Beauchamp, G. Long-distance migrating species of birds travel in larger groups. Biol. Lett. 7, 692–694 (2011).
Google Scholar
Watts, H. E., Cornelius, J. M., Fudickar, A. M., Pérez, J. & Ramenofsky, M. Understanding variation in migratory movements: A mechanistic approach. Gen. Comp. Endocrinol. 256, 112–122 (2018).
Google Scholar
Amezaga, J. M., Santamaría, L. & Green, A. J. Biotic wetland connectivity—Supporting a new approach for wetland policy. Acta Oecol. 23, 213–222 (2002).
Google Scholar
O’Connell, M. Threats to waterbirds and wetlands: Implications for conservation, inventory and research. Wildfowl 51, 1–16 (2000).
Darrah, S. E. et al. Improvements to the wetland extent trends (WET) index as a tool for monitoring natural and human-made wetlands. Ecol. Ind. 99, 294–298 (2019).
Google Scholar
BirdLife International. Waterbirds are Showing Widespread Declines, Particularly in Asia. http://www.birdlife.org (2017).
Maron, M. et al. The many meanings of no net loss in environmental policy. Nat. Sustain. 1, 19–27 (2018).
Google Scholar
He, Q. Conservation: ‘No net loss’ of wetland quantity and quality. Curr. Biol. 29, R1070–R1072 (2019).
Google Scholar
Mander, L., Marie-Orleach, L. & Elliott, M. The value of wader foraging behaviour study to assess the success of restored intertidal areas. Estuar. Coast. Shelf Sci. 131, 1–5 (2013).
Google Scholar
Choi, C., Gan, X., Hua, N., Wang, Y. & Ma, Z. The habitat use and home range analysis of Dunlin (Calidris alpina) in Chongming Dongtan, China and their conservation implications. Wetlands 34, 255–266 (2014).
Google Scholar
Xia, S. et al. Identifying priority sites and gaps for the conservation of migratory waterbirds in China’s coastal wetlands. Biol. Cons. 210, 72–82 (2017).
Google Scholar
Ramsar Sites Information Service. Mai Po Marshes and Inner Deep Bay. https://rsis.ramsar.org/ris/750 (2021).
Environment Bureau. Hong Kong Biodiversity Strategy Action Plan 2016–2021 (The Government of the Hong Kong Special Administrative Region, 2016).
Sung, Y. H., Tse, I. W. L. & Yu, Y. T. Population trends of the Black-faced Spoonbill Platalea minor: Analysis of data from international synchronised censuses. Bird Conserv. Int. 28, 157–167. https://doi.org/10.1017/s0959270917000016 (2017).
Google Scholar
Wei, P. et al. Impact of habitat management on waterbirds in a degraded coastal wetland. Mar. Pollut. Bull. 124, 645–652 (2017).
Google Scholar
Cheung, S. C. The politics of wetlandscape: Fishery heritage and natural conservation in Hong Kong. Int. J. Herit. Stud. 17, 36–45 (2011).
Google Scholar
AFCD. Agriculture, Fisheries and Conservation Department (AFCD). Marine Fish Culture, Pond Fish Culture and Oyster Culture. https://www.afcd.gov.hk/english/fisheries/fish_aqu/fish_aqu_mpo/fish_aqu_mpo.html.
Yu, Y. T., Li, C. H., Tse, I. W. L. & Fong, H. N. F. International Black-Faced Spoonbill Census 2019 (The Hong Kong Bird Watching Society, 2019).
Pickett, E. J. et al. Cryptic and cumulative impacts on the wintering habitat of the endangered black-faced spoonbill (Platalea minor) risk its long-term viability. Environ. Conserv. 45, 147–154. https://doi.org/10.1017/s0376892917000340 (2018).
Google Scholar
The Hong Kong Bird Watching Society. Black-Faced Spoonbill Population Hits Record High. Number in HK Continues to Decline. Protection of Deep Bay in Urgent Need. https://cms.hkbws.org.hk/cms/ (2020).
Swennen, C. & Yu, Y. T. Food and feeding behavior of the black-faced spoonbill. Waterbirds 28, 19–27. https://doi.org/10.1675/1524-4695(2005)028[0019:Fafbot]2.0.Co;2 (2005).
Google Scholar
Nichols, R. V., Åkesson, M. & Kjellander, P. Diet assessment based on rumen contents: A comparison between DNA metabarcoding and macroscopy. PLoS ONE 11, e0157977 (2016).
Google Scholar
Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).
Google Scholar
Elbrecht, V., Vamos, E. E., Meissner, K., Aroviita, J. & Leese, F. Assessing strengths and weaknesses of DNA metabarcoding-based macroinvertebrate identification for routine stream monitoring. Methods Ecol. Evol. 8, 1265–1275 (2017).
Google Scholar
McInnes, J. C. et al. Optimised scat collection protocols for dietary DNA metabarcoding in vertebrates. Methods Ecol. Evol. 8, 192–202 (2017).
Google Scholar
Thuo, D. et al. Food from faeces: Evaluating the efficacy of scat DNA metabarcoding in dietary analyses. PLoS ONE 14, e0225805 (2019).
Google Scholar
De Sousa, L., Silva, S. M. & Xavier, R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystem. Environ. DNA 1, 199–214 (2019).
Google Scholar
Ueng, Y. T., Perng, J. J., Wang, J. P., Weng, J. H. & Hou, P. C. Diet of the black-faced spoonbill wintering at Chiku Wetland in Southwestern Taiwan. Waterbirds 29, 185–191 (2006).
Google Scholar
Veen, J., Overdijk, O. & Veen, T. The diet of an endemic subspecies of the Eurasian Spoonbill Platalea leucorodia balsaci, breeding at the Banc d’Arguin, Mauritania. Ardea 100, 123–130 (2012).
Google Scholar
Lee, S. Y. The Mangrove Ecosystem of Deep Bay and the Mai Po Marshes, Hong Kong (Hong Kong University Press, 1999).
Wong, L. C., Corlett, R. T., Young, L. & Lee, J. S. Comparative feeding ecology of Little Egrets on intertidal mudflats in Hong Kong, South China. Waterbirds 23, 214–225 (2000).
Yang, K. Y., Lee, S. Y. & Williams, G. A. Selective feeding by the mudskipper (Boleophthalmus pectinirostris) on the microalgal assemblage of a tropical mudflat. Mar. Biol. 143, 245–256 (2003).
Google Scholar
Froese, R., Pauly, D. & eds. FishBase. World Wide Web Electronic Publication. https://www.fishbase.org, version 12/2019 (2019).
Aguilera, E., Ramo, C. & de le Court, C. Food and feeding sites of the Eurasian spoonbill (Platalea leucorodia) in southwestern Spain. Colon. Waterbirds 19, 159–166 (1996).
Google Scholar
Yu, Y. T. & Swennen, C. K. Habitat use of the black-faced spoonbill. Waterbirds 27, 129–135 (2004).
Google Scholar
World Wide Fund Hong Kong. Mai Po Nature Reserve Habitat Management, Monitoring and Research Plan 2013–2018 (World Wide Fund Hong Kong, 2013).
Sazima, I. Waterbirds catch and release a poisonous fish at a mudflat in southeastern Australia. Ornithol. Res. 27, 126–128 (2019).
Google Scholar
Marchetti, K. & Price, T. Differences in the foraging of juvenile and adult birds: The importance of developmental constraints. Biol. Rev. 64, 51–70 (1989).
Google Scholar
Jiguet, F. Arthropods in diet of Little Bustards Tetrax tetrax during the breeding season in western France. Bird Study 49, 105–109 (2002).
Google Scholar
Birks, J. D. S. & Dunstone, N. Sex-related differences in the diet of the mink Mustela vison. Ecography 8, 245–252 (1985).
Google Scholar
Mata, V. A. et al. Female dietary bias towards large migratory moths in the European free-tailed bat (Tadarida teniotis). Biol. Lett. 12, 20150988 (2016).
Google Scholar
Carreiro, A. R. et al. Metabarcoding, stables isotopes, and tracking: Unraveling the trophic ecology of a winter-breeding storm petrel (Hydrobates castro) with a multimethod approach. Mar. Biol. 167, 14 (2020).
Google Scholar
Rose, L. M. Sex differences in diet and foraging behavior in white-faced capuchins (Cebus capucinus). Int. J. Primatol. 15, 95–114 (1994).
Google Scholar
Beeston, R., Baines, D. & Richardson, M. Seasonal and between-sex differences in the diet of Black Grouse Tetrao tetrix. Bird Study 52, 276–281 (2005).
Google Scholar
Durell, S. L. V. D., Goss-Custard, J. D. & Caldow, R. W. G. Sex-related differences in diet and feeding method in the oystercatcher Haematopus ostralegus. J. Anim. Ecol. 62, 205–215 (1993).
Google Scholar
Faegre, S. K., Nietmann, L., Hannon, P., Ha, J. C. & Ha, R. R. Age-related differences in diet and foraging behavior of the critically endangered Mariana Crow (Corvus kubaryi), with notes on the predation of Coenobita hermit crabs. J. Ornithol. 161, 149–158 (2020).
Google Scholar
Dunn, E. K. Effect of age on the fishing ability of sandwich terns Sterna sandvicensis. Ibis 114, 360–366 (1972).
Google Scholar
Watson, M. J. & Hatch, J. J. Differences in foraging performance between juvenile and adult roseate terns at a pre-migratory staging area. Waterbirds 22, 463–465 (1999).
Google Scholar
AEC Limited. Ecological Monitoring and Adaptive Management Advice Services for Lok Ma Chau and West Rail Wetlands. Lok Ma Chau Habitat Creation and Management Plan (AEC Limited, 2019).
The Hong Kong Bird Watching Society. Hong Kong Fishpond Conservation Scheme Project. https://cms.hkbws.org.hk/cms/ (2020).
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).
Google Scholar
Edgar, R. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461. https://doi.org/10.1093/bioinformatics/btq461 (2010).
Google Scholar
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12. https://doi.org/10.14806/ej.17.1.200 (2011).
Google Scholar
Andrews, S., Krueger, F. & Segonds-Pichon, A. FastQC a Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahe, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584. https://doi.org/10.7717/peerj.2584 (2016).
Google Scholar
Edgar, R. C. & Flyvbjerg, H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics (Oxford, England) 31, 3476–3482. https://doi.org/10.1093/bioinformatics/btv401 (2015).
Google Scholar
Edgar, R. C. UNOISE2: Improved error-correction for Illumina 16S and ITS amplicon sequencing. BioRxiv https://doi.org/10.1101/081257 (2016).
Google Scholar
Edgar, R. SINTAX: A simple non-Bayesian taxonomy classifier for 16S and ITS sequences. BioRxiv https://doi.org/10.1101/074161 (2016).
Google Scholar
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581 (2016).
Google Scholar
Machida, R. J., Leray, M., Ho, S. L. & Knowlton, N. Metazoan mitochondrial gene sequence reference datasets for taxonomic assignment of environmental samples. Sci. Data 4, 170027. https://doi.org/10.1038/sdata.2017.27 (2017).
Google Scholar
Sato, K., Miya, M., Fukunaga, T., Sado, T. & Iwasaki, W. MitoFish and MiFish pipeline: A mitochondrial genome database of fish with an analysis pipeline for environmental DNA metabarcoding. Mol. Biol. Evol. 35, 1553–1555 (2018).
Google Scholar
Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, D590-596. https://doi.org/10.1093/nar/gks1219 (2013).
Google Scholar
Kahlke, T. & Ralph, P. J. BASTA—Taxonomic classification of sequences and sequence bins using last common ancestor estimations. Methods Ecol. Evol. 10, 100–103. https://doi.org/10.1111/2041-210X.13095 (2019).
Google Scholar
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406. https://doi.org/10.1111/mec.14734 (2019).
Google Scholar
Lahti, L. & Shetty, S. Microbiome R Package Version 1.6.0. http://microbiome.github.io (2012).
Oksanen, J. et al. vegan: Community Ecology Package Version 2.5–6. https://cran.r-project.org, https://github.com/vegandevs/vegan (2019).
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217. https://doi.org/10.1371/journal.pone.0061217 (2013).
Google Scholar
Martinez-Arbizu, P. pairwiseAdonis: Pairwise Multilevel Comparison Using Adonis. R Package Version 0.3. https://github.com/pmartinezarbizu/pairwiseAdonis (2019).
Steinberger, A. J. Asteinberger9/seq_scripts: Release v1. https://github.com/asteinberger9/seq_scripts (2018).
ArcGIS. ArcGIS Version 10.7. https://desktop.arcgis.com/en/arcmap/ (2020).
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