Philippa Kaur

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

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

Dynamic in-flight flock organizationIt is commonly assumed that during flocking, flock members follow three basic interaction rules: Attraction, Repulsion and Alignment, to coordinate spatial positions between each other18. To study the spatial organization of our zebra finch flock during flight, the spatial positions of all birds in the flight section were tracked in every fifth frame (sample rate: 24 Hz (that is, frames per second)) of the synchronized footage recorded by two high-speed digital video cameras (Camera 1: centred upwind view, Fig. 1a,b; Camera 2: upturned vertical view, Fig. 1a,c) for the entire duration (51.7, 58.3, 69.2 and 127 s) of four (session 2, 5, 8 and 13) out of 13 flight sessions. Flight paths were reconstructed from the tracking data for each bird in the flock, with horizontal and vertical coordinates delivered by Camera 1 and coordinates in wind direction delivered by Camera 2. The data show that each bird mainly occupied a particular area in the flight section, and that this spatial preference was stable over different flight sessions. Bird Green, for example, was preferentially flying very low above the flight section’s floor, and bird Lilac preferred to fly at upwind positions in front of the flock (Fig. 1d, Extended Data Figs. 1 and 3 and Supplementary Information).Despite their preference in flight area, all birds constantly changed their spatial positions fast and rhythmically along the horizontal dimension of the flight section (Fig. 1e–g, Extended Data Figs. 2 and 4, Supplementary Video 1 and Supplementary Information). This behaviour is reminiscent of the flight behaviour of wild zebra finches: when being surprised in flight by a predator, zebra finches fly in a rapid zig-zag course low above the ground, heading for nearby vegetation16. Whether the sideways oscillating flight manoeuvres, which are performed by both wild birds in open space and domesticated birds in the wind tunnel’s flight section, are caused by the close proximity to the ground or are part of an escape reaction is yet unknown.From the tracking data, we further calculated the spatial distances in all three dimensions between all pairwise combinations of birds throughout the four flight sessions (sample rate: 24 Hz). When normalized to the maximum distance detected for each bird pairing, each dimension and each flight session, mean distances of bird pairings in all dimensions were narrowly distributed within a range of 27.7–38.0% of maximum distance (Fig. 1h and Supplementary Table 1). This may indicate that during flocking flight, zebra finches actively balance Attraction and Repulsion to maintain a stable 3D distance towards all other members of the flock. Owing to the spatial limitations in the wind tunnel’s flight section, we did not expect the zebra finches to perform large-scale flight manoeuvres with movements aligned between all flock members (Extended Data Fig. 5 and Supplementary Information), as can be observed, for example, in freely flying flocks of homing pigeons (Columba livia domestica)19 and white storks (Ciconia Ciconia)20.Visually guided horizontal repositioningWhen observing the dynamic spatial organization of our zebra finch flock, a question immediately arises: how do the birds prevent collisions during their frequent horizontal position changes? When considering the spatial limitation experienced by the flock of six birds during flight in the flight section and their highly dynamic flight style, collision rates seemed to be astonishingly low (median: 0.02 Hz; interquartile range (IQR): 0–0.03 Hz; n = 13 sessions) during flocking flight (in total 16 collisions in 13 min of analysed flight time). In birds, the visual system represents the main input channel for environmental information. To tackle the above question, we therefore first investigated the role of vision during flocking flight, and tested whether a bird’s viewing direction was correlated with the direction of horizontal position change. As gaze changes are governed by head movements in birds21, we used a bird’s head direction as an indicator for the orientation of its visual axis. We tracked (sample rate: 120 Hz) the position of a bird’s beak tip and neck in each frame of the footage during ten horizontal position changes (Fig. 2a and Supplementary Video 2) per bird, and found a strong interaction between a bird’s head angle relative to the wind direction and its direction of horizontal position change. During horizontal position changes, the birds always turned their heads in the direction of the position change (Fig. 2b). While the population’s median absolute angle of position change was 84.0° (IQR: 78.6–87.2°; n = 60) relative to 0° in wind direction, the population’s median absolute head turning angle was 36.0° (IQR: 26.4–42.5°; n = 60; see Supplementary Information for results on head movements during solo flight). The eyes of zebra finches are positioned laterally on their heads22 and each retina features a small region of highest ganglion cell density (fovea, that is, region of highest visual spatial resolution) at an area that receives visual input from horizontal positions at 60° relative to the midsagittal plane23. By turning their heads by about 36° during horizontal position changes, the zebra finches roughly align the foveal area in the retina of one eye with their direction of position change, and in the retina of the other eye with the wind direction (Fig. 2c,d). Thus, head turns in the direction of position change may indicate that the birds use visual cues while repositioning themselves within the flock. This hypothesis is supported by a study on zebra finch head movements performed during an obstacle avoidance task. In this study, instead of fixating on the obstacle, zebra finches turned their head in the direction of movement while navigating around the obstacle24.Fig. 2: Horizontal position changes are accompanied by head turns.a, Head and body orientation of bird Orange (ventral view) during one example of position changes to the right, tracked (sample rate: 120 Hz) in the footage of Camera 2. Circles: beak tip positions; plus signs: neck positions; upward pointing triangles: tail base positions. Cutouts of freeze frames of the footage taken with Camera 2 show the bird’s head and body posture for 11 time points during the position change. b, In all birds, the median angle of head turn during horizontal position change in flocking flight is positively correlated (linear mixed effects model (LMM), estimates ± s.e.m.: 2.05 ± 0.1, P  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

Understanding the source and fate of microplastics (plastic debris  More

Study siteThe study was conducted at the Center for Applied Research on the Environment and Sustainability (CARES) at The American University in Cairo, New Cairo, Egypt (30°01′11.7″N 31°29′59.8″E) from 12/Nov/2019 until 31st/March/2020. The experiment was carried out in a greenhouse-controlled environment with temperatures ranging from 18 to 23 °C and relative humidity between 60 and 70% during the growing period.Experimental designThe proposed design starts by treating brackish water using RO membrane separation technology, powered by an on-grid 10 kW photovoltaic solar panel as shown in Fig. 1. The permeate (freshwater) from the RO facility is directed to the aquaculture units of capacity of 1 m3, where the fish effluents are used as irrigation water and as the sole source of fertilizers for the crops.Figure 1Schematic Integrated model design. T1 Deep water culture system without sand, T2 Sandponics system with sand from October, T3 Sandponics system with sand from Beni suef, T4 Sandponics system with sand from Fayoum.Full size imageThe study followed a completely randomized design with four variants, i.e., an aquaponic deep-water culture system (T1) and three sandponics systems (T2–T4). The three sandponics systems were established with different sand collected from different sand locations in Egypt during the period between September and October 2019.Initially, an exploratory field trip was set to six different locations in Egypt to collect sand samples for lab analysis aimed at sourcing the most suitable sand for the system under study with regards to both the physical and chemical parameters. These areas include Ismailia Governorate; 30°34′55.2″N 31°50′08.1″E, 6th October governorate; 29°54′49.8″N 31°05′51.5″E, Benu Suef governorate; 28°53′18.4″N 30°45′12.9″E, Al-Minya governorate; 28.725799, 30.630305, and two sites from Fayoum governorate; 29°05′07.4″N 30°49′39.9″E.From the six locations in Egypt, preliminary sand analysis was carried out, and sand samples were also collected for both physical and chemical lab analysis at the Soil and Water Lab at the Agricultural Research Center in Dokki, Egypt. Following a thorough technical, field, mechanical, and lab chemical evaluation of the six sand samples from six locations, three sand locations/types were selected for experimentation that seemed fit and suitable for the current study. The criteria parameters for the shortlisting of sand included water retention potential of the sand by the percolation process, testing the carbonates level in the soil, the turbidity of the sand, porosity percentage and drainage potential of the sand. The three locations included 6th October (T2), Benu Suef (T3), and Fayoum site 2 (T4). In the second week of November 2019, ten cubic meter tracks of sand from the three above locations were set to collect sand from these areas to the research facility at CARES where the experiment was carried out.The study was carried out with two systems/setups, i.e., an aquaponic Deep Water Culture (DWC) and SP systems. The DWC model comprises a 1 m3 fish tank, a settlement tank, a mechanical filter, a biological filter, three grow beds, and a drainage tank. This system being the most practiced aquaponics technique was considered as the control. Fish effluent water flowed from the fish tank to the settlement tank to filter big solid wastes through the mechanical filter to remove the smaller solid wastes and the biological filter for the nitrification process. Then filtered water continues to the grow beds, where overflow drains into the drainage tank and back to the fish tank in a closed system.On the other hand, the variable in the three IAVS systems is the sand source. This system comprises three independent set-ups: a 1 m3 fish tank, three grow beds, and a drainage tank. Fish effluents flowed from the fish tank directly to the sand grow beds where water was supplied through irrigation drip lines using diaghram emitters connected with valves to ensure uniformity of water application to each grow bed.All the fish tanks were installed with the same fish stock size of 30 Nile tilapia (Oreochromis niloticus) from an existing fish stock at the research center with an average initial weight of 244 g and the same amount of water, initially 850L per tank. The fish was sourced from an already existing aquaponics system at the research center to avoid any transportation stress effects and related shocks on the small fish, leading to a lot of mortality cases. The fish were fed 3–4 times daily with commercial pellets containing 30% proteins, 5% crude lipid, 6% crude fiber, 13% Ash, and 9% moisture content supplied by Skretting Egypt. The feeding pattern and frequency were according to the fish body biomass percentage of 2–3% depending on the growth stage and upon reaching satiation.DesalinationThe experiment was entirely run with desalinated water produced from a desalination facility at the center. The desalination technology used was Reverse Osmosis (RO); in batch mode; using a Sea Water Pump with Energy Recovery Unit (model Danfoss-APP1.0/APM1.2). The RO membrane used is Hydraunatic SWC5-4040, from Lenntech company with an average salt rejection of 99.7%. Three modules were connected in a series arrangement (3 Pressure Vessels each equipped with a single module). Synthesized brackish water was prepared by dissolving industrial grade sodium chloride (sea salt) from El-Arish Governorate, Egypt. The salt chemical properties are presented in Table 1. Feedwater salinity was 10 mg/L, with an equivalent osmotic pressure equal to 8.61 bars. The osmotic pressure was calculated using Van’t Hoff relation. Permeate Total Dissolved Solids (TDS) was 192 mg/L, and brine TDS was 13.1 g/L as shown in Table 2.Table 1 Chemical properties of the used salt.Full size tableTable 2 Chemical properties of water samples used.Full size tableThe average pure water flux is 9.5 LMH and was calculated by dividing the permeate volume by the product of membrane surface area and time. Each batch run produced around 4 m3 of permeate, which was enough to irrigate the designated plant beds. The estimated average permeate recovery for the RO process is 22% and salt rejection exceeded 98.7%. The differential pressure between membrane inlet and outlet was equal to 1 bar, where membrane inlet pressure was 16 bars, and the outlet was 15 bars. The RO process operated at an average transmembrane pressure equal to 16 bars and an average permeate and brine flow rates equivalent to 3.49 and 12.41 Lpm, respectively. All experiment runs were performed at 25 °C.Plant materials and cultivation practiceSwiss chard bright lights (Beta vulgaris subsp. cicia) seeds were imported from Seed kingdom seed company in the USA. Seeds were sown in ¼ inch holes in a seed starting mix containing perlite and vermiculite and irrigated with a hand mist sprayer daily to keep the growing media always moist. Sowing was done on the 12th of November 2019, and seedlings were transplanted when they were 40 days old. Seedlings were transplanted into raised grow beds made of fiberglass material measuring 1.8 × 1.2 × 0.6 m for each of the four systems. The beds were raised off the ground by 0.5 m to allow drainage water from the bed to be collected and circulated back to the fish tank. Each bed was constructed with a drainage pipe at the bottom covered with a mesh net to prevent water blockage by the sand. Also, a 5 cm layer of small gravel was uniformly laid at the bottom of the beds to facilitate drainage, followed by sand with a height of 50 cm.In the IAVS systems, plants were irrigated using manually punched diaphragm emitters, and the irrigation flow rate was controlled using small plastic valves at the start of every irrigation tube. Emitters were installed in drip tubing at a 30 cm distance as well the tubing lines were also placed 30 cm between each other. Seedlings were transplanted 5 cm away from the emitters at 30 cm between rows and 30 cm within the row. Since the water was pumped with submersible pumps to the grow beds, regulatory pressure valves were installed in between the pump and the main irrigation line, and then water flows through the emitters into the row furrows. Water would then saturate in the sand and eventually drain at the bottom into drainage tanks and pumped back to the fish tanks.To maintain the water quality, two full cycles of water recirculation were run every day. Every irrigation cycle recirculated 25% of the fish tank, and complete drainage was allowed for a maximum of two hours. Plants were harvested upon reaching maturity for three cuts, except with the T1, which could not grow back after the second cut. Plants took 52 days from transplanting to reach the first cut, 20 days from cut 1 to cut 2, and as well 23 days from cut 2 to reach cut 3. Measurable crop parameters included plant height at harvesting/cutting, leaf area, number of leaves per plant, chlorophyll content, fresh weight per plant, and nutrient composition. Since the focus of SP is on the crops, fish were only measured to monitor their relative growth in terms of weight gained at harvesting/cutting time.Measurement of crop parametersPlants were cut 5 cm above the soil surface, and agronomical trait measurements from a representative sample of 12 plants per replicate were taken as follows.Plant heights were taken using a foot ruler and averages determined. Leaf number was obtained as the number of leaves counted per plant and averages determined. Leaf area was calculated according to the equation reported by Yeshitila and Taye16.$${text{Leaf}} , {text{ Area }}left( {{text{cm}}^{{2}} } right) = , – {422}.{973} + { 22}.{752}0{text{L }}left( {{text{cm}}} right) , + { 8}.{text{31W }}left( {{text{cm}}} right)$$where L and W represent the leaf length and Leaf width respectively, − 422.973 is a constant relating to the shape of the leaf of Swiss chard developed by the author under citation.Chlorophyll content was measured using MC-100 chlorophyll meter from Apogee Instruments, Inc, and data was expressed as SPAD averages. Fresh weight was measured using a digital weighing balance and data expressed as g/plant.Sand testSand samples were obtained and sent for analysis at the Soils, Water and Environment Research Institute, Agricultural Research Center, Giza, Egypt. The Electrical conductivity (EC) values were measured from the sand paste extract; pH values were taken from sand suspensions at ratio of 1:2.5 as described by Estefan17. The available nitrogen in the sand sample was extracted using potassium chloride (KCl) as an extractable solution with the ratio of (5gm sand to 50 ml KCl) and determined using the micro- kjeldahl method. Available potassium was determined using a flame photometer, and the other elements in the sand sample were determined by using inductively coupled plasma (ICP) Spectrometry (model Ultima 2 JY Plasma)18,19. The physical and chemical characteristics of the used sand are presented in Table 3.Table 3 (a): Chemical analysis of field sand samples, (b): Available macro, micronutrients, and heavy metals content of the sand samples.Full size tableWater analysisEvery 15 days, a measured amount of desalinated water was added to a standard mark of 850L in the fish tanks to compensate for the consumed amount of water in the system. Fish water quality parameters such as water temperature, pH, and dissolved oxygen (DO) was closely monitored using automated digital Nilebot technologies by Conative labs to fit the ideal required levels as reported by Somerville et al.20. In contrast, ammonia, nitrite, and nitrate were adjusted using an API test kit every week. These parameters’ recorded values were as follows: water temperature ranged between 25 and 28 °C, DO range between 6–7 mg/L, and pH between 6.5 and 7.0. Ammonia levels were kept below 1 mg/L. Elements in water samples were determined according to EPA methods18 using inductively coupled plasma (ICP) Spectrometry (model Ultima 2 JY Plasma) as presented in Table 4.Table 4 Water sample analysis for the different systems’ fish tanks and sump tanks.Full size tableNutritive composition analysisAccording to Official methods of analysis from the association of official analytical chemists (A.O.A.C) (1990), moisture content and Vitamin C were determined. Vitamin A was determined according to the procedures described by Aremu and Nweze21. Briefly, 100 g of the sample were homogenized, from which 1 g was obtained and soaked in 5 mL methanol for two hours at room temperature in the dark for complete extraction of a pro-vitamin A carotenoid, β-carotene. Separation of the β-carotene layer was achieved through the addition of hexane to the sample, and moisture was removed using sodium sulphonate. The absorbance of the layer was measured at 436 nm using hexane as a blank. β-carotene was calculated using the formula:$$beta {text{-carotene }}left( {{mu g}/{1}00{text{ g}}} right) , = {text{ Absorbance }}left( {text{436 nm}} right) , times {text{ V }} times {text{ D }} times { 1}00 , times { 1}00/{text{W }} times {text{ Y}}$$where: V = total volume of the extract; D = Dilution factor; W = Sample weight; Y = Percentage dry matter content of the sample.Vitamin A was then determined according to the concept of Retinol Equivalent (RE) of the β-carotene content of the vegetables using the standard conversion formula. Total hydrolyzable carbohydrates were determined as glucose using phenol–sulfuric acid reagent as described by Michel22.Vitamin C content was determined using dichlorophenol indophenol reagent. As such, 10 g of fresh leaf tissues, were crushed using a motor and pestle in the presence of 10 ml metaphosphoric acid 6% (Merck). This was followed by centrifugation at 4000×g for 5 min at 4 °C. Five mL of the supernatant were transferred into an Erlenmeyer flask, and 20 mL of 3% metaphosphoric acid were added. The extract was titrated by dichlorophenol indophenol (Sigma-Aldrich) until a rose color was observed. Vitamin C (mg/100 g FW) was then calculated and based on the standard curve of l-Ascorbic acid (Merck) concentrations.For the determination of protein and mineral content, 0.5 g of dried samples were digested using sulfuric acid (H2SO4) and hydrogen peroxide (H2O2) as described by Cottenie23. From the extracted sample, the following minerals were determined:Nitrogen was determined according to the procedures described by Plummer24. Briefly, 5 mL of the digestive solution was distilled with 10 mL of sodium hydroxide (NaOH) for 10 min to obtain ammonia. Back titration was then used to determine the amount of nitrogen present in ammonia. Protein content was calculated by multiplying total nitrogen by 6.25 according to methods of AOAC25.Phosphorus content was determined calorimetrically (660 nm) according to the procedures described by Jackson26. Potassium, Calcium, and Sodium were determined against a standard using a flame-photometer (JEN way flame photometer) as described by Piper27. Magnesium (Mg), Copper (Cu), Manganese (Mn), Zinc (Zn), and Iron (Fe) content were determined using Atomic Absorption Spectrophotometer, Pyeunican SP1900, according to methods described by Liu28.The moisture percentage of leaf samples was determined by weighing the fresh weight for each sample (Fw), then dried for 72 h at 80 °C. The dry matter weight was record as Dw. The leaf water content was then calculated as the following:$${text{Moisture}};{text{ content }}left( % right) , = , left( {{text{Fw}} – {text{Dw}}} right) , /{text{ Fw}} * {1}00$$Statistical analysisStatistical comparisons among means of more than two groups were performed with analysis of variance (ANOVA) using SPSS V22, and the difference in means was analyzed by Tukey’s test at α = 0.05. Statistical differences were considered significant at P ≤ 0.05 in triplicates and data expressed as mean ± S.D.Plant materialAll plant materials and related procedures in this study were done in accordance with the guidelines of the Institutional Review Board of the American University in Cairo and the Ministry of Agriculture and Land Reclamation in Egypt.Ethics approvalThis study followed the guidelines and approval of Committee of Animal Welfare and Research Ethics, Faculty of Agriculture, Kafrelsheikh University, Egypt. More

Siderius, C., Biemans, H., Kashaigili, J. & Conway, D. Water conservation can reduce future water-energy-food-environment trade-offs in a medium-sized African river basin. Agric. Water Manag. 266, 107548 (2022).
Google Scholar 
Zhao, G., Liang, R., Li, K., Wang, Y. & Pu, X. Study on the coupling model of urbanization and water environment with basin as a unit: A study on the Hanjiang Basin in China. Ecol. Ind. 131, 108130 (2021).
Google Scholar 
Zhu, Q. et al. Relationship between ecological quality and ecosystem services in a red soil hilly watershed in southern China. Ecol. Ind. 121, 107119 (2021).
Google Scholar 
Fu, A. et al. The effects of ecological rehabilitation projects on the resilience of an extremely drought-prone desert riparian forest ecosystem in the Tarim River Basin, Xinjiang, China. Sci. Rep. 11, 18485 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dai, D. et al. Comprehensive assessment of the water environment carrying capacity based on the spatial system dynamics model, a case study of Yongding River Basin in North China. J. Clean. Prod. 344, 131137 (2022).
Google Scholar 
Basu, H., Dandele, P. S. & Srivastava, S. K. Sedimentary facies of the Mesoproterozoic Srisailam Formation, Cuddapah basin, India: Implications for depositional environment and basin evolution. Mar. Pet. Geol. 133, 105242 (2021).
Google Scholar 
Capella, W. et al. Sandy contourite drift in the late Miocene Rifian Corridor (Morocco): Reconstruction of depositional environments in a foreland-basin seaway. Sed. Geol. 355, 31–57 (2017).
Google Scholar 
Ilevbare, M. & Omodolor, H. E. Ancient deposition environment, mechanism of deposition and textural attributes of Ajali Formation, western flank of the Anambra Basin, Nigeria. Case Stud. Chem. Environ. Eng. 2, 100022 (2020).
Google Scholar 
Qiao, J. B., Zhu, Y. J., Jia, X. X. & Shao, M. A. Multifractal characteristics of particle size distributions (50–200 m) in soils in the vadose zone on the Loess Plateau, China. Soil Tillage Res. 205, 104786 (2021).
Google Scholar 
Bach, E. M., Baer, S. G., Meyer, C. K. & Six, J. Soil texture affects soil microbial and structural recovery during grassland restoration. Soil Biol. Biochem. 42, 2182–2191 (2010).CAS 

Google Scholar 
Rodríguez-Lado, L. & Lado, M. Relation between soil forming factors and scaling properties of particle size distributions derived from multifractal analysis in topsoils from Galicia (NW Spain). Geoderma 287, 147–156 (2017).ADS 

Google Scholar 
Mozaffari, H., Moosavi, A. A. & Dematte, J. A. M. Estimating particle-size distribution from limited soil texture data: Introducing two new methods. Biosys. Eng. 216, 198–217 (2022).
Google Scholar 
Sudarsan, B., Ji, W., Adamchuk, V. & Biswas, A. Characterizing soil particle sizes using wavelet analysis of microscope images. Comput. Electron. Agric. 148, 217–225 (2018).
Google Scholar 
Pollacco, J. A. P., Fernández-Gálvez, J. & Carrick, S. Improved prediction of water retention curves for fine texture soils using an intergranular mixing particle size distribution model. J. Hydrol. 584, 124597 (2020).
Google Scholar 
Richer-de-Forges, A. C. et al. Hand-feel soil texture and particle-size distribution in central France. Relationships and implications. CATENA 213, 106155 (2022).CAS 

Google Scholar 
Du, W. et al. Insights into vertical differences of particle number size distributions in winter in Beijing, China. Sci. Total Environ. 802, 149695 (2022).ADS 
CAS 
PubMed 

Google Scholar 
Darder, M. L., Paz-González, A., García-Tomillo, A., Lado, M. & Wilson, M. G. Comparing multifractal characteristics of soil particle size distributions calculated by Mie and Fraunhofer models from laser diffraction measurements. Appl. Math. Model. 94, 36–48 (2021).
Google Scholar 
Ke, Z. M. et al. Multifractal parameters of soil particle size as key indicators of the soil moisture distribution. J. Hydrol. 595, 125988 (2021).
Google Scholar 
Qi, F. et al. Soil particle size distribution characteristics of different land-use types in the Funiu mountainous region. Soil Tillage Res. 184, 45–51 (2018).
Google Scholar 
Tyler, S. W. & Wheatcraft, S. W. Fractal scaling of soil particle-size distribution: Analysis and imitations. Soil Sci. Soc. Am. J. 56, 362–369 (1992).ADS 

Google Scholar 
Zhang, Y. et al. Effects of fractal dimension and water content on the shear strength of red soil in the hilly granitic region of southern China. Geomorphology 351, 106956 (2020).
Google Scholar 
Ahmadi, A., Neyshabouri, M.-R., Rouhipour, H. & Asadi, H. Fractal dimension of soil aggregates as an index of soil erodibility. J. Hydrol. 400, 305–311 (2011).ADS 

Google Scholar 
Gao, Z., Niu, F., Lin, Z. & Luo, J. Fractal and multifractal analysis of soil particle-size distribution and correlation with soil hydrological properties in active layer of Qinghai-Tibet Plateau, China. CATENA 203, 105373 (2021).
Google Scholar 
Xu, G. et al. New method for the reconstruction of sedimentary systems including lithofacies, environments, and flow paths: A case study of the Xisha Trough Basin, South China Sea. Mar. Pet. Geol. 133, 105268 (2021).
Google Scholar 
Li, Z., Yu, X., Dong, S., Chen, Q. & Zhang, C. Microtextural features on quartz grains from eolian sands in a subaqueous sedimentary environment: A case study in the hinterland of the Badain Jaran Desert, Northwest China. Aeolian Res. 43, 100573 (2020).
Google Scholar 
Chen, T. et al. Modeling the effects of topography and slope gradient of an artificially formed slope on runoff, sediment yield, water and soil loss of sandy soil. CATENA 212, 106060 (2022).
Google Scholar 
George, C. F., Macdonald, D. I. M. & Spagnolo, M. Deltaic sedimentary environments in the Niger Delta, Nigeria. J. Afr. Earth Sci. 160, 103592 (2019).
Google Scholar 
Tian, Y. et al. Petrology, lithofacies, and sedimentary environment of Upper Cretaceous Abu Roash “G” in the AESW Block, Abu Gharadig Basin, Western Desert, Egypt. J. Afr. Earth Sci. 145, 178–189 (2018).ADS 

Google Scholar 
Cheng, Z., Jalon-Rójas, I., Wang, X. H. & Liu, Y. Impacts of land reclamation on sediment transport and sedimentary environment in a macro-tidal estuary. Estuar. Coast. Shelf Sci. 242, 106861 (2020).
Google Scholar 
Wei, X., Li, X. G. & Wei, N. Fractal features of soil particle size distribution in layered sediments behind two check dams: Implications for the Loess Plateau, China. Geomorphology 266, 133–145 (2016).ADS 

Google Scholar 
Wang, S. et al. Grain size characteristics of surface sediment and its response to the dynamic sedimentary environment in Qiantang Estuary, China. Int. J. Sediment Res. 37, 457–467 (2022).
Google Scholar 
Wided, S., Jalila, S. & Kamel, R. Grain size analysis and characterization of sedimentary environment along the Bizerte Coast, N-E of Tunisia. J. Afr. Earth Sc. 184, 104353 (2021).
Google Scholar 
Cai, X., Yang, Y. E., Ringler, C., Zhao, J. & You, L. Agricultural water productivity assessment for the Yellow River Basin. Agric. Water Manag. 98, 1297 (2011).
Google Scholar 
Fu, J., Zang, C. & Zhang, J. Economic and resource and environmental carrying capacity trade-off analysis in the Haihe river basin in China. J. Clean. Prod. 270, 122271 (2020).
Google Scholar 
Zhang, K. et al. Confronting challenges of managing degraded lake ecosystems in the anthropocene, exemplified from the Yangtze River Basin in China. Anthropocene 24, 30–39 (2018).
Google Scholar 
Huybrechts, N., Zhang, Y. F. & Verbanck, M. A. A new closure methodology for 1D fully coupled models of mobile-bed alluvial hydraulics: Application to silt transport in the Lower Yellow River. Int. J. Sedim. Res. 26(1), 36–49 (2011).
Google Scholar 
Cheng, D. Z. Strengthen the financial foundation of ecological protection and development of the Yellow River Basin. People Tribune 27, 76–78 (2021).
Google Scholar 
Yang, W. N., Zhou, L. & Sun, D. Q. Ecological vulnerability assessment of the Yellow River basin based on partition: Integration concept. Remote Sens. Nat. Resourc. 33(03), 211–218 (2021).
Google Scholar 
Sun, H. et al. Exposure of population to droughts in the Haihe river basin under global warming of 1.5 and 2.0 °C Scenarios. Q. Int. 453, 74–84 (2017).ADS 

Google Scholar 
Mandelbrott, B. B. The Fractal Geometry of Nature (W.H. Freeman and Company, 1983).
Google Scholar 
Samiei-Fard, R., Heidari, A., Konyushkova, M. & Mahmoodi, S. Application of particle size distribution throughout the soil profile as a criterion for recognition of newly developed geoforms in the Southeastern Caspian coast. CATANA 203, 105362 (2021).CAS 

Google Scholar 
Guo, J. Y. et al. Grain size characteristics and source analysis of aeolian sediment feed into river in Ulanbuh Desert along bank of Yellow River. J. China Inst. Water Resour. Hydropower Res. 19(01), 15–24 (2021).
Google Scholar 
Ge, T. T., Xue, Y. J., Jiang, X. Y., Zou, L. & Wang, X. C. Sources and radiocarbon ages of organic carbon in different grain size fractions of Yellow River-transported particles and coastal sediments. Chem. Geol. 534, 119452 (2020).ADS 

Google Scholar 
Hou, C. Y., Yi, Y. J., Song, J. & Zhou, Y. Effect of water-sediment regulation operation on sediment grain size and nutrient content in the lower Yellow River. J. Clean. Prod. 279, 123533 (2021).CAS 

Google Scholar 
Ni, S. M., Feng, S. Y., Zhang, D. Q., Wang, J. G. & Cai, C. F. Sediment transport capacity in erodible beds with reconstituted soils of different textures. CATANA 183, 104197 (2019).
Google Scholar 
Li, J. L. et al. Multifractal features of the particle-size distribution of suspended sediment in the Three Gorges Reservoir, China. Int. J. Sedim. Res. 36(4), 489–500 (2021).
Google Scholar 
Wang, W. F., Liu, R. T., Guo, Z. X., Feng, Y. H. & Jiang, J. Y. Physical and chemical properties and fractal dimension distribution of soil under shrubs in the southern area of Tengger Desert. J. Desert Res. 41(01), 209–218 (2021).
Google Scholar 
Wang, K., Pei, Z. Y., Wang, W. M., Hao, S. R. & Pang, G. H. Influence of the flat cycle on the fractal characteristics of soil pore structure in Salix psammophila. Sci. Technol. Eng. 21(07), 2647–2654 (2021).
Google Scholar 
Gao, G. L. et al. Fractal approach to estimating changes in soil properties following the establishment of Caragana korshinskii shelterbelts in Ningxia, NW China. Ecol. Indic. 43, 236–243 (2014).CAS 

Google Scholar 
Liu, X., Zhang, G. C., Heathman, G. C., Wang, Y. Q. & Huang, C. H. Fractal features of soil particle-size distribution as affected by plant communities in the forested region of Mountain Yimeng, China. Geoderma 154(1), 123–130 (2009).ADS 

Google Scholar 
Xu, G. C., Li, Z. B. & Li, P. Fractal features of soil particle-size distribution and total soil nitrogen distribution in a typical watershed in the source area of the middle Dan River, China. CATENA 101, 17–23 (2013).CAS 

Google Scholar 
Zhao, S. Q., Chi, D. Q., Jia, F. C., Deng, Y. P. & Sun, C. T. Fractal characteristics of saline soil particles in different regions. Jiangsu Agric. Sci. 49(06), 203–207 (2021).
Google Scholar  More

High diet overlap is assumed to cause competition between the three dominant pelagic planktivorous mesopredators in the Baltic Sea, sprat, herring, and stickleback11,24,25. Despite this assumption, stickleback populations have increased dramatically over the past decades, which raises the question of whether and how resources are partitioned26. While previous studies of fish diet overlap have mainly relied on microscopic identification of gut content, we implemented a DNA metabarcoding approach targeting two different gene regions, the 18S rRNA gene (18S) and the mitochondrial cytochrome c oxidase I gene (COI) to reveal the taxonomic diversity of prey, and a qPCR step to quantify rotifers that are at times abundant in the Baltic Sea. Our study highlights consistency between methods, with DNA metabarcoding resolving the plankton-fish link at the highest taxonomic resolution. Our results suggest a unique niche of stickleback that may enable high population growth in the open water, despite high competition between mesopredators, although this finding needs to be confirmed at larger scale. More than half of the DNA found in herring and sprat stomach contents was assigned to Pseudocalanus, supporting previous observations of high diet overlap between the two clupeids11,12. On the other hand, the diet of stickleback differed substantially from the two clupeids, with rotifers appearing as main prey DNA in spring. The high rotifer biomass in the environment and lack of competition from other predators indicate that this novel niche utilization may support the drastic increase of pelagic stickleback in the Baltic Sea.We find that copepods dominated the gut content of the two clupeids sprat and herring. Pseudocalanus and Temora occupied most of the sequence reads of the clupeid metabarcoding, two species that are often reported as preferred prey in previous studies11,12. Despite high contributions of these two copepods, Pseudocalanus was more than four times as abundant as Temora in clupeid gut contents. A strong preference for this copepod with marine origin can further confirm the increased competition between the clupeids, as Pseudocalanus has decreased due to decreased salinity12 and shares a similar vertical distribution as clupeid during daytime27. Our study using metabarcoding further reveals a large relative quantity (11%) of the ctenophore Mertensia in the gut samples of both clupeids. Similar, Clarke et al.28 reported an important contribution of gelatinous zooplankton to upper trophic levels in the Southern Ocean. Despite high abundances of ctenophores in the Baltic Sea and their assumed importance in marine food webs19, they are not reported as food for planktivorous fish. A possible explanation is the difficulty observing them microscopically, as their digestion rate is faster than crustaceans29, and no hard parts remain in the digestive system. Further, COI detected the presence of cladocerans, which was confirmed by the microscopic survey, but underrepresented with 18S that strongly amplify copepods20. Interestingly, more than twice annelid COI reads, including the benthic macroinvertebrates Bylgides and Marenzellaria, were associated to stickleback (15%) and herring (8%) than to sprat (4%), highlighting their ability to migrate vertically. These interactions suggest that together stickleback and herring contribute to benthic-pelagic coupling when oxygen is not restricting vertical migration in the southern Baltic Sea30.Sprat and herring share a similar feeding niche, which may explain previously observed declines in body mass and stomach fullness, and supports the theory of competition between the two species31. In contrast, stickleback revealed little diet overlap with the other mesopredators. The low relative abundances of Pseudocalanus (1–8%) in metabarcoding analyses indicates that the density-dependent competition may not limit the population growth of stickleback. The copepods that were shared in the diet of stickleback, sprat, and herring were Temora, Acartia, and Centropages have increased over the last decades, as opposed to Pseudocalanus32. Our results show that stickleback are able to feed on a broader spectrum of prey and highlight that stickleback utilizes the rotifer Synchaeta baltica as prey, which is an important component of the plankton community composition in the Baltic Sea18,20. Due to the difference of prey size, we can expect an overrepresentation of copepod to rotifer sequences compared with microscopic count data. High predation rate on S. baltica is supported by both the qPCR assay as well as microscopic counts, although only the eggshells were visible but not the soft-bodied rotifer. Despite the considerably lower carbon content per S. baltica (ca. 6 µg C ind−1) compared to copepods (ca. 20 µg C ind−1)33, the high number of rotifers likely act as a major food source for stickleback. These results propose that stickleback, due to their opportunistic feeding behaviour34 and smaller size35, have a distinct feeding niche from sprat and herring in the open water, as they feed on a smaller size class of zooplankton compared to the clupeids. Thus, we cannot assume the same process of competition between clupeids and stickleback.Rotifers can at times be very abundant in the Baltic Sea, reaching densities up to 25,000 ind m−3, but their natural predators are poorly studied. An increasing trend in biomass of the two main rotifer genera (Synchaeta and Keratella) was observed since the 1990s36. In a recent study, we showed that rotifers might occupy a unique feeding niche, as direct grazers of dinoflagellate spring bloom, as well as in the recycling of organic matter in summer20. The low level of predation on rotifers by clupeid adults ( More