., Leigh, D. S., Freeman, B. J. & Pringle, C. M. in Effects of Urbanization on Stream Ecosystems Vol. 47 American Fisheries Society Symposium 69–85 (2005).Hu, X., Zuo, D., Liu, B., Huang, Z. & Xu, Z. Quantitative analysis of the correlation between macrobenthos community and water environmental factors and aquatic ecosystem health assessment in the North Canal River Basin of Beijing. Environ. Sci. 43, 247–255 (2022).
Google Scholar 
Kadye, W. T., Magadza, C. H. D., Moyo, N. A. G. & Kativu, S. Stream fish assemblages in relation to environmental factors on a montane plateau (Nyika Plateau, Malawi). Environ. Biol. Fishes 83, 417–428 (2008).Article 

Google Scholar 
Smith, T. A. & Kraft, C. E. Stream fish assemblages in relation to landscape position and local habitat variables. Trans. Am. Fish. Soc. 134, 430–440 (2005).Article 

Google Scholar 
Blabolil, P. et al. Environmental DNA metabarcoding uncovers environmental correlates of fish communities in spatially heterogeneous freshwater habitats. Ecol. Ind. 126, 107698 (2021).CAS 
Article 

Google Scholar 
Xie, R. et al. eDNA metabarcoding revealed differential structures of aquatic communities in a dynamic freshwater ecosystem shaped by habitat heterogeneity. Environ. Res. 201, 111602 (2021).CAS 
PubMed 
Article 

Google Scholar 
Qu, C. et al. Comparing fish prey diversity for a critically endangered aquatic mammal in a reserve and the wild using eDNA metabarcoding. Sci. Rep. 10, 16715 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Pont, D. et al. Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. Sci. Rep. 8, 10361 (2018).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Doble, C. J. et al. Testing the performance of environmental DNA metabarcoding for surveying highly diverse tropical fish communities: A case study from Lake Tanganyika. Environ. DNA 2, 24–41 (2020).Article 

Google Scholar 
Xu, N. et al. Monitoring seasonal distribution of an endangered anadromous sturgeon in a large river using environmental DNA. Sci. Nat. 105, 62 (2018).Article 
CAS 

Google Scholar 
Laramie, M. B., Pilliod, D. S. & Goldberg, C. S. Characterizing the distribution of an endangered salmonid using environmental DNA analysis. Biol. Cons. 183, 29–37 (2015).Article 

Google Scholar 
Harper, L. R. et al. Development and application of environmental DNA surveillance for the threatened crucian carp (Carassius carassius). Freshw. Biol. 64, 93–107 (2019).CAS 
Article 

Google Scholar 
Ushio, M. et al. Quantitative monitoring of multispecies fish environmental DNA using high-throughput sequencing. Metabarcoding Metagenomics 2, e2329 (2018).
Google Scholar 
Evans, N. T. et al. Quantification of mesocosm fish and amphibian species diversity via environmental DNA metabarcoding. Mol. Ecol. Resour. 16, 29–41 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Harrison, J. B., Sunday, J. M. & Rogers, S. M. Predicting the fate of eDNA in the environment and implications for studying biodiversity. Proc. Biol. Sci. 286, 20191409 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Kelly, R. P., Shelton, A. O. & Gallego, R. Understanding PCR processes to draw meaningful conclusions from environmental DNA studies. Sci. Rep. 9, 12133 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Civade, R. et al. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS ONE 11, e0157366 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barnes, M. A. et al. Environmental conditions influence eDNA persistence in aquatic systems. Environ. Sci. Technol. 48, 1819–1827 (2014).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Shogren, A. J. et al. Water flow and biofilm cover influence environmental DNA detection in recirculating streams. Environ. Sci. Technol. 52, 8530–8537 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
Zhao, B., van Bodegom, P. M. & Trimbos, K. The particle size distribution of environmental DNA varies with species and degradation. Sci. Total Environ. 797, 149175 (2021).ADS 
CAS 
PubMed 
Article 

Google Scholar  More

Fink, D. et al. Crowdsourcing meets ecology: he misphere wide spatiotemporal species distribution models. AI Mag. 35, 19–30. https://doi.org/10.1609/aimag.v35i2.2533 (2014).Article 

Google Scholar 
Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Cons. 213, 280–294. https://doi.org/10.1016/j.biocon.2016.09.004 (2017).Article 

Google Scholar 
Schmeller, D. S. et al. Advantages of volunteer-based biodiversity monitoring in Europe. Conserv. Biol. 23, 307–316. https://doi.org/10.1111/j.1523-1739.2008.01125.x (2009).Article 
PubMed 

Google Scholar 
Boakes, E. H. et al. Distorted views of biodiversity: Spatial and temporal bias in species occurrence data. PLoS Biol. https://doi.org/10.1371/journal.pbio.1000385 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Follett, R. & Strezov, V. An analysis of citizen science based research: Usage and publication patterns. PLoS ONE https://doi.org/10.1371/journal.pone.0143687 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Zattara, E. E. & Aizen, M. A. Worldwide occurrence records suggest a global decline in bee species richness. One Earth 4, 114–123. https://doi.org/10.1016/j.oneear.2020.12.005 (2021).ADS 
Article 

Google Scholar 
Dickinson, J. L. et al. The current state of citizen science as a tool for ecological research and public engagement. Front. Ecol. Environ. 10, 291–297. https://doi.org/10.1890/110236 (2012).Article 

Google Scholar 
Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560. https://doi.org/10.1002/fee.1436 (2016).Article 

Google Scholar 
Bayraktarov, E. et al. Do big unstructured biodiversity data mean more knowledge?. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2018.00239 (2019).Article 

Google Scholar 
Burgess, H. K. et al. The science of citizen science: Exploring barriers to use as a primary research tool. Biol. Cons. 208, 113–120. https://doi.org/10.1016/j.biocon.2016.05.014 (2017).Article 

Google Scholar 
Isaac, N. J. B. & Pocock, M. J. O. Bias and information in biological records. Biol. J. Lin. Soc. 115, 522–531. https://doi.org/10.1111/bij.12532 (2015).Article 

Google Scholar 
August, T., Fox, R., Roy, D. B. & Pocock, M. J. O. Data-derived metrics describing the behaviour of field-based citizen scientists provide insights for project design and modelling bias. Sci. Rep. https://doi.org/10.1038/s41598-020-67658-3 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Boakes, E. H. et al. Patterns of contribution to citizen science biodiversity projects increase understanding of volunteers’ recording behaviour. Sci. Rep. https://doi.org/10.1038/srep33051 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Di Cecco, G. J. et al. Observing the observers: How participants contribute data to iNaturalist and implications for biodiversity science. Bioscience 71, 1179–1188. https://doi.org/10.1093/biosci/biab093 (2021).Article 

Google Scholar 
Kamp, J., Oppel, S., Heldbjerg, H., Nyegaard, T. & Donald, P. F. Unstructured citizen science data fail to detect long-term population declines of common birds in Denmark. Divers. Distrib. 22, 1024–1035. https://doi.org/10.1111/ddi.12463 (2016).Article 

Google Scholar 
Altwegg, R. & Nichols, J. D. Occupancy models for citizen-science data. Methods Ecol. Evol. 10, 8–21. https://doi.org/10.1111/2041-210x.13090 (2019).Article 

Google Scholar 
Courter, J. R., Johnson, R. J., Stuyck, C. M., Lang, B. A. & Kaiser, E. W. Weekend bias in citizen science data reporting: Implications for phenology studies. Int. J. Biometeorol. 57, 715–720. https://doi.org/10.1007/s00484-012-0598-7 (2013).ADS 
Article 
PubMed 

Google Scholar 
Amano, T., Lamming, J. D. L. & Sutherland, W. J. Spatial gaps in global biodiversity information and the role of citizen science. Bioscience 66, 393–400. https://doi.org/10.1093/biosci/biw022 (2016).Article 

Google Scholar 
Geldmann, J. et al. What determines spatial bias in citizen science? Exploring four recording schemes with different proficiency requirements. Divers. Distrib. 22, 1139–1149. https://doi.org/10.1111/ddi.12477 (2016).Article 

Google Scholar 
Girardello, M. et al. Gaps in butterfly inventory data: A global analysis. Biol. Cons. 236, 289–295. https://doi.org/10.1016/j.biocon.2019.05.053 (2019).Article 

Google Scholar 
Husby, M., Hoset, K. S. & Butler, S. Non-random sampling along rural-urban gradients may reduce reliability of multi-species farmland bird indicators and their trends. Ibis https://doi.org/10.1111/ibi.12896 (2021).Article 

Google Scholar 
Petersen, T. K., Speed, J. D. M., Grøtan, V. & Austrheim, G. Species data for understanding biodiversity dynamics: The what, where and when of species occurrence data collection. Ecol. Solut. Evid. https://doi.org/10.1002/2688-8319.12048 (2021).Article 

Google Scholar 
Egerer, M., Lin, B. B. & Kendal, D. Towards better species identification processes between scientists and community participants. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2019.133738 (2019).Article 
PubMed 

Google Scholar 
Jimenez, M. F., Pejchar, L. & Reed, S. E. Tradeoffs of using place-based community science for urban biodiversity monitoring. Conserv. Sci. Pract. https://doi.org/10.1111/csp2.338 (2021).Article 

Google Scholar 
Branchini, S. et al. Using a citizen science program to monitor coral reef biodiversity through space and time. Biodivers. Conserv. 24, 319–336. https://doi.org/10.1007/s10531-014-0810-7 (2015).Article 

Google Scholar 
Snall, T., Kindvall, O., Nilsson, J. & Part, T. Evaluating citizen-based presence data for bird monitoring. Biol. Cons. 144, 804–810. https://doi.org/10.1016/j.biocon.2010.11.010 (2011).Article 

Google Scholar 
Gardiner, M. M. et al. Lessons from lady beetles: Accuracy of monitoring data from US and UK citizen-science programs. Front. Ecol. Environ. 10, 471–476. https://doi.org/10.1890/110185 (2012).Article 

Google Scholar 
Troudet, J., Grandcolas, P., Blin, A., Vignes-Lebbe, R. & Legendre, F. Taxonomic bias in biodiversity data and societal preferences. Sci. Rep. https://doi.org/10.1038/s41598-017-09084-6 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Johansson, F. et al. Can information from citizen science data be used to predict biodiversity in stormwater ponds?. Sci. Rep. https://doi.org/10.1038/s41598-020-66306-0 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Everett, G. & Geoghegan, H. Initiating and continuing participation in citizen science for natural history. BMC Ecol. https://doi.org/10.1186/s12898-016-0062-3 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Richter, A. et al. The social fabric of citizen science drivers for long-term engagement in the German butterfly monitoring scheme. J. Insect Conserv. 22, 731–743. https://doi.org/10.1007/s10841-018-0097-1 (2018).Article 

Google Scholar 
MacPhail, V. J., Gibson, S. D. & Colla, S. R. Community science participants gain environmental awareness and contribute high quality data but improvements are needed: Insights from Bumble Bee Watch. PeerJ https://doi.org/10.7717/peerj.9141 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Maund, P. R. et al. What motivates the masses: Understanding why people contribute to conservation citizen science projects. Biol. Conserv. https://doi.org/10.1016/j.biocon.2020.108587 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Moczek, N., Nuss, M. & Kohler, J. K. Volunteering in the citizen science project “Insects of Saxony”—The larger the island of knowledge, the longer the bank of questions. Insects https://doi.org/10.3390/insects12030262 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Branchini, S. et al. Participating in a citizen science monitoring program: Implications for environmental education. PLoS ONE https://doi.org/10.1371/journal.pone.0131812 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
Kelemen-Finan, J., Scheuch, M. & Winter, S. Contributions from citizen science to science education: An examination of a biodiversity citizen science project with schools in Central Europe. Int. J. Sci. Educ. 40, 2078–2098. https://doi.org/10.1080/09500693.2018.1520405 (2018).Article 

Google Scholar 
Deguines, N., Prince, K., Prevot, A. C. & Fontaine, B. Assessing the emergence of pro-biodiversity practices in citizen scientists of a backyard butterfly survey. Sci. Total Environ. https://doi.org/10.1016/j.scitotenv.2020.136842 (2020).Article 
PubMed 

Google Scholar 
Peter, M., Diekötter, T., Höffler, T. & Kremer, K. Biodiversity citizen science: Outcomes for the participating citizens. People Nat. 3, 294–311. https://doi.org/10.1002/pan3.10193 (2021).Article 

Google Scholar 
Phillips, T. B., Bailey, R. L., Martin, V., Faulkner-Grant, H. & Bonter, D. N. The role of citizen science in management of invasive avian species: What people think, know, and do. J. Environ. Manage. https://doi.org/10.1016/j.jenvman.2020.111709 (2021).Article 
PubMed 

Google Scholar 
Parrish, J. K. et al. Hoping for optimality or designing for inclusion: Persistence, learning, and the social network of citizen science. Proc. Natl. Acad. Sci. U.S.A. 116, 1894–1901. https://doi.org/10.1073/pnas.1807186115 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mac Domhnaill, C., Lyons, S. & Nolan, A. The citizens in citizen science: Demographic, socioeconomic, and health characteristics of biodiversity recorders in Ireland. Citiz. Sci.: Theory Pract. 5, 16. https://doi.org/10.5334/cstp.283 (2020).Article 

Google Scholar 
van der Wal, R., Sharma, N., Mellish, C., Robinson, A. & Siddharthan, A. The role of automated feedback in training and retaining biological recorders for citizen science. Conserv. Biol. 30, 550–561. https://doi.org/10.1111/cobi.12705 (2016).Article 
PubMed 

Google Scholar 
Bloom, E. H. & Crowder, D. W. Promoting data collection in pollinator citizen science projects. Citiz. Sci.: Theory Pract. 5, 3. https://doi.org/10.5334/cstp.217 (2020).Article 

Google Scholar 
Johnston, A., Fink, D., Hochachka, W. M. & Kelling, S. Estimates of observer expertise improve species distributions from citizen science data. Methods Ecol. Evol. 9, 88–97. https://doi.org/10.1111/2041-210x.12838 (2018).Article 

Google Scholar 
Kelling, S. et al. Using semistructured surveys to improve citizen science data for monitoring biodiversity. Bioscience 69, 170–179. https://doi.org/10.1093/biosci/biz010 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Koen, B., Loosveldt, G., Vandenplas, C. & Stoop, I. Response rates in the european social survey: Increasing, decreasing, or a matter of fieldwork efforts?. Surv. Methods: Insights Field https://doi.org/10.13094/SMIF-2018-00003 (2018).Article 

Google Scholar 
Gideon, L. Handbook of Survey Methodology for the Social Sciences (Springer, 2012).Book 

Google Scholar 
Wolf, C., Joye, D., Smith, T. W. & Fu, Y. C. The SAGE Handbook of Survey Methodology (SAGE Publications Ltd, 2016).Book 

Google Scholar 
Richter, A. et al. Motivation and support services in citizen science insect monitoring: A cross-country study. Biol. Conserv. 263, 109325. https://doi.org/10.1016/j.biocon.2021.109325 (2021).Article 

Google Scholar 
Johnston, A., Moran, N., Musgrove, A., Fink, D. & Baillie, S. R. Estimating species distributions from spatially biased citizen science data. Ecol. Model. https://doi.org/10.1016/j.ecolmodel.2019.108927 (2020).Article 

Google Scholar 
Isaac, N. J. B., van Strien, A. J., August, T. A., de Zeeuw, M. P. & Roy, D. B. Statistics for citizen science: Extracting signals of change from noisy ecological data. Methods Ecol. Evol. 5, 1052–1060. https://doi.org/10.1111/2041-210x.12254 (2014).Article 

Google Scholar 
Liao, H.-I., Yeh, S.-L. & Shimojo, S. Novelty vs. familiarity principles in preference decisions: Task context of past experience matters. Front. Psychol. https://doi.org/10.3389/fpsyg.2011.00043 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
Park, J., Shimojo, E. & Shimojo, S. Roles of familiarity and novelty in visual preference judgments are segregated across object categories. Proc. Natl. Acad. Sci. U.S.A. 107, 14552–14555. https://doi.org/10.1073/pnas.1004374107 (2010).ADS 
Article 
PubMed 

Google Scholar 
Tiago, P., Gouveia, M. J., Capinha, C., Santos-Reis, M. & Pereira, H. M. The influence of motivational factors on the frequency of participation in citizen science activities. Nat. Conserv.-Bulg. https://doi.org/10.3897/natureconservation.18.13429 (2017).Article 

Google Scholar 
Davis, A., Taylor, C. E. & Martin, J. M. Are pro-ecological values enough? Determining the drivers and extent of participation in citizen science programs. Hum. Dimens. Wildl. 24, 501–514. https://doi.org/10.1080/10871209.2019.1641857 (2019).Article 

Google Scholar 
Bell, S. et al. What counts? Volunteers and their organisations in the recording and monitoring of biodiversity. Biodivers. Conserv. 17, 3443–3454. https://doi.org/10.1007/s10531-008-9357-9 (2008).Article 

Google Scholar 
Toomey, A. H. & Domroese, M. C. Can citizen science lead to positive conservation attitudes and behaviors?. Hum. Ecol. Rev. 20, 50–62 (2013).Article 

Google Scholar 
Dennis, E. B., Morgan, B. J. T., Brereton, T. M., Roy, D. B. & Fox, R. Using citizen science butterfly counts to predict species population trends. Conserv. Biol. 31, 1350–1361. https://doi.org/10.1111/cobi.12956 (2017).Article 
PubMed 

Google Scholar 
Callaghan, C. T., Poore, A. G. B., Major, R. E., Rowley, J. J. L. & Cornwell, W. K. Optimizing future biodiversity sampling by citizen scientists. Proc. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rspb.2019.1487 (2019).Article 

Google Scholar 
Outhwaite, C. L., Gregory, R. D., Chandler, R. E., Collen, B. & Isaac, N. J. B. Complex long-term biodiversity change among invertebrates, bryophytes and lichens. Nat. Ecol. Evol. 4, 384. https://doi.org/10.1038/s41559-020-1111-z (2020).Article 
PubMed 

Google Scholar 
Bowler, D. E. et al. Winners and losers over 35 years of dragonfly and damselfly distributional change in Germany. Divers. Distrib. https://doi.org/10.1111/ddi.13274 (2021).Article 

Google Scholar  More

Falkowski, P. et al. The global carbon cycle: a test of our knowledge of Earth as a system. Science 290, 291–296 (2000).Article 

Google Scholar 
McMahon, S. & Parnell, J. Weighing the deep continental biosphere. FEMS Microbiol. Ecol. 87, 113–120 (2014).Article 

Google Scholar 
Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11, 707–717 (2018).Article 

Google Scholar 
Gleeson, T., Befus, K. M., Jasechko, S., Luijendijk, E. & Cardenas, M. B. The global volume and distribution of modern groundwater. Nat. Geosci. 9, 161–167 (2016).Article 

Google Scholar 
Stevanović, Z. Karst waters in potable water supply: a global scale overview. Environ. Earth Sci. 78, 662 (2019).Article 

Google Scholar 
Poulson, T. L. & White, W. B. The cave environment. Science 165, 971–981 (1969).Article 

Google Scholar 
Rusterholtz, K. J. & Mallory, L. M. Density, activity, and diversity of bacteria indigenous to a karstic aquifer. Microb. Ecol. 28, 79–99 (1994).Article 

Google Scholar 
Smith, H. J. et al. Impact of hydrologic boundaries on microbial planktonic and biofilm communities in shallow terrestrial subsurface environments. FEMS Microbiol. Ecol. 94, fiy191 (2018).
Google Scholar 
Alexander, M. Introduction to Soil Microbiology (Wiley, 1977).Griebler, C. & Lueders, T. Microbial biodiversity in groundwater ecosystems. Freshw. Biol. 54, 649–677 (2009).Article 

Google Scholar 
Krumholz, L. R., McKinley, J. P., Ulrich, G. A. & Suflita, J. M. Confined subsurface microbial communities in Cretaceous rock. Nature 386, 64–66 (1997).Article 

Google Scholar 
Probst, A. J. et al. Differential depth distribution of microbial function and putative symbionts through sediment-hosted aquifers in the deep terrestrial subsurface. Nat. Microbiol. 3, 328–336 (2018).Article 

Google Scholar 
Magnabosco, C. et al. A metagenomic window into carbon metabolism at 3 km depth in Precambrian continental crust. ISME J. 10, 730–741 (2016).Article 

Google Scholar 
Stevens, T. O. & McKinley, J. P. Lithoautotrophic microbial ecosystems in deep basalt aquifers. Science 270, 450–455 (1995).Article 

Google Scholar 
Tiago, I. & Veríssimo, A. Microbial and functional diversity of a subterrestrial high pH groundwater associated to serpentinization. Environ. Microbiol. 15, 1687–1706 (2013).Article 

Google Scholar 
Mccollom, T. M. & Amend, J. P. A thermodynamic assessment of energy requirements for biomass synthesis by chemolithoautotrophic micro-organisms in oxic and anoxic environments. Geobiology 3, 135–144 (2005).Article 

Google Scholar 
Momper, L., Jungbluth, S. P., Lee, M. D. & Amend, J. P. Energy and carbon metabolisms in a deep terrestrial subsurface fluid microbial community. ISME J. 11, 2319–2333 (2017).Article 

Google Scholar 
Jewell, T. N. M., Karaoz, U., Brodie, E. L., Williams, K. H. & Beller, H. R. Metatranscriptomic evidence of pervasive and diverse chemolithoautotrophy relevant to C, S, N and Fe cycling in a shallow alluvial aquifer. ISME J. 10, 2106–2117 (2016).Article 

Google Scholar 
Herrmann, M., Rusznyák, A. & Akob, D. M. Large fractions of CO2-fixing microorganisms in pristine limestone aquifers appear to be involved in the oxidation of reduced sulfur and nitrogen compounds. Appl. Environ. Microbiol. 81, 2384–2394 (2015).Peterson, B. J. Aquatic primary productivity and the 14C–CO2 method: a history of the productivity problem. Annu. Rev. Ecol. Syst. 11, 359–385 (1980).Article 

Google Scholar 
Viviani, D. A., Karl, D. M. & Church, M. J. Variability in photosynthetic production of dissolved and particulate organic carbon in the North Pacific Subtropical Gyre. Front. Mar. Sci. 2, 73 (2015).Article 

Google Scholar 
Kohlhepp, B. et al. Aquifer configuration and geostructural links control the groundwater quality in thin-bedded carbonate–siliciclastic alternations of the Hainich CZE, central Germany. Hydrol. Earth Syst. Sci. 21, 6091–6116 (2017).Article 

Google Scholar 
Pedersen, K. & Ekendahl, S. Assimilation of CO2 and introduced organic compounds by bacterial communities in groundwater from southeastern Sweden deep crystalline bedrock. Microb. Ecol. 23, 1–14 (1992).Article 

Google Scholar 
Partensky, F. & Garczarek, L. Prochlorococcus: advantages and limits of minimalism. Ann. Rev. Mar. Sci. 2, 305–331 (2010).Article 

Google Scholar 
Karl, D. M., Hebel, D. V., Björkman, K. & Letelier, R. M. The role of dissolved organic matter release in the productivity of the oligotrophic North Pacific Ocean. Limnol. Oceanogr. 43, 1270–1286 (1998).Article 

Google Scholar 
Liang, Y. et al. Estimating primary production of picophytoplankton using the carbon-based ocean productivity model: a preliminary study. Front. Microbiol. 8, 1926 (2017).Article 

Google Scholar 
Steinberg, D. K. et al. Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep Sea Res. 2 48, 1405–1447 (2001).Article 

Google Scholar 
Gundersen, K., Orcutt, K. M., Purdie, D. A., Michaels, A. F. & Knap, A. H. Particulate organic carbon mass distribution at the Bermuda Atlantic Time-series Study (BATS) site. Deep Sea Res. 2 48, 1697–1718 (2001).Article 

Google Scholar 
Karl, D. M. & Lukas, R. The Hawaii Ocean Time-series (HOT) program: background, rationale and field implementation. Deep Sea Res. 2 43, 129–156 (1996).Article 

Google Scholar 
Martiny, A. C., Vrugt, J. A. & Lomas, M. W. Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Sci. Data 1, 140048 (2014).Article 

Google Scholar 
Martiny, A. C., Vrugt, J. A. & Lomas, M. W. Data from: Concentrations and ratios of particulate organic carbon, nitrogen, and phosphorus in the global ocean. Dryad https://doi.org/10.5061/dryad.d702p (2015).Schwab, V. F. et al. 14C-free carbon Is a major contributor to cellular biomass in geochemically distinct groundwater of shallow sedimentary bedrock aquifers. Water Resour. Res. 55, 2104–2121 (2019).Article 

Google Scholar 
Taubert, M. et al. Bolstering fitness via CO2 fixation and organic carbon uptake: mixotrophs in modern groundwater. ISME J 16, 1153–1162 (2022).Article 

Google Scholar 
Rimstidt, J. D. & Vaughan, D. J. Pyrite oxidation: a state-of-the-art assessment of the reaction mechanism. Geochim. Cosmochim. Acta 67, 873–880 (2003).Article 

Google Scholar 
Lin, W. et al. Genomic insights into the uncultured genus “Candidatus Magnetobacterium” in the phylum Nitrospirae. ISME J. 8, 2463–2477 (2014).Article 

Google Scholar 
Kato, S. et al. Genome-enabled metabolic reconstruction of dominant chemosynthetic colonizers in deep-sea massive sulfide deposits. Environ. Microbiol. 20, 862–877 (2018).Article 

Google Scholar 
Anantharaman, K. et al. Thousands of microbial genomes shed light on interconnected biogeochemical processes in an aquifer system. Nat. Commun. 7, 13219 (2016).Article 

Google Scholar 
Kojima, H., Watanabe, T. & Fukui, M. Sulfuricaulis limicola gen. nov., sp. nov., a sulfur oxidizer isolated from a lake. Int. J. Syst. Evol. Microbiol. 66, 266–270 (2016).Article 

Google Scholar 
Strous, M., Van Gerven, E., Kuenen, J. G. & Jetten, M. Effects of aerobic and microaerobic conditions on anaerobic ammonium-oxidizing (anammox) sludge. Appl. Environ. Microbiol. 63, 2446–2448 (1997).Article 

Google Scholar 
Ji, X., Wu, Z., Sung, S. & Lee, P.-H. Metagenomics and metatranscriptomics analyses reveal oxygen detoxification and mixotrophic potentials of an enriched anammox culture in a continuous stirred-tank reactor. Water Res. 166, 115039 (2019).Article 

Google Scholar 
Dalsgaard, T. et al. Oxygen at nanomolar levels reversibly suppresses process rates and gene expression in anammox and denitrification in the oxygen minimum zone off northern Chile. mBio 5, e01966 (2014).Article 

Google Scholar 
Smith, R. L., Böhlke, J. K., Song, B. & Tobias, C. R. Role of anaerobic ammonium oxidation (anammox) in nitrogen removal from a freshwater aquifer. Environ. Sci. Technol. 49, 12169–12177 (2015).Article 

Google Scholar 
Strous, M., Heijnen, J. J., Kuenen, J. G. & Jetten, M. S. M. The sequencing batch reactor as a powerful tool for the study of slowly growing anaerobic ammonium-oxidizing microorganisms. Appl. Microbiol. Biotechnol. 50, 589–596 (1998).Article 

Google Scholar 
Kits, K. D. et al. Kinetic analysis of a complete nitrifier reveals an oligotrophic lifestyle. Nature 549, 269–272 (2017).Article 

Google Scholar 
Rittmann, B. E. & McCarty, P. L. Environmental Biotechnology: Principles and Applications (McGraw-Hill Education, 2001).Zhang, Y. et al. Nitrifier adaptation to low energy flux controls inventory of reduced nitrogen in the dark ocean. Proc. Natl. Acad. Sci. USA 117, 4823–4830 (2020).Article 

Google Scholar 
Field, C. B., Behrenfeld, M. J., Randerson, J. T. & Falkowski, P. Primary production of the biosphere: integrating terrestrial and oceanic components. Science 281, 237–240 (1998).Article 

Google Scholar 
Lehmann, R. & Totsche, K. U. Multi-directional flow dynamics shape groundwater quality in sloping bedrock strata. J. Hydrol. 580, 124291 (2020).Article 

Google Scholar 
Küsel, K. et al. How deep can surface signals be traced in the Critical Zone? Merging biodiversity with biogeochemistry research in a central German Muschelkalk landscape. Front. Earth Sci. 4, 32 (2016).Article 

Google Scholar 
Yan, L. et al. Environmental selection shapes the formation of near-surface groundwater microbiomes. Water Res. 170, 115341 (2019).Article 

Google Scholar 
Pack, M. A. et al. A method for measuring methane oxidation rates using low levels of 14C-labeled methane and accelerator mass spectrometry: methane oxidation rates by AMS. Limnol. Oceanogr. Methods 9, 245–260 (2011).Article 

Google Scholar 
Nielsen, E. S. The use of radio-active carbon (C14) for measuring organic production in the sea. ICES J. Mar. Sci. 18, 117–140 (1952).Article 

Google Scholar 
Xu, X. et al. Modifying a sealed tube zinc reduction method for preparation of AMS graphite targets: reducing background and attaining high precision. Nucl. Instrum. Methods Phys. Res. B 259, 320–329 (2007).Article 

Google Scholar 
Merser, S. Acetabulum online interactive statistical calculators. Accessed Feb, 2021. https://acetabulum.dk/anova.htmlBermuda Oceanographic Timeseries, accessed 21 Oct 2020, http://batsftp.bios.edu/BATS/production/bats_primary_production.txtHawaiian Oceanographic Timeseries, accessed 21 Oct 2020, ftp://ftp.soest.hawaii.edu/hot/primary_productionHawaiian Oceanographic Timeseries, accessed 21 Oct 2020, https://hahana.soest.hawaii.edu/FTP/hot/microscopy/EPIslides.txtKumar, S. et al. Nitrogen loss from pristine carbonate-rock aquifers of the Hainich Critical Zone Exploratory (Germany) is primarily driven by chemolithoautotrophic anammox processes. Front. Microbiol. 8, 1951 (2017).Article 

Google Scholar 
Füssel, J. et al. Nitrite oxidation in the Namibian oxygen minimum zone. ISME J. 6, 1200–1209 (2012).Article 

Google Scholar 
McIlvin, M. R. & Altabet, M. A. Chemical conversion of nitrate and nitrite to nitrous oxide for nitrogen and oxygen isotopic analysis in freshwater and seawater. Anal. Chem. 77, 5589–5595 (2005).Article 

Google Scholar 
Dalsgaard, T., Thamdrup, B., Farías, L. & Revsbech, N. P. Anammox and denitrification in the oxygen minimum zone of the eastern South Pacific. Limnol. Oceanogr. 57, 1331–1346 (2012).Article 

Google Scholar 
Thamdrup, B. et al. Anaerobic ammonium oxidation in the oxygen-deficient waters off northern Chile. Limnol. Oceanogr. 51, 2145–2156 (2006).Article 

Google Scholar 
Taubert, M. et al. Tracking active groundwater microbes with D2O labelling to understand their ecosystem function. Environ. Microbiol. 20, 369–384 (2018).Article 

Google Scholar 
Bushnell, B. BBMap (SourceForge, 2014); http://sourceforge.net/projects/bbmapBornemann, T. L. V. et al. Geological degassing enhances microbial metabolism in the continental subsurface. Preprint at bioRxiv https://doi.org/10.1101/2020.03.07.980714 (2020).Nurk, S., Meleshko, D., Korobeynikov, A. & Pevzner, P. A. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27, 824–834 (2017).Article 

Google Scholar 
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC Bioinformatics 11, 119 (2010).Article 

Google Scholar 
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357 (2012).Article 

Google Scholar 
Wu, Y.-W., Simmons, B. A. & Singer, S. W. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics 32, 605–607 (2016).Article 

Google Scholar 
Brown, C. T. et al. Unusual biology across a group comprising more than 15% of domain bacteria. Nature 523, 208–211 (2015).Article 

Google Scholar 
Sieber, C. M. K. et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat. Microbiol. 3, 836–843 (2018).Article 

Google Scholar 
Olm, M. R., Brown, C. T., Brooks, B. & Banfield, J. F. dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication. ISME J. 11, 2864–2868 (2017).Article 

Google Scholar 
Murat Eren, A. et al. Anvi’o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3, e1319 (2015).Article 

Google Scholar 
Aramaki, T. et al. KofamKOALA: KEGG Ortholog assignment based on profile HMM and adaptive score threshold. Bioinformatics 36, 2251–2252 (2020).Article 

Google Scholar 
Graham, E. D., Heidelberg, J. F. & Tully, B. J. Potential for primary productivity in a globally-distributed bacterial phototroph. ISME J. 12, 1861–1866 (2018).Article 

Google Scholar 
Kanehisa, M., Sato, Y. & Morishima, K. BlastKOALA and GhostKOALA: KEGG tools for functional characterization of genome and metagenome sequences. J. Mol. Biol. 428, 726–731 (2016).Article 

Google Scholar 
Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).Article 

Google Scholar 
Pelikan, C. et al. Diversity analysis of sulfite- and sulfate-reducing microorganisms by multiplex dsrA and dsrB amplicon sequencing using new primers and mock community-optimized bioinformatics. Environ. Microbiol. 18, 2994–3009 (2016).Article 

Google Scholar 
Lücker, S., Nowka, B., Rattei, T., Spieck, E. & Daims, H. The genome of Nitrospina gracilis Illuminates the metabolism and evolution of the major marine nitrite oxidizer. Front. Microbiol. 4, 27 (2013).Article 

Google Scholar 
Orellana, L. H., Rodriguez-R, L. M. & Konstantinidis, K. T. ROCker: accurate detection and quantification of target genes in short-read metagenomic data sets by modeling sliding-window bitscores. Nucleic Acids Res. 45, e14 (2017).
Google Scholar 
Chaumeil, P.-A., Mussig, A. J., Hugenholtz, P. & Parks, D. H. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics 36, 1925–1927 (2020).
Google Scholar 
Parks, D. H. et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat. Biotechnol. 36, 996–1004 (2018).Article 

Google Scholar 
Parks, D. H. et al. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. https://doi.org/10.1038/s41587-020-0501-8 (2020).Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538 (2010).Article 

Google Scholar 
Jain, C., Rodriguez-R, L. M., Phillippy, A. M., Konstantinidis, K. T. & Aluru, S. High throughput ANI analysis of 90 K prokaryotic genomes reveals clear species boundaries. Nat. Commun. 9, 5114 (2018).Article 

Google Scholar 
Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2—approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).Article 

Google Scholar 
Eddy, S. R. Accelerated profile HMM searches. PLoS Comput. Biol. 7, e1002195 (2011).Article 

Google Scholar 
Ondov, B. D. et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 17, 132 (2016).Article 

Google Scholar 
Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL): an online tool for phylogenetic tree display and annotation. Bioinformatics 23, 127–128 (2007).Article 

Google Scholar 
Emiola, A. & Oh, J. High throughput in situ metagenomic measurement of bacterial replication at ultra-low sequencing coverage. Nat. Commun. 9, 4956 (2018).Article 

Google Scholar 
Wegner, C.-E. et al. Biogeochemical regimes in shallow aquifers reflect the metabolic coupling of the elements nitrogen, sulfur, and carbon. Appl. Environ. Microbiol. 85, e02346-18 (2019).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (R Core Team, 2018).RStudio: Integrated Development Environment for R (RStudio Team, 2016).Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Neuwirth, E. RColorBrewer: ColorBrewer Palettes. R package version 1.1-2. https://CRAN.R-project.org/package=RColorBrewer (2014). More

Gao, Y., Schofield, O. M. & Leustek, T. Characterization of sulfate assimilation in marine algae focusing on the enzyme 5′-adenylylsulfate reductase. Plant Physiol. 123, 1087–1096 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
Huang, C.-W., Walker, M. E., Fedrizzi, B., Gardner, R. C. & Jiranek, V. Hydrogen sulfide and its roles in Saccharomyces cerevisiae in a winemaking context. FEMS Yeast Res. 17, 058 (2017).
Google Scholar 
Kopriva, S., Calderwood, A., Weckopp, S. C. & Koprivova, A. Plant sulfur and big data. Plant Sci. 241, 1–10 (2015).CAS 
PubMed 

Google Scholar 
Shibagaki, N. & Grossman, A. The state of sulfur metabolism in algae: From ecology to genomics. In Sulfur Metabolism in Phototrophic Organisms (eds Hell, C. D. R. et al.) 231–267 (Springer, 2008).
Google Scholar 
Fakhraee, M. & Katsev, S. Organic sulfur was integral to the Archean sulfur cycle. Nat. Commun. 10, 1–8 (2019).CAS 

Google Scholar 
Ho, T. Y. et al. The elemental composition of some marine phytoplankton 1. J. Phycol. 39, 1145–1159 (2003).CAS 

Google Scholar 
Jørgensen, B. B. Unravelling the sulphur cycle of marine sediments. Environ. Microbiol. 21, 3533–3538 (2019).PubMed 

Google Scholar 
El Mahrad, B. et al. Social-environmental analysis for the management of coastal lagoons in North Africa. Front. Environ. Sci. 8, 37 (2020).
Google Scholar 
Srarfi, F. Etude géochimique et état de pollution de la lagune de Bizerte. These de doctorat, Univ. Tunis el Manar 122 (2007).FAO. La Situation Mondiale Des Pêches et de L’aquaculture 2020 (Food & Agriculture Organisation, 2020).
Google Scholar 
Soto, D. & Wurmann, C. The Future of Ocean Governance and Capacity Development 379–384 (Brill Nijhoff, 2019).
Google Scholar 
Ran, W. et al. Storage of starch and lipids in microalgae: Biosynthesis and manipulation by nutrients. Bioresour. Technol. 291, 121894 (2019).CAS 
PubMed 

Google Scholar 
Aikawa, S. et al. Improving polyglucan production in cyanobacteria and microalgae via cultivation design and metabolic engineering. Biotechnol. J. 10, 886–898 (2015).CAS 
PubMed 

Google Scholar 
Klok, A., Lamers, P., Martens, D., Draaisma, R. & Wijffels, R. Edible oils from microalgae: Insights in TAG accumulation. Trends Biotechnol. 32, 521–528 (2014).CAS 
PubMed 

Google Scholar 
Yuan, Y. et al. Enhancing carbohydrate productivity of Chlorella sp. AE10 in semi-continuous cultivation and unraveling the mechanism by flow cytometry. Appl. Biochem. 185, 419–433 (2018).CAS 

Google Scholar 
Rodríguez, M. C., Matulewicz, M. C., Noseda, M., Ducatti, D. & Leonardi, P. I. Agar from Gracilaria gracilis (Gracilariales, Rhodophyta) of the Patagonic coast of Argentina-Content, structure and physical properties. Biores. Technol. 100, 1435–1441 (2009).
Google Scholar 
Lee, W.-K. et al. Factors affecting yield and gelling properties of agar. J. Appl. Phycol. 29, 1527–1540 (2017).
Google Scholar 
Fethi, M. & Ghedifa, A. B. Optimum ranges of combined abiotic factor for Gracilaria gracilis aquaculture. J. Appl. Phycol. 31, 3025–3040 (2019).
Google Scholar 
Friedlander, M. Inorganic nutrition in pond cultivated Gracilaria conferta (Rhodophyta): Nitrogen, phosphate and sulfate. J. Appl. Phycol. 13, 279–286 (2001).CAS 

Google Scholar 
Lee, W.-K., Namasivayam, P. & Ho, C.-L. Effects of sulfate starvation on agar polysaccharides of Gracilaria species (Gracilariaceae, Rhodophyta) from Morib, Malaysia. J. Appl. Phycol. 26, 1791–1799 (2014).CAS 

Google Scholar 
Carfagna, S. et al. Impact of sulfur starvation in autotrophic and heterotrophic cultures of the extremophilic microalga Galdieria phlegrea (Cyanidiophyceae). Plant Cell Physiol. 57, 1890–1898 (2016).CAS 
PubMed 

Google Scholar 
Collén, P. N., Camitz, A., Hancock, R. D., Viola, R. & Pedersén, M. Effect of nutrient deprivation and resupply on metabolites and enzymes related to carbon allocation in gracilaria tenuistipitata (rhodophyta) 1. J. Phycol. 40, 305–314 (2004).
Google Scholar 
Collier, J. L. & Grossman, A. A small polypeptide triggers complete degradation of light-harvesting phycobiliproteins in nutrient-deprived cyanobacteria. EMBO J. 13, 1039–1047 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
Richaud, C., Zabulon, G., Joder, A. & Thomas, J.-C. Nitrogen or sulfur starvation differentially affects phycobilisome degradation and expression of the nblA gene in Synechocystis strain PCC 6803. J. Bacteriol. 183, 2989–2994 (2001).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lloyd, A. G., Dodgson, K. S. & Rose, F. A. Infrared studies on sulphate esters I. Polysaccharide sulphates. Biochim. Biophys. Acta 46, 108–115 (1961).CAS 
PubMed 

Google Scholar 
Kawachi, M. & Noël, M.-H. Sterilization and sterile technique. In Algal Culturing Techniques (ed. Anderson, R. A.) 65–81 (Academic Press, 2005).
Google Scholar 
Harrison, P. J. & Berges, J. A. Marine culture media. In Algal Culturing Techniques (ed. Anderson, R. A.) 21–34 (Academic Press, 2005).
Google Scholar 
Guiry, M. & Cunningham, E. Photoperiodic and temperature responses in the reproduction of north-eastern Atlantic Gigartina acicularis (Rhodophyta: Gigartinales). Phycologia 23, 357–367 (1984).
Google Scholar 
Kolmert, Å., Wikström, P. & Hallberg, K. B. A fast and simple turbidimetric method for the determination of sulfate in sulfate-reducing bacterial cultures. J. Microbiol. Methods 41, 179–184 (2000).CAS 
PubMed 

Google Scholar 
Destombe, C., Godin, J., Nocher, M., Richerd, S. & Valero, M. In Fourteenth International Seaweed Symposium (eds Brown, M. T. & Lahaye, M.) 131–137 (Springer, 1993).
Google Scholar 
Rueness, J. & Tananger, T. In Eleventh International Seaweed Symposium (eds Bird, C. J. & Ragan, M. A.) 303–307 (Springer, 1984).
Google Scholar 
Shea, R. & Chopin, T. Effects of germanium dioxide, an inhibitor of diatom growth, on the microscopic laboratory cultivation stage of the kelp, Laminaria saccharina. J. Appl. Phycol. 19, 27–32 (2007).CAS 

Google Scholar 
Dawes, C., Orduna-Rojas, J. & Robledo, D. Response of the tropical red seaweed Gracilaria cornea to temperature, salinity and irradiance. J. Appl. Phycol. 10, 419–425 (1998).
Google Scholar 
Yaphe, W. & Arsenault, G. Improved resorcinol reagent for the determination of fructose, and of 3, 6-anhydrogalactose in polysaccharides. Anal. Biochem. 13, 143–148 (1965).CAS 

Google Scholar 
Mensi, F., Ksouri, J., Seale, E., Romdhane, M. S. & Fleurence, J. A statistical approach for optimization of R-phycoerythrin extraction from the red algae Gracilaria verrucosa by enzymatic hydrolysis using central composite design and desirability function. J. Appl. Phycol. 24, 915–926 (2012).CAS 

Google Scholar 
Dubois, M., Gilles, K. A., Hamilton, J. K., Rebers, P. T. & Smith, F. Colorimetric method for determination of sugars and related substances. Anal. Chem. 28, 350–356 (1956).CAS 

Google Scholar 
Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 72, 248–254 (1976).CAS 
PubMed 

Google Scholar 
Sörbo, B. Sulfate: Turbidimetric and nephelometric methods. Methods Enzymol. 143, 3–6 (1987).PubMed 

Google Scholar 
Redmond, S., Green, L., Yarish, C., Kim, J. & Neefus, C. New England Seaweed Culture Handbook (University of Connecticut Sea Garent, 2014).
Google Scholar 
Kakita, H. & Kamishima, H. Effects of environmental factors and metal ions on growth of the red alga Gracilaria chorda Holmes (Gracilariales, Rhodophyta). J. Appl. Phycol. 18, 469–474 (2006).CAS 

Google Scholar 
Berges, J. A., Franklin, D. J. & Harrison, P. J. Evolution of an artificial seawater medium: Improvements in enriched seawater, artificial water over the last two decades. J. Phycol. 37, 1138–1145 (2001).
Google Scholar 
Shpigun, L. K., Kolotyrkina, I. Y. & Zolotov, Y. A. Experience with flow-injection analysis in marine chemical research. Anal. Chim. Acta 261, 307–314 (1992).CAS 

Google Scholar 
Cosano, J., de Castro, M. & Valcarcel, M. Flow injection analysis of water. Part 1: Automatic preconcentration determination of sulphate, ammonia and iron (II)/iron (III). J. Autom. Chem. 15, 141–146 (1993).CAS 

Google Scholar 
Van Staden, J. & Taljaard, R. Determination of sulphate in natural waters and industrial effluents by sequential injection analysis. Anal. Chim. Acta 331, 271–280 (1996).
Google Scholar 
Petersen, S. P. & Ahring, B. K. Analysis of sulfate in sewage sludge using ion chromatographic techniques. J. Microbiol. Methods 12, 225–230 (1990).CAS 

Google Scholar 
Rand, M., Greenberg, A., Taras, K. & Franson, M. Standard Methods for the Examination of Water and Waste Water (American Public Health Association, 1975).
Google Scholar 
Wanner, G., Henkelmann, G., Schmidt, A. & Köst, H.-P. Nitrogen and sulfur starvation of the cyanobacterium Synechococcus 6301 an ultrastructural, morphometrical, and biochemical comparison. Zeitschrift Naturforschung C 41, 741–750 (1986).CAS 

Google Scholar 
Molloy, F. & Bolton, J. The effect of season and depth on the growth of Gracilaria gracilis at Lüderitz, Namibia. Bot. Mar. 39, 407–414 (1996).
Google Scholar 
Mensi, F., Nasraoui, S., Bouguerra, S., Ben Ghedifa, A. & Chalghaf, M. Effect of lagoon and sea water depth on Gracilaria gracilis growth and biochemical composition in the northeast of Tunisia. Sci. Rep. 10, 1–12 (2020).
Google Scholar 
Mensi, F., Ksouri, J., Hammami, W. & Romdhane, M. État des connaissances et perspectives de recherches sur la culture de Gracilariales (Gracilaria et Gracilariopsis): Application a la lagune de Bizerte. Bull. Inst. Natn. Scien. Tech. Mer Salammbô 41, 101–119 (2014).
Google Scholar 
Sugimoto, K., Sato, N. & Tsuzuki, M. Utilization of a chloroplast membrane sulfolipid as a major internal sulfur source for protein synthesis in the early phase of sulfur starvation in Chlamydomonas reinhardtii. FEBS Lett. 581, 4519–4522 (2007).CAS 
PubMed 

Google Scholar 
Cakmak, T. et al. Nitrogen and sulfur deprivation differentiate lipid accumulation targets of Chlamydomonas reinhardtii. Bioengineered 3, 343–346 (2012).PubMed 
PubMed Central 

Google Scholar 
Ostaszewska-Bugajska, M., Rychter, A. M. & Juszczuk, I. M. Antioxidative and proteolytic systems protect mitochondria from oxidative damage in S-deficient Arabidopsis thaliana. J. Plant Physiol. 186, 25–38 (2015).PubMed 

Google Scholar 
Zhang, L. et al. Sulfur deficiency-induced glucosinolate catabolism attributed to two β-glucosidases, BGLU28 and BGLU30, is required for plant growth maintenance under sulfur deficiency. Plant Cell Physiol. 61, 803–813 (2020).CAS 
PubMed 

Google Scholar 
Takahashi, H., Kopriva, S., Giordano, M., Saito, K. & Hell, R. Sulfur assimilation in photosynthetic organisms: Molecular functions and regulations of transporters and assimilatory enzymes. Annu. Rev. Plant biol. 62, 157–184 (2011).CAS 
PubMed 

Google Scholar 
Butterfield, N. J. Bangiomorpha pubescens n. gen., n. sp.: Implications for the evolution of sex, multicellularity, and the Mesoproterozoic/Neoproterozoic radiation of eukaryotes. Paleobiology 26, 386–404 (2000).
Google Scholar 
Collier, J. L. & Grossman, A. R. Chlorosis induced by nutrient deprivation in Synechococcus sp. strain PCC 7942: Not all bleaching is the same. J. Bacteriol. 174, 4718–4726. https://doi.org/10.1128/jb.174.14.4718-4726.1992 (1992).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kaur, H. et al. Cys-Gly specific dipeptidase Dug1p from S. cerevisiae binds promiscuously to di-, tri-, and tetra-peptides: Peptide-protein interaction, homology modeling, and activity studies reveal a latent promiscuity in substrate recognition. Biochimie 93, 175–186. https://doi.org/10.1016/j.biochi.2010.09.008 (2011).CAS 
Article 
PubMed 

Google Scholar 
Said, R. B. et al. Effects of depth and initial fragment weights of Gracilaria gracilis on the growth, agar yield, quality, and biochemical composition. J. Appl. Phycol. 30, 2499–2512 (2018).
Google Scholar 
Bird, K. T. Agar production and quality from Gracilaria sp. strain G—16: Effects of environmental factors. Bot. Mar. 31, 33–38 (1988).
Google Scholar 
Cote, G. & Hanisak, M. Production and properties of native agars from Gracilaria tikvahiae and other red algae. Bot. Mar. 29, 359–366 (1986).CAS 

Google Scholar 
Lahaye, M. & Yaphe, W. Effects of seasons on the chemical structure and gel strength of Gracilaria pseudoverrucosa agar (Gracilariaceae, Rhodophyta). Carbohydr. Polym. 8, 285–301 (1988).CAS 

Google Scholar 
Yaphe, W. Eleventh International Seaweed Symposium 171–174 (Springer, 1984).
Google Scholar 
Duckworth, M., Hong, K. & Yaphe, W. The agar polysaccharides of Gracilaria species. Carbohydr. Res. 18, 1–9 (1971).CAS 

Google Scholar 
Rotem, A., Roth-Bejerano, N. & Arad, S. Effect of controlled environmental conditions on starch and agar contents of Gracilaria sp. (Rhodophyceae) 1. J. Phycol. 22, 117–121 (1986).CAS 

Google Scholar 
Arad, S. M., Lerental, Y. B. & Dubinsky, O. Effect of nitrate and sulfate starvation on polysaccharide formation in Rhodella reticulata. Bioresour. Technol. 42, 141–148 (1992).CAS 

Google Scholar  More

Mangrove forests thrive along tropical and subtropical shorelines and their distribution extends to warm temperate regions1. They are globally recognized for the valuable ecosystem services they provide2 but are expected to be substantially influenced by climate change-related physical processes in the future3,4. Under warming winter temperatures, poleward expansion is predicted for mangroves5,6, with potential implications for ecosystem structure and functioning, as well as human livelihoods and well-being7,8. The global distribution, abundance and species richness of mangroves is governed by a broad range of biotic and environmental factors, including temperature and precipitation9 and diverse geomorphological and hydrological gradients10. Climate and aspects related to coastal geography (for example, floodplain area) determine the availability of suitable habitat for establishment11,12. However, the potential for mangroves to track changing environmental conditions and expand their distributions ultimately depends on dispersal11,13. The importance of dispersal in controlling mangrove distributions has been demonstrated by mangrove distributional responses to historical climate variability14, past mangrove (re)colonization of oceanic islands15 and from the long-term survival of mangrove seedlings planted beyond natural range limits16. As such, quantifying changes in the factors that influence dispersal is important for understanding climate-driven distributional responses of mangroves under future climate conditions.In mangroves, dispersal is accomplished by buoyant seeds and fruits (hereafter referred to as ‘propagules’). In combination with prevailing currents, the spatial scale of this process, ranging from local retention to transoceanic dispersal over thousands of kilometres13, is determined by propagule buoyancy17, that is, the density difference between that of propagules and the surrounding water. Hence, the course of dispersal trajectories for propagules from these species depends on the interaction between spatiotemporal changes in both propagule density and that of the surrounding water, rendering this process sensitive to climate-driven changes in coastal and open-ocean water properties. The biogeographic implications of such density differences were recognized more than a century ago by Henry Brougham Guppy, who discussed18 ‘the far-reaching influence on plant-distribution and on plant-development that the relation between the specific weight of seeds and fruits and the density of sea-water must possess’.Since the time of Guppy’s early observations, climate change from human activities has driven pronounced changes in ocean temperature and salinity, with further changes predicted throughout the twenty-first century19. Ocean density is a nonlinear function of temperature, salinity and pressure20; therefore, these changes may influence dispersal patterns of mangrove propagules by altering their buoyancy and floating orientation. As Guppy noted18, ‘[for] plants whose seeds or fruits are not much lighter than seawater […] the effect of increased density of the water is to extend the flotation period’ or ‘to increase the number that floated for a given period’. Guppy also reported that the seedlings of the widespread mangrove genera Rhizophora and Bruguiera present exceptional examples of propagules with densities somewhere between seawater and freshwater18. Previous studies of the impacts of climate change on mangroves have focused on factors such as sea level rise, altered precipitation regimes and increasing temperature and storm frequency4,21,22,23 but the potential impact of climate-driven changes in seawater properties on mangroves has not yet been examined. This is somewhat surprising, as the ocean is the primary dispersal medium of this ‘sea-faring’ coastal vegetation and dispersal is a key process that governs a species’ response to climate change by changing its geographical range. This knowledge gap contrasts with recent efforts to expose links between climate change and dispersal in other ecologically important marine taxa such as zooplankton and fish species24,25,26,27.In this study, we investigate predicted changes in sea surface temperature (SST), sea surface salinity (SSS) and sea surface density (SSD) for coastal waters bordering mangrove forests (hereafter referred to as ‘coastal mangrove waters’), over the next century. Using a biogeographic classification system for coastal and shelf areas28, we examine spatiotemporal changes in these surface ocean properties, with a particular focus on the world’s two major mangrove diversity hotspots: (1) the Atlantic East Pacific (AEP) region, including all of the Americas, West and Central Africa and (2) the Indo West Pacific (IWP) region, extending from East Africa eastwards to the islands of the central Pacific1. Finally, we synthesize available data on the density of mangrove propagules for different mangrove species and explore the potential impact of climate-driven changes in SSD on propagule dispersal.To assess changes in SST and SSS throughout the global range of mangrove forests, we used present (2000–2014) and future (2090–2100) surface ocean properties from the Bio-ORACLE database29,30. SSD estimates were derived from these variables using the UNESCO EOS-80 equation of state polynomial for seawater31. Changes in SST, SSS and SSD (Fig. 1) were calculated for four representative concentration pathways (RCPs) and derived for coastal waters closest to the 583,578 polygon centroids from the 2015 Global Mangrove Watch (GMW) database32. After removing duplicates, our dataset contained 10,108 unique mangrove occurrence locations, with corresponding present conditions and predicted future changes in mean SST, SSS and SSD. Under the low-warming scenario RCP 2.6, mean SST of coastal mangrove waters is predicted to change by +0.64 (±0.11) °C and mean SSS by −0.06 (±0.25) practical salinity units (PSU). Combined, this results in an average change in mean SSD of −0.25 (±0.20) kg m−3 in coastal mangrove waters by the late twenty-first century (Supplementary Table 1). These values roughly double under RCP 4.5 (Supplementary Table 2), while under RCP 6.0, a change of +1.69 (±0.14) °C in mean SST, −0.21 (±0.42) PSU in mean SSS and −0.71 (±0.32) kg m−3 in mean SSD is predicted (Supplementary Table 3). Under RCP 8.5, our study predicts a change in SST of +2.84 (±0.21) °C (range 2.11–4.01 °C), a change in SSS of −0.30 (±0.74) PSU (−2.01–1.26 PSU) and a corresponding change in SSD of −1.17 (±0.56) kg m−3 (−2.53–0.03 kg m−3) (Supplementary Table 4).Fig. 1: Global map showing the change in sea surface variables across mangrove bioregions under RCP 8.5.a–c, Change in SST (a), SSS (b) and SSD (c). Changes in SST and SSS are based on present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The vertical line (19° E) separates the two major mangrove bioregions: the AEP and IWP.Full size imageSpatial variability in predicted surface ocean property changes was examined by considering the two major mangrove bioregions (AEP and IWP) (Fig. 2) and using the Marine Ecoregions of the World (MEOW) biogeographic classification28 (Fig. 3). Both the range and changes in mean SST were comparable for the AEP and IWP mangrove bioregions, for all respective RCP scenarios (Fig. 2a and Supplementary Tables 1–4). Under RCP 8.5, mean SST in both mangrove bioregions is predicted to warm ~2.8 °C by 2100, which is roughly 4.5 times the predicted increase in mean SST under RCP 2.6 (Supplementary Tables 1 and 4). Predictions for the RCP 8.5 scenario are generally consistent with reported global ocean temperature trends33 and show that the greatest warming occurs in coastal waters near the Galapagos Islands (change in mean SST of 3.92 ± 0.06 °C). Pronounced SST increases are also predicted for Hawaii (change in mean SST of 3.36 ± 0.05 °C), the Southeast Australian Shelf (3.30 ± 0.25 °C), Northern and Southern New Zealand (3.25 ± 0.07 °C and 3.34 ± 0.02 °C, respectively), Warm Temperate Northwest Pacific (3.27 ± 0.16 °C), the Red Sea and Gulf of Aden (3.24 ± 0.08 °C), Somali/Arabian Coast (3.23 ± 0.15 °C), South China Sea (3.07 ± 0.10 °C), the Tropical East Pacific (3.09 ± 0.15 °C) and the Warm Temperate Northwest Atlantic (3.14 ± 0.13 °C) (Fig. 3b and Supplementary Tables 4).Fig. 2: Change in surface ocean properties for coastal waters bordering mangrove forests and in the two major mangrove bioregions, the AEP and IWP, for different RCPs.a–c, Variation in SST (a), SSS (b) and SSD (c) under various RCP scenarios. Grey indicates global distribution (n = 10,108), orange denotes AEP (n = 3,190) and green represents IWP (n = 6,918). Data for SST and SSS consist of present-day (2000–2014) and future (2090–2100) marine fields from the Bio-ORACLE database29,30, from which SSD data were derived. The cat-eye plots50 show the distribution of the data. Median and mean values are indicated with black and white circles, respectively, and the vertical lines represent the interquartile range.Full size imageFig. 3: Global spatial variability in SST, SSS and SSD for coastal waters bordering mangrove forests under RCP 8.5.a, Global map showing the provinces (colour code and numbers) from the MEOW database28 used to investigate spatial patterns in mangrove coastal ocean water changes by 2100. b–d, Longitudinal gradient of the change in SST (b), SSS (c) and SSD (d) under RCP 8.5 in the AEP and the IWP mangrove bioregions; circles are coloured according to the MEOW province in which respective mangrove sites are located.Full size imagePredicted SSS changes exhibit an opposite trend in the AEP and IWP bioregions, with increased salinity in the AEP and reduced salinity in the IWP under global warming (RCP 2.6–RCP 8.5; Fig. 2b); this is reflected in contrasting SSD changes in both mangrove bioregions (Fig. 2c) and associated with predicted global changes in precipitation, with extensions of the rainy season over most of the monsoon domains, except for the American monsoon34. Under RCP 8.5, the spatially averaged change in mean SSS is +0.51 (±0.57) PSU in the AEP and −0.68 (±0.44) PSU in the IWP region. The maximum decrease in mean SSS (−2.01 PSU) is predicted for the Gulf of Guinea in the AEP bioregion (Fig. 3c and Supplementary Table 4). Within the IWP, the Western Indian Ocean region shows little or no changes in SSS, which contrasts with the pronounced freshening trends predicted in the eastern part of this ocean basin and the Tropical West Pacific (Figs. 1b and 3c). Increased freshening is predicted in the Bay of Bengal (SSS change: −1.17 ± 0.43 PSU), the Sunda Shelf (SSS change: −1.21 ± 0.29 PSU) and the Western Coral Triangle province (mean SSS change: −0.80 ± 0.17 PSU) (Fig. 3c and Supplementary Table 4). Within the AEP, salinity increases exceed +0.96 PSU in the Tropical Northwestern Atlantic, +0.80 in the Warm Temperate Northwest Atlantic and +0.68 in the West African Transition (Fig. 3c and Supplementary Table 4). The spatial heterogeneity in SSS across the global range of mangrove forests corresponds with observed changes in SSS35. Trends in SSD (Fig. 3d) strongly track changes in SSS (Fig. 3c) rather than SST. All RCP scenarios predict an overall decrease in SSD for both mangrove bioregions; however, the predicted decrease in SSD in the IWP region was a factor of 2 (RCP 6.0) and 2.5 (RCP 2.6, RCP 4.5 and RCP 8.5) stronger than in the AEP (Figs. 2 and 3d and Supplementary Tables 1–4).Propagule density values from our literature survey range from 1,080 kg m−3 for different mangrove species (Fig. 4 and Supplementary Table 5). The low densities reported for Heritiera littoralis propagules provide a strong contrast with the near-seawater propagule densities reported for Avicennia and members of the Rhizophoraceae (Bruguiera, Rhizophora and Ceriops). Floating characteristics of the latter may be particularly sensitive to changes in SSD. To illustrate the potential influence of changing ocean conditions on mangrove propagule dispersal, we considered threshold water density values (1,020 and 1,022 kg m−3) that are within the range where elongated propagules of important mangrove genera tend to change floating orientation (Fig. 4a). More specifically, we determined the ocean surface area with an SSD below or equal to these thresholds under different climate change scenarios (Fig. 5). Under RCP 8.5, the ocean surface covered by mangrove coastal waters (coastal waters bordering present mangrove forests) with a density ≤1,020 kg m−3 increases ~27% by 2100, notably more so in the IWP (~37%) than in the AEP (~6%) (Supplementary Table 6). A threshold of 1,022 kg m−3 results in increases of roughly +11% (global), +12% (IWP) and +8% (AEP) (Supplementary Table 7). Similar spatial patterns are observed for open-ocean waters within the global latitudinal range of mangroves (Fig. 5 and Supplementary Figs. 1 and 2).Fig. 4: Potential effect of future declines in SSD on mangrove propagule dispersal.a, Range of reported propagule density values for wide-ranging mangrove species and present and future range of SSD for coastal waters along the range of those mangrove species. Mangrove propagule data are extracted from the literature (Supplementary Table 5). H. lit, Heritiera littoralis; X. gra, Xylocarpus granatum; A. ger, Avicennia germinans; A. mar, Avicennia marina; B. gym, Bruguiera gymnorrhiza; C. tag, Ceriops tagal; R. man, Rhizophora mangle; R. muc, Rhizophora mucronata. Bottom part adapted from ref. 51. b, Conceptual figure of the potential effects of ocean warming and freshening on mangrove propagule dispersal. Ocean warming and freshening drive changes in SSD and may reduce the timeframe for opportunistic colonization. For a propagule with a specific density and floating profile under present surface ocean conditions, reduced SSD of coastal and open-ocean waters may reduce floatation time (shaded area) and hence, reduce the proportion of long-distance dispersers. For simplicity, the density of propagules is assumed to increase linearly over time, although the actual increase may be nonlinear.Full size imageFig. 5: Future changes in SSD.a–d, Spatial extent of coastal and open-ocean surface waters with a density ≤1,020 kg m−3 (a,b) and 1,022 kg m−3 (c,d), for present (2000–2014) (a,c) and future (2090–2100; RCP 8.5) (b,d) scenarios. Data are shown for surface ocean waters within the global latitudinal range of mangrove forests (between 32° N and 38° S). The two density thresholds considered are within the range of densities at which mangrove propagule buoyancy and floating orientation of several mangrove genera change, as reported in available literature. Black dots along the coast represent the global mangrove extent from the 2015 GMW dataset32. Magenta-coloured circles represent SSD values More

Carpenter, K. E. et al. One-third of reef-building corals face elevated extinction risk from climate change and local impacts. Science 321, 560–563 (2008).CAS 
PubMed 
Article 

Google Scholar 
Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. 9, 4921–4932 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Vega Thurber, R. et al. Metagenomic analysis of stressed coral holobionts. Environ. Microbiol. 11, 2148–2163 (2009).PubMed 
Article 
CAS 

Google Scholar 
Rosenberg, E. & Zilber-Rosenberg, I. Microbes drive evolution of animals and plants: the hologenome concept. mBio 7, e01395 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
van Oppen, M. J. H. & Blackall, L. L. Coral microbiome dynamics, functions and design in a changing world. Nat. Rev. Microbiol. 17, 557–567 (2019).PubMed 
Article 
CAS 

Google Scholar 
Reshef, L., Koren, O., Loya, Y., Zilber-Rosenberg, I. & Rosenberg, E. The coral probiotic hypothesis. Environ. Microbiol. 8, 2068–2073 (2006).CAS 
PubMed 
Article 

Google Scholar 
Ainsworth, T. D., Thurber, R. V. & Gates, R. D. The future of coral reefs: a microbial perspective. Trends Ecol. Evol. 25, 233–240 (2010).PubMed 
Article 

Google Scholar 
Lee, S. M. et al. Bacterial colonization factors control specificity and stability of the gut microbiota. Nature 501, 426–429 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Freter, R. The fatal enteric cholera infection in the guinea pig, achieved by inhibition of normal enteric flora. J. Infect. Dis. 97, 57–65 (1955).CAS 
PubMed 
Article 

Google Scholar 
Corr, S. C. et al. Bacteriocin production as a mechanism for the antiinfective activity of Lactobacillus salivarius UCC118. Proc. Natl. Acad. Sci. USA 104, 7617–7621 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kamada, N. et al. Regulated virulence controls the ability of a pathogen to compete with the gut microbiota. Science 336, 1325–1329 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Khosravi, A. et al. Gut microbiota promote hematopoiesis to control bacterial infection. Cell Host Microbe 15, 374–381 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Li, J., Kuang, W. Q., Long, L. J. & Zhang, S. Production of quorum-sensing signals by bacteria in the coral mucus layer. Coral Reefs 36, 1235–1241 (2017).Article 

Google Scholar 
Alagely, A., Krediet, C. J., Ritchie, K. B. & Teplitski, M. Signaling-mediated cross-talk modulates swarming and biofilm formation in a coral pathogen Serratia marcescens. ISME J. 5, 1609–1620 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Krediet, C. J., Ritchie, K. B., Alagely, A. & Teplitski, M. Members of native coral microbiota inhibit glycosidases and thwart colonization of coral mucus by an opportunistic pathogen. ISME J. 7, 980–990 (2013).CAS 
PubMed 
Article 

Google Scholar 
Thompson, F. L., Hoste, B., Thompson, C. C., Huys, G. & Swings, G. The coral bleaching Vibrio shiloi Kushmaro et al. 2001 is a later synonym of Vibrio mediterranei Pujalte and Garay 1986. Syst. Appl. Microbiol. 24, 516–519 (2001).CAS 
PubMed 
Article 

Google Scholar 
Santoro, E. P. et al. Coral microbiome manipulation elicits metabolic and genetic restructuring to mitigate heat stress and evade mortality. Sci. Adv. 7, eabg3088 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Tang, K. H. et al. Antagonism between coral pathogen Vibrio coralliilyticus and other bacteria in the gastric cavity of scleractinian coral Galaxea fascicularis. Sci. China-Earth Sci. 63, 157–166 (2020).CAS 
Article 

Google Scholar 
Zhou, Y. Q. et al. Identification of bacteria-derived urease in the coral gastric cavity. Sci. China-Earth Sci. 63, 1553–1563 (2020).CAS 
Article 

Google Scholar 
Chen, B. et al. Microbiome community and complexity indicate environmental gradient acclimatisation and potential microbial interaction of endemic coral holobionts in the South China Sea. Sci. Total Environ. 765, 142690 (2021).CAS 
PubMed 
Article 

Google Scholar 
Tout, J. et al. Increased seawater temperature increases the abundance and alters the structure of natural Vibrio populations associated with the coral Pocillopora damicornis. Front. Microbiol. 6, 432 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Savary, R. et al. Fast and pervasive transcriptomic resilience and acclimation of extremely heat-tolerant coral holobionts from the northern Red Sea. Proc. Natl. Acad. Sci. USA 118, e2023298118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Vezzulli, L. et al. Vibrio infections triggering mass mortality events in a warming Mediterranean Sea. Environ. Microbiol. 12, 2007–2019 (2010).CAS 
PubMed 
Article 

Google Scholar 
Rosenberg, E. & Falkovitz, L. The Vibrio shiloi/Oculina patagonica model system of coral bleaching. Annu. Rev. Microbiol. 58, 143–159 (2004).CAS 
PubMed 
Article 

Google Scholar 
Gibbin, E. et al. Vibrio coralliilyticus infection triggers a behavioural response and perturbs nutritional exchange and tissue integrity in a symbiotic coral. ISME J. 13, 989–1003 (2019).CAS 
PubMed 
Article 

Google Scholar 
Kimes, N. E. et al. Temperature regulation of virulence factors in the pathogen Vibrio coralliilyticus. ISME J. 6, 835–846 (2012).CAS 
PubMed 
Article 

Google Scholar 
Banin, E., Vassilakos, D., Orr, E., Martinez, R. J. & Rosenberg, E. Superoxide dismutase is a virulence factor produced by the coral bleaching pathogen Vibrio shiloi. Curr. Microbiol. 46, 418–422 (2003).CAS 
PubMed 
Article 

Google Scholar 
Meron, D. et al. Role of flagella in virulence of the coral pathogen Vibrio coralliilyticus. Appl. Environ. Microbiol. 75, 5704–5707 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Rubio-Portillo, E. et al. Virulence as a side effect of interspecies interaction in Vibrio coral pathogens. mBio 11, e00201-20 (2020).Rubio-Portillo, E., Yarza, P., Penalver, C., Ramos-Espla, A. A. & Anton, J. New insights into Oculina patagonica coral diseases and their associated Vibrio spp. communities. ISME J. 8, 1794–1807 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Bourne, D. G. et al. Microbial disease and the coral holobiont. Trends Microbiol. 17, 554–562 (2009).CAS 
PubMed 
Article 

Google Scholar 
Ben-Haim, Y., Zicherman-Keren, M. & Rosenberg, E. Temperature-regulated bleaching and lysis of the coral Pocillopora damicornis by the novel pathogen Vibrio coralliilyticus. Appl. Environ. Microbiol. 69, 4236–4242 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Gavish, A. R., Shapiro, O. H., Kramarsky-Winter, E. & Vardi, A. Microscale tracking of coral–vibrio interactions. ISME Commun. 1, 18 (2021).Shapiro, O. H. et al. Vortical ciliary flows actively enhance mass transport in reef corals. Proc. Natl. Acad. Sci. USA 111, 13391–13396 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Shapiro, O. H., Kramarsky-Winter, E., Gavish, A. R., Stocker, R. & Vardi, A. A coral-on-a-chip microfluidic platform enabling live-imaging microscopy of reef-building corals. Nat. Commun. 7, 10860 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, D. D. et al. Identification and characterization of microsatellite markers for scleractinian coral Galaxea fascicularis and its symbiotic zooxanthellae. Conservation. Genet. Resour. 5, 741–743 (2013).Article 

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

Google Scholar 
Liu, X. et al. Symbiosis of a P2-family phage and deep-sea Shewanella putrefaciens. Environ. Microbiol. 21, 4212–4232 (2019).CAS 
PubMed 
Article 

Google Scholar 
Wang, P. et al. Eliminating mcr-1-harbouring plasmids in clinical isolates using the CRISPR/Cas9 system. J. Antimicrob. Chemother. 74, 2559–2565 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zeng, Z. et al. Cold adaptation regulated by cryptic prophage excision in Shewanella oneidensis. ISME J. 10, 2787–2800 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Wang, X. et al. Cryptic prophages help bacteria cope with adverse environments. Nat. Commun. 1, 147 (2010).Bardwell, J. C., McGovern, K. & Beckwith, J. Identification of a protein required for disulfide bond formation in vivo. Cell 67, 581–589 (1991).CAS 
PubMed 
Article 

Google Scholar 
Wang, X., Kim, Y. & Wood, T. K. Control and benefits of CP4-57 prophage excision in Escherichia coli biofilms. ISME J. 3, 1164–1179 (2009).CAS 
PubMed 
Article 

Google Scholar 
Wood, T. K., Gonzalez Barrios, A. F., Herzberg, M. & Lee, J. Motility influences biofilm architecture in Escherichia coli. Appl. Microbiol. Biotechnol. 72, 361–367 (2006).CAS 
PubMed 
Article 

Google Scholar 
Song, S., Guo, Y., Kim, J. S., Wang, X. & Wood, T. K. Phages mediate bacterial self-recognition. Cell Rep. 27, 737–749 (2019).CAS 
PubMed 
Article 

Google Scholar 
Krediet, C. J., Carpinone, E. M., Ritchie, K. B. & Teplitski, M. Characterization of the gacA-dependent surface and coral mucus colonization by an opportunistic coral pathogen Serratia marcescens PDL100. FEMS Microbiol. Ecol. 84, 290–301 (2013).CAS 
PubMed 
Article 

Google Scholar 
Guo, Y., Lin, J. & Wang, X. Rapid detection of temperate bacteriophage using a simple motility assay. Environ. Microbiol. Rep. 13, 728–734 (2021).CAS 
PubMed 
Article 

Google Scholar 
Tang, K. et al. Prophage Tracer: precisely tracing prophages in prokaryotic genomes using overlapping split-read alignment. Nucleic Acids Res. 49, e128 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ding, J. Y., Shiu, J. H., Chen, W. M., Chiang, Y. R. & Tang, S. L. Genomic insight into the host–endosymbiont relationship of Endozoicomonas montiporae CL-33(T) with its coral host. Front. Microbiol. 7, 251 (2016).PubMed 
PubMed Central 

Google Scholar 
Yang, C. S. et al. Endozoicomonas montiporae sp. nov., isolated from the encrusting pore coral Montipora aequituberculata. Int. J. Syst. Evol. Microbiol. 60, 1158–1162 (2010).CAS 
PubMed 
Article 

Google Scholar 
Schreiber, L., Kjeldsen, K. U., Obst, M., Funch, P. & Schramm, A. Description of Endozoicomonas ascidiicola sp nov., isolated from Scandinavian ascidians. Syst. Appl. Microbiol. 39, 313–318 (2016).CAS 
PubMed 
Article 

Google Scholar 
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lu, S. N. et al. CDD/SPARCLE: the conserved domain database in 2020. Nucleic Acids Res. 48, D265–D268 (2020).CAS 
PubMed 
Article 

Google Scholar 
Mai-Prochnow, A. et al. Hydrogen peroxide linked to lysine oxidase activity facilitates biofilm differentiation and dispersal in several Gram-negative bacteria. J. Bacteriol. 190, 5493–5501 (2008).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Campillo-Brocal, J. C., Lucas-Elio, P. & Sanchez-Amat, A. Identification in Marinomonas mediterranea of a novel quinoprotein with glycine oxidase activity. MicrobiologyOpen 2, 684–694 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chacon-Verdu, M. D., Gomez, D., Solano, F., Lucas-Elio, P. & Sanchez-Amat, A. LodB is required for the recombinant synthesis of the quinoprotein l-lysine-epsilon-oxidase from Marinomonas mediterranea. Appl. Microbiol. Biotechnol. 98, 2981–2989 (2014).CAS 
PubMed 
Article 

Google Scholar 
Gomez, D., Lucas-Elio, P., Solano, F. & Sanchez-Amat, A. Both genes in the Marinomonas mediterranea lodAB operon are required for the expression of the antimicrobial protein lysine oxidase. Mol. Microbiol. 75, 462–473 (2010).CAS 
PubMed 
Article 

Google Scholar 
Piewngam, P. et al. Pathogen elimination by probiotic Bacillus via signalling interference. Nature 562, 532–537 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zipperer, A. et al. Human commensals producing a novel antibiotic impair pathogen colonization. Nature 535, 511–516 (2016).CAS 
PubMed 
Article 

Google Scholar 
Selva, L. et al. Killing niche competitors by remote-control bacteriophage induction. Proc. Natl. Acad. Sci. USA 106, 1234–1238 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Regev-Yochay, G., Trzcinski, K., Thompson, C. M., Malley, R. & Lipsitch, M. Interference between Streptococcus pneumoniae and Staphylococcus aureus: in vitro hydrogen peroxide-mediated killing by Streptococcus pneumoniae. J. Bacteriol. 188, 4996–5001 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Paul, J. H. Prophages in marine bacteria: dangerous molecular time bombs or the key to survival in the seas? ISME J. 2, 579–589 (2008).CAS 
PubMed 
Article 

Google Scholar 
Frazao, N., Sousa, A., Lassig, M. & Gordo, I. Horizontal gene transfer overrides mutation in Escherichia coli colonizing the mammalian gut. Proc. Natl. Acad. Sci. USA 116, 17906–17915 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yu, M. et al. Purification and characterization of antibacterial compounds of Pseudoalteromonas flavipulchra JG1. Microbiology-SGM 158, 835–842 (2012).CAS 
Article 

Google Scholar 
James, S. G., Holmstrom, C. & Kjelleberg, S. Purification and characterization of a novel antibacterial protein from the marine bacterium D2. Appl. Environ. Microbiol. 62, 2783–2788 (1996).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lucas-Elio, P., Gomez, D., Solano, F. & Sanchez-Amat, A. The antimicrobial activity of marinocine, synthesized by Marinomonas mediterranea, is due to hydrogen peroxide generated by its lysine oxidase activity. J. Bacteriol. 188, 2493–2501 (2006).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Imlay, J. A. & Linn, S. Mutagenesis and stress responses induced in Escherichia coli by hydrogen peroxide. J. Bacteriol. 169, 2967–2976 (1987).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Los, J. M., Los, M., Wegrzyn, G. & Wegrzyn, A. Differential efficiency of induction of various lambdoid prophages responsible for production of Shiga toxins in response to different induction agents. Microb. Pathog. 47, 289–298 (2009).CAS 
PubMed 
Article 

Google Scholar 
Knowles, B. et al. Lytic to temperate switching of viral communities. Nature 531, 466–470 (2016).CAS 
PubMed 
Article 

Google Scholar 
Howard-Varona, C., Hargreaves, K. R., Abedon, S. T. & Sullivan, M. B. Lysogeny in nature: mechanisms, impact and ecology of temperate phages. ISME J. 11, 1511–1520 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Arkin, A. P. et al. KBase: The United States Department of Energy Systems Biology Knowledgebase. Nat. Biotechnol. 36, 566–569 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Luo, P., He, X. Y., Liu, Q. T. & Hu, C. Q. Developing universal genetic tools for rapid and efficient deletion mutation in Vibrio species based on suicide T-vectors carrying a novel counterselectable marker, vmi480. PLoS ONE 10, e0144465 (2015).Wang, P. et al. Development of an efficient conjugation-based genetic manipulation system for Pseudoalteromonas. Microb. Cell Fact. 14, 11 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bertani, L. E. & Bertani, G. Preparation and characterization of temperate, non-inducible bacteriophage P2 (host: Escherichia coli). J. Gen. Virol. 6, 201–212 (1970).CAS 
PubMed 
Article 

Google Scholar 
Garneau, J. R., Depardieu, F., Fortier, L. C., Bikard, D. & Monot, M. PhageTerm: a tool for fast and accurate determination of phage termini and packaging mechanism using next-generation sequencing data. Sci Rep. 7, 8292 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Pratt, L. A. & Kolter, R. Genetic analysis of Escherichia coli biofilm formation: roles of flagella, motility, chemotaxis and type I pili. Mol. Microbiol. 30, 285–293 (1998).CAS 
PubMed 
Article 

Google Scholar 
Page, A. J. et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Brynildsrud, O., Bohlin, J., Scheffer, L. & Eldholm, V. Rapid scoring of genes in microbial pan-genome-wide association studies with Scoary. Genome Biol. 17, 238 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).CAS 
PubMed 
PubMed Central 
Article 

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

Google Scholar 
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yilmaz, P. et al. The SILVA and ‘All-species Living Tree Project (LTP)’ taxonomic frameworks. Nucleic Acids Res. 42, D643–D648 (2014).CAS 
PubMed 
Article 

Google Scholar 
Chong, J., Liu, P., Zhou, G. & Xia, J. Using MicrobiomeAnalyst for comprehensive statistical, functional, and meta-analysis of microbiome data. Nat. Protoc. 15, 799–821 (2020).CAS 
PubMed 
Article 

Google Scholar 
Nagpal, S., Singh, R., Yadav, D. & Mande, S. S. MetagenoNets: comprehensive inference and meta-insights for microbial correlation networks. Nucleic Acids Res. 48, W572–W579 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Key delimitation trapping survey performance factorsTrap attractivenessThe performance of the current Medfly design was unexpectedly inferior to that of the leek moth even with a more vagile target insect, 2.8 times greater trap density in the core, and a grid size over three times larger. Despite all those factors, p(capture) for the leek moth grid with 1/λ = 20 m was 15 percentage points greater than that for Medfly at 30 days duration. Thus, trap attractiveness was the key determinant for delimiting survey performance, as it was for detection13.One straightforward way to improve p(capture) and the accuracy of boundary setting, while also cutting costs, would be to develop more attractive traps. Poorly attractive traps include food-based attractants48 and traps based solely on visual stimuli36. But developing better traps is difficult. Pheromone-based attractants generally perform best49, but these are unavailable for many insects. For instance, scientists have searched for decades for effective pheromones for Anastrepha suspensa (Loew) and A. ludens (Loew) without success50. Common issues include the complexity of components, costs of synthesis, and chemical stability.Trap densitiesAll else being equal, increasing the trap density will generally improve p(capture) for any survey grid, and intuitively this can help compensate for using less attractive traps. However, the impact of increasing density is limited when attractiveness is low13,47, and large surveys or grids with many traps can become prohibitively expensive51. The Medfly grid designers likely understood that the available trap and lure was not highly attractive, and used higher densities in inner bands to try to reach some desired (non-quantitative) survey performance level. By contrast, the designers of the leek moth grid used a (constant) density three times smaller, likely because the trap and lure were known to be relatively strong. Here, for both species, marginal ROI decreased as densities increased (Tables 2, 3). Hence, increasing densities has limited benefit, but may be useful when better lures are unavailable13.In that context, the use of variable densities in the Medfly grid is understandable. At its standard size, the survey grid would require 8,100 traps if the core trap density were constant (Table 1). The designers likely intuited that lower densities could be used in outer bands because captures there were less likely. However, doing so reduces the likelihood of detection in outer bands and could increase the possibility of undetected egress, especially with longer survey durations. As far as we know, natural egress has not been raised as a concern following the numerous Medfly quarantines that have used this survey grid over the years, in Southern California in particular52.Generally, however, we think the variable Medfly grid densities run counter to delimitation goals. Greater core and Band 2 densities have proportionally more impact on p(capture), but only a few detections in the core are necessary to confirm the presence of the population (Goal 1), and inner area detections probably contribute little to boundary setting (see below). Therefore, lower or intermediate densities (at most) may be optimal for the core when considering ROI. For the outer bands, increasing densities might improve boundary setting (Goal 2) and help mitigate potential egress, but the sizes of those bands already limit cost efficiency (Table 2), making greater densities less advisable. Our simulation results can help elucidate how to balance these interests to achieve delimitation goals while minimizing costs47.Grid size considerationsThe simulation results indicated that the standard survey sizes for these two pests were excessive. We have verified that empirically for Medfly using trapping detections data53. A 14.5-km grid has been widely used for many other insects in the CDFA (2013) guidelines10, such as Mexfly and OFF, and the same analysis indicated that those are also oversized for use in short-term delimitation surveys53. From the same analysis, the predicted survey radius for leek moth, with D = 500 m2 per day, would be 2,382 m, or a diameter of nearly 4.8 km, which matches the results here. Similarly, Dominiak and Fanson45 analyzed trapping data for Qfly and found that the recommended quarantine area distance of 15 km could be reduced to 3 to 4 km.Grids with radii larger than 4.8-km only seem necessary for highly vagile insects, those with D ≥ 50,000 m2 per day47. This should not be surprising. Small insect populations are unlikely to move very far31,54, especially if hosts are available20,39,55. The (proposed) short duration of a delimitation survey would also limit dispersal potential (see below). Many delimiting survey plans may be oversized, because they were developed before much dispersal research had been done37, thus uncertainty was high. Our dispersal distance analysis included species with a wide range of dispersal abilities, so it can be used generally to choose smaller survey grid radii53.Reducing grid sizes down to about 4.8-km diameters may have little impact on p(capture), since detections in bands outside that distance contributed little to overall performance. The cores of both the leek moth and Medfly grids accounted for 86 percent or more of overall p(capture). While core area detections will confirm the presence of the population, they are less useful for defining spatial extent. The furthest detections from the presumed source are usually used to delimit the incursion46,56 (although in our experience formal boundary setting exercises seem rare). Delimiting surveys may often yield few captures anyway, because adventive populations can be very small and subject to high mortality31. Because size reductions eliminate traps in proportionally larger outer areas, the impact on survey costs is substantial. Removing just the outermost bands of each grid would directly reduce costs by $11,200 for leek moth (400 traps) and by $7,488 for Medfly (288 traps; Table 1).Another reason for the large size of the standard Medfly grid may be that it was designed for monitoring and management in addition to delimitation57. Medfly quarantines end after at least three generations without a detection, so the surveys may last for months. The grid size was reportedly originally determined by multiplying the estimated dispersal distance by three (PPQ, personal communication), to account for uncertainty. This implies that the estimated distance was about 2,400 m per 30 days. Thus, the design may not have been built for the 30-d duration used here, but our recommended design is valid if a shorter delimitation activity without further monitoring is appropriate.Although it seemed too large for leek moth, an 8-km grid for delimitation could be appropriate for some other moths. For example, the delimiting survey plans for Spodoptera littoralis (Boisduval) and S. exempta Walker use this size9. S. littoralis is described as dispersing “many miles”, and S. exempta can travel hundreds of miles9, which clearly exceeds the described dispersal ability of leek moth. On the other hand, the survey plan for summer fruit tortrix moth (Adoxophyes orana Fischer von Röeslerstamm) also specifies an 8-km grid for delimitation but contains little information on dispersal, suggesting only that most movement is local8. Like leek moth, a 4.8-km grid for that species seems likely to be more appropriate.Limiting egress potential is probably the main consideration when setting survey size, but uncertainty about the source population location may also be a factor. Survey grids placed over the earliest insect detection may sometimes be off center from the location of the source population54. However, so far as we know for our agency, most adventive populations have been localized, based on post-discovery detections (PPQ, personal communication). Likewise, we have found53 and other researchers have found that dispersal distances for different species in outbreaks and mark-recapture studies are often less than 1 km58,59,60. That may often be the case for detection networks of traps (e.g., for high risk fruit flies), which increase the likelihood of capture before the population has had much time to grow and disperse. Here, we focused explicitly on localized populations, but allowed for uncertainty in the simulations by varying outbreak locations over one mile in the central part of the grid. If the outbreak population is very large and has extensively spread out (e.g., spotted lanternfly, Lycorma delicatula (White) in 201461), delimitation will not be localized, but “area-wide”2. The results here do not apply to area-wide outbreaks, and we are currently studying how to effectively delimit them.Optimizing delimitation surveysMany trapping survey designs in use were based not on “hard” science but on local experience62. Scientists have recognized the need for more cost-effective surveillance strategies63,64. Quantitatively assessing p(capture) in different designs for the same target pest allows us to determine grid sizes and densities that lower costs while maintaining performance. Results here demonstrated that the sizes and densities of these two survey grids could be optimized to save up to $20,244 per survey for the leek moth and $38,168 per survey for the Medfly. In practical terms, that means more than five leek moth surveys could be run for the cost of one standard design survey. Additionally, over seven Medfly delimitation surveys could be funded by the budget of one standard plan. The magnitudes of reduction seen here may be typical, since about 90 percent of the costs in trapping surveys are for transportation and maintenance related to traps65.Quantifying survey performance was not possible until very recently, so it has been little discussed in the literature5,66, and no standard thresholds exist. We think 0.5 may be a reasonable minimum threshold for the choice of p(capture), to try to ensure that population detection is “more likely than not”. Designs that aim to maximize p(capture) could be realistic with high attractiveness traps, but those designs seem very likely to have lower ROIs (e.g., Table 2). Even for the most serious insect pests, we think targeting near-perfect population detection during delimitation is likely not justified. Designs achieving p(capture) from 0.6 to 0.75 could be highly effective in terms of both costs and performance.Another potential area of improvement is grid shape. Circular grids perform as well as square grids but use fewer traps and less service area to achieve equivalent p(capture)47. Moreover, detections in the corners of a square grid are evidence that insects could have traveled beyond the square along the axes, resulting in uncertain boundary setting. Most published survey grids are square10,46, but many field managers tend to use approximately circular trapping grids in the field (PPQ, personal communication). The conversion to a circular grid with a radius of half the square side length reduces the area and number of traps by around 21 percent47. Our findings were consistent with that value.This new quantification ability also indicates that some delimiting survey designs in the U.S.A. may not be performing as well as expected47. For instance, the delimiting survey design for Mexfly uses approximately 31 traps per km2 in the core of a 14.5 km square grid11, but the traps are only weakly attractive (1/λ ≈ 5 m). In this scenario, p(capture) was only around 0.23 with a 30-d survey duration47. A much greater density ( > 80 traps per km2) could be used in the core to achieve p(capture) ≥ 0.5, but this may not be feasible depending on the survey budget.Technical and modeling considerationsExamining diffusion-based movement for these two insects in TrapGrid can give insight into why simulations indicated that smaller grids may be adequate47. The value of σ for Medfly after 30 days is only about 1,550 m. In a normal distribution, σ = 1,550 m gives a 95th percentile distance of 2,550 m, which is similar to the estimated distance above of 2,400 m. Over 90 days, σ = 2,700 m for Medfly, which gives a 95th percentile distance of 4,441 m, still much shorter than the grid radius of 7,250 m. A 95th percentile of 7,250 m requires σ ≈ 4,408 m, which equals t = 253 days. In addition, the maximum total distance (up to 39 days after detection) we observed in trapping detections data for Medfly in Florida was about 4,800 m53.The same calculations for leek moth give σ ≈ 490 m for 30 days, with a 95th percentile distance of only 806 m. That is half the length of the recommended shortened radius above of 2.4 km, and nearly five times shorter than the radius of the standard 8-km grid. A 95th percentile of 4,000 m requires σ = 2,432 m, which implies t = 740 days, which is about two years. Therefore, the leek moth grid is arguably even more oversized than the Medfly grid.The default capture probability calculation in the current version (Ver. 2019-12-11) of TrapGrid is not sensitive to population size32 and does not consider the effects of ambient factors (e.g., wind speed and direction, rainfall, temperature). Many other factors can also impact trapping survey outcomes, such as topography of the environment, availability of host plants, seasonality of pest, and population dynamics. These factors are not considered in the current version of TrapGrid. More