Ecology

Adams, D. C., Rohlf, F. J. & Slice, D. E. A field comes of age: Geometric morphometrics in the 21st century. Hystrix 24, 7–14. https://doi.org/10.4404/hystrix-24.1-6283 (2013).Article 

Google Scholar 
Terray, L. et al. Skull morphological evolution in Malagasy endemic Nesomyinae rodents. PLoS ONE 17, e0263045. https://doi.org/10.1371/journal.pone.0263045 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Viacava, P., Baker, A. M., Blomberg, S. P., Phillips, M. J. & Weisbecker, V. Using 3D geometric morphometrics to aid taxonomic and ecological understanding of a recent speciation event within a small Australian marsupial (Antechinus: Dasyuridae). Zool. J. Linn. Soc. 1–16. https://doi.org/10.1093/zoolinnean/zlab048 (2021).Brassard, C. et al. Morphological and functional divergence of the lower jaw between native and invasive red foxes. J. Mamm. Evol. 29, 335–352. https://doi.org/10.1007/s10914-021-09593-2 (2022).Article 

Google Scholar 
Boessneck, J. & von den Driesch, A. The significance of measuring animal bones from archaeological sites. In Approaches to Faunal Analysis in the Middle East (eds Meadows, R. H. & Zeder, M. A.) 5–39 (Peabody Museum Bulletin 2, 1978).
Google Scholar 
Serjeantson, D. ‘Science is measurement’; ABMAP, a database of domestic animal bone measurements. Environ. Archaeol. 10, 97–103. https://doi.org/10.1179/env.2005.10.1.97 (2005).Article 

Google Scholar 
Haruda, A. F. Separating sheep (Ovis aries L.) and goats (Capra hircus L.) using geometric morphometric methods: An investigation of astragalus morphology from late and final Bronze Age Central Asian contexts. Int. J. Osteoarchaeol. 27, 551–562 (2017).Article 

Google Scholar 
Davis, S. J. M. Towards a metrical distinction between sheep and goat astragali. In Economic Zooarchaeology: Studies in Hunting, Herding and Early Agriculture (eds Rowley-Conwy, P. et al.) 93–138 (Oxbow Books Limited, 2019).
Google Scholar 
Jeanjean, M. et al. Sorting the flock: Quantitative identification of sheep and goat from isolated third lower molars and mandibles through geometric morphometrics. J. Archaeol. Sci. 141, 105580. https://doi.org/10.1016/j.jas.2022.105580 (2022).Article 

Google Scholar 
Evin, A. et al. Phenotype and animal domestication: A study of dental variation between domestic, wild, captive, hybrid and insular Sus scrofa. BMC Evol. Biol. 15, 1–16. https://doi.org/10.1186/s12862-014-0269-x (2015).Article 

Google Scholar 
Harbers, H. et al. The mark of captivity: Plastic responses in the ankle bone of a wild ungulate (Sus scrofa). R. Soc. Open Sci. 7, 192039. https://doi.org/10.1098/rsos.192039 (2020).Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 
Drake, A. G., Coquerelle, M. & Colombeau, G. 3D morphometric analysis of fossil canid skulls contradicts the suggested domestication of dogs during the late Paleolithic. Sci. Rep. 5, 8299. https://doi.org/10.1038/srep08299 (2015).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Ventresca Miller, A. R., Haruda, A., Varfolomeev, V., Goryachev, A. & Makarewicz, C. A. Close management of sheep in ancient Central Asia: Evidence for foddering, transhumance, and extended lambing seasons during the Bronze and Iron Ages. Sci. Technol. Archaeol. Res. 6, 41–60. https://doi.org/10.1080/20548923.2020.1759316 (2020).Duval, C., Lepetz, S., Horard-Herbin, M.-P. & Cucchi, T. Did Romanization impact Gallic pig morphology? New insights from molar geometric morphometrics. J. Archaeol. Sci. 57, 345–354. https://doi.org/10.1016/j.jas.2015.03.004 (2015).Article 

Google Scholar 
Davis, S. J. M. Zooarchaeological evidence for Moslem and Christian improvements of sheep and cattle in Portugal. J. Archaeol. Sci. 35, 991–1010. https://doi.org/10.1016/j.jas.2007.07.001 (2008).Article 

Google Scholar 
Samper Carro, S. C., Louys, J. & Oonnor, S. Shape does matter: A geometric morphometric approach to shape variation in Indo-Pacific fish vertebrae for habitat identification. J. Archaeol. Sci. 99, 124–134. https://doi.org/10.1016/j.jas.2018.09.010 (2018).Stimpson, C. M. A 48,000 year record of swiftlets (Aves: Apodidae) in North-western Borneo: Morphometric identifications and palaeoenvironmental implications. Palaeogeogr. Palaeoclimatol. Palaeoecol. 374, 132–143. https://doi.org/10.1016/j.palaeo.2013.01.011 (2013).Article 

Google Scholar 
Medina, M. E., Picasso, M. B. J., Campos, M. R. & Avila, N. C. Tarsometatarsus, eggshells, and the species level identification of large-sized flightless birds from Boyo Paso 2 (Sierras of Córdoba, Argentina). Int. J. Osteoarchaeol. 29, 584–594. https://doi.org/10.1002/oa.2754 (2019).Article 

Google Scholar 
Weaver, L. N. & Grossnickle, D. M. Functional diversity of small-mammal postcrania is linked to both substrate preference and body size. Curr. Zool. 66, 539–553. https://doi.org/10.1093/cz/zoaa057 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Chen, X., Milne, N. & O’Higgins, P. Morphological variation of the thoracolumbar vertebrae in Macropodidae and its functional relevance. J. Morphol. 266, 167–181. https://doi.org/10.1002/jmor.10370 (2005).Article 
PubMed 

Google Scholar 
Etienne, C., Filippo, A., Cornette, R. & Houssaye, A. Effect of mass and habitat on the shape of limb long bones: A morpho-functional investigation on Bovidae ( Mammalia: Cetartiodactyla ). J. Anat. 238, 886–904. https://doi.org/10.1111/joa.13359 (2020).Article 
PubMed 

Google Scholar 
Bassarova, M., Janis, C. M. & Archer, M. The calcaneum-on the heels of marsupial locomotion. J. Mamm. Evol. 16, 1–23. https://doi.org/10.1007/s10914-008-9093-7 (2009).Article 

Google Scholar 
Janis, C. M., Buttrill, K. & Figueirido, B. Locomotion in extinct giant kangaroos: Were Sthenurines hop-less monsters?. PLoS ONE 9, e109888. https://doi.org/10.1371/journal.pone.0109888 (2014).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Argot, C. Functional-adaptive analysis of the hindlimb anatomy of extant marsupials and the paleobiology of the paleocene marsupials Mayulestes ferox and Pucadelphys andinus. J. Morphol. 253, 76–108. https://doi.org/10.1002/jmor.1114 (2002).Article 
PubMed 

Google Scholar 
Mein, E. & Manne, T. Identifying marsupials from Australian archaeological sites: Current methodological challenges and opportunities in zooarchaeological practice. Archaeol. Ocean. 56, 133–141. https://doi.org/10.1002/arco.5234 (2021).Article 

Google Scholar 
Woinarski, J. C. Z. et al. Reading the black book: The number, timing, distribution and causes of listed extinctions in Australia. Biol. Conserv. 239, 108261. https://doi.org/10.1016/j.biocon.2019.108261 (2019).Article 

Google Scholar 
Garvey, J. Preliminary zooarchaeological interpretations from Kutikina Cave, south-west Tasmania. Aust. Aborig. Stud. 1, 57–62 (2006).
Google Scholar 
Veth, P. et al. Montebello Islands Archaeology: Late Quaternary Foragers on an Arid Coastline. (BAR Publishing, 2007).Morse, K. Who can see the sea? Prehistoric Aboriginal occupation of the Cape Range peninsula. Rec. West. Aust. Mus. Suppl. 45, 227–242 (1993).
Google Scholar 
Warburton, N. M. & Prideaux, G. Functional pedal morphology of the extinct tree-kangaroo Bohra (Diprotodontia: Macropodidae). In Macropods: The Biology of Kangaroos, Wallabies, and Rat-Kangaroos (eds Coulson, G. & Eldridge, M.) 137–151 (CSIRO Publishing, 2010).
Google Scholar 
Bishop, N. Functional anatomy of the macropodid pes. Proc. Linn. Soc. New South Wales 117, 17–50 (1997).ADS 

Google Scholar 
Szalay, F. S. Evolutionary History of the Marsupials and an Analysis of Osteological Characters. (Cambridge University Press, 1994).Veth, P. et al. Early human occupation of a maritime desert, Barrow Island, north-west Australia. Quat. Sci. Rev. 168, 19–29. https://doi.org/10.1016/j.quascirev.2017.05.002 (2017).Article 
ADS 

Google Scholar 
Moro, D. & Lagdon, R. History and environment of Barrow Island. Rec. West. Aust. Mus. Suppl. 83, 1–8. https://doi.org/10.18195/issn.0313-122x.83.2013.001-008 (2013).Veth, P., Ditchfield, K. & Hook, F. Maritime deserts of the Australian northwest. Aust. Archaeol. 79, 156–166. https://doi.org/10.1080/03122417.2014.11682032 (2014).Article 

Google Scholar 
Morse, K. Coastwatch: Pleistocene resource use on the Cape Range peninsula. In Australian Coastal Archaeology (eds Hall, J. & McNiven, I. J.) 73–78 (ANH Publications, 1999).
Google Scholar 
Baynes, A. & McDowell, M. C. The original mammal fauna of the Pilbara biogeographic region of north-western Australia. Rec. West. Aust. Mus. Suppl. 78, 285–298. https://doi.org/10.18195/issn.0313-122x.78(1).2010.285-298 (2010).Article 

Google Scholar 
Shortridge, G. C. An account of the geographical distribution of the marsupials and monotremes of south-west Australia, having special reference to the specimens collected during the Balston expedition of 1904–1907. Proc. Zool. Soc. Lond. 74, 803–848. https://doi.org/10.1111/j.1469-7998.1910.tb06974.x (1909).Article 

Google Scholar 
Ballard, C. K. Use of Epiphyseal and Total Fusion Scores as Methods of Age Estimation and Evaluation of Morphological Indices in the Macropodidae. (Northern Illinois University, 2007).Rose, R. W. Age estimation of the Tasmanian bettong (Bettongia gaimardi) (Marsupialia: Potoroidae). Wildl. Res. 16, 251–261. https://doi.org/10.1071/WR9890251 (1989).Article 

Google Scholar 
Johnson, P. M. & Delean, S. Reproduction in the northern bettong, Bettongia tropica Wakefield (Marsupialia: Potoroidae), in captivity, with age estimation and development of pouch young. Wildl. Res. 28, 79–85. https://doi.org/10.1071/WR00007 (2001).Article 

Google Scholar 
Thompson, C. K., Wayne, A. F., Godfrey, S. S. & Andrew Thompson, R. C. Survival, age estimation and sexual maturity of pouch young of the brush-tailed bettong (Bettongia penicillata) in captivity. Aust. Mammal. 37, 29–38. https://doi.org/10.1071/AM14025 (2015).Article 

Google Scholar 
Janis, C. M. Correlation of cranial and dental variables with dietary preferences in mammals: A comparison of macropodoids and ungulates. Mem. – Queensl. Museum 28, 349–366 (1990).
Google Scholar 
Sharman, G. B., Frith, H. J. & Calaby, J. H. Growth of the pouch young, tooth eruption and age determination in the red kangaroo, Megaleia rufa. CSIRO Wildl. Res. 9, 20–49. https://doi.org/10.1071/cwr9640020 (1964).Article 

Google Scholar 
Newsome, A. E., Merchant, J. C., Bolton, B. L. & Dudziński, M. L. Sexual dimorphism in molar progression and eruption in the agile wallaby. Wildl. Res. 4, 1–5. https://doi.org/10.1071/WR9770001 (1977).Article 

Google Scholar 
Poole, W. E., Merchant, J. C., Carpenter, S. M. & Calaby, J. H. Reproduction, growth and age determination in the yellow-footed rock-wallaby Petrogale xanthopus Gray, in captivity. Wildl. Res. 12, 127–136. https://doi.org/10.1071/WR9850127 (1985).Article 

Google Scholar 
Delaney, R. & Marsh, H. Estimating the age of wild rock-wallabies by dental radiography: A basis for quantifying the age structure of a discrete colony of Petrogale assimilis. Wildl. Res. 22, 547–559. https://doi.org/10.1071/WR9950547 (1995).Article 

Google Scholar 
Kido, N., Tanaka, S., Wada, Y., Sato, S. & Omiya, T. Molar eruption and identification of the eastern grey kangaroo (Macropus giganteus) at different ages. J. Vet. Med. Sci. 80, 648–652. https://doi.org/10.1292/jvms.17-0069 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Koo, T. K. & Li, M. Y. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J. Chiropr. Med. 15, 155–163. https://doi.org/10.1016/j.jcm.2016.02.012 (2016).Article 
PubMed 
PubMed Central 

Google Scholar 
Claude, J. Log-shape ratios, Procrustes superimposition, elliptic Fourier analysis: Three worked examples in R. Hystrix 24, 94–102. https://doi.org/10.4404/hystrix-24.1-6316 (2013).Article 

Google Scholar 
Mosimann, J. E. Size allometry: Size and shape variables with characterizations of the Lognormal and generalized gamma distributions. J. Am. Stat. Assoc. 65, 930–945. https://doi.org/10.2307/2284599 (1970).Article 
MATH 

Google Scholar 
Kovarovic, K., Aiello, L. C., Cardini, A. & Lockwood, C. A. Discriminant function analyses in archaeology: Are classification rates too good to be true ?. J. Archaeol. Sci. 38, 3006–3018. https://doi.org/10.1016/j.jas.2011.06.028 (2011).Article 

Google Scholar 
Ramayah, T. et al. Discriminant analysis: An illustrated example. Afr. J. Bus. Manag. 4, 1654–1667 (2010).
Google Scholar 
Sanchez, P. M. The unequal group size problem in discriminant analysis. J. Acad. Mark. Sci. 2, 629–633. https://doi.org/10.1007/BF02729456 (1974).Article 

Google Scholar 
Hair, J. F., Black, W. C., Babin, B. J. & Anderson, R. E. Multivariate Data Analysis. (Cengage, 2018).Venables, W. N. & Ripley, B. D. Modern Applied Statistics with S. (Springer, 2002).Fox, J. & Weisberg, S. An R Companion to Applied Regression. (Sage 2019).Harrell, F. E. & Dupont, C. Harrell Miscellaneous. (2021).Oksanen, J. et al. Community Ecology Package (2020).Kassambara, A. Pipe-Friendly Framework for Basic Statistical Tests. (2021).Korkmaz, S., Goksuluk, D. & Zararsiz, G. MVN: An R Package for Assessing Multivariate Normality. R J. 6, 151–162 (2014).Article 

Google Scholar 
Revelle, W. Procedures for Psychological, Psychometric, and Personality Research. (2022).Weisbecker, V. et al. Individual variation of the masticatory system dominates 3D skull shape in the herbivory-adapted marsupial wombats. Front. Zool. 16, 41. https://doi.org/10.1186/s12983-019-0338-5 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Richards, J. D. et al. The biology of banded (Lagostrophus fasciatus) and rufous (Lagorchestes hirsutus) hare-wallabies (Diprotodontia: Macropodidae) on Dorre and Bernier Islands, Western Australia. Wildl. Res. 28, 311–322. https://doi.org/10.1071/WR99109 (2001).Article 
ADS 

Google Scholar 
Ingleby, S. & Westoby, M. Habitat requirements of the spectacled hare-wallaby (Lagorchestes conspicillatus) in the Northern Territory and Western Australia. Wildl. Res. 19, 721–741. https://doi.org/10.1071/WR9920721 (1992).Article 

Google Scholar 
Helgen, K. M. & Flannery, T. F. Taxonomy and historical distribution of the wallaby genus Lagostrophus. Aust. J. Zool. 51, 199–212. https://doi.org/10.1071/ZO02078 (2003).Article 

Google Scholar 
McDowell, M. C. et al. Morphological and molecular evidence supports specific recognition of the recently extinct Bettongia anhydra (Marsupialia: Macropodidae). J. Mammal. 96, 287–296. https://doi.org/10.1093/jmammal/gyv006 (2015).Article 

Google Scholar 
Ingleby, S. Distribution and status of the northern nailtail wallaby, Onychogalea unguífera (Gould, 1841). Wildl. Res. 18, 655–676. https://doi.org/10.1071/WR9910655 (1991).Article 

Google Scholar 
Peters, C. et al. Species identification of Australian marsupials using collagen fingerprinting. R. Soc. Open Sci. 8, 211229. https://doi.org/10.1098/rsos.211229 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Prince, R. I. T. Banded hare-wallaby. In Mammals of Australia (eds Strahan, R. & van Dyck, S.) 406–408 (Reed New Holland, 2008).
Google Scholar 
De Deckker, P., Barrows, T. T. & Rogers, J. Land-sea correlations in the Australian region: Post-glacial onset of the monsoon in northwestern Western Australia. Quat. Sci. Rev. 105, 181–194. https://doi.org/10.1016/j.quascirev.2014.09.030 (2014).Article 
ADS 

Google Scholar 
Ward, I. et al. 50,000 years of archaeological site stratigraphy and micromorphology in Boodie Cave, Barrow Island, Western Australia. J. Archaeol. Sci. Rep. 15, 344–369. https://doi.org/10.1016/j.jasrep.2017.08.012 (2017).Article 

Google Scholar 
Skippington, J., Manne, T. & Veth, P. Isotopic indications of late Pleistocene and Holocene paleoenvironmental changes at Boodie Cave archaeological site, Barrow Island, Western Australia. Molecules 26, 2585. https://doi.org/10.3390/molecules26092582 (2021).Article 
CAS 

Google Scholar 
Baynes, A. & Jones, B. The mammals of Cape Range Peninsula, north-western Australia. Rec. West. Aust. Mus. Suppl. 45, 207–255 (1993).
Google Scholar 
Piper, C. & Veth, P. Palaeoecology and sea level changes: Decline of mammal species richness during late Quaternary island formation in the Montebello Islands, north-western Australia. Palaeontol. Electron. 24, a20. https://doi.org/10.26879/1050 (2021).Article 

Google Scholar 
Lyman, R. L. The history of ‘laundry lists’ in North American zooarchaeology. J. Anthropol. Archaeol. 39, 42–50. https://doi.org/10.1016/j.jaa.2015.02.003 (2015).Article 

Google Scholar 
Guillaud, E., Cornette, R. & Béarez, P. Is vertebral form a valid species-specific indicator for salmonids? The discrimination rate of trout and Atlantic salmon from archaeological to modern times. J. Archaeol. Sci. 65, 84–92. https://doi.org/10.1016/j.jas.2015.11.010 (2016).Article 

Google Scholar 
Monchot, H. & Gendron, D. Disentangling long bones of foxes (Vulpes vulpes and Alopex lagopus) from artic archaeological sites. J. Archaeol. Sci. 37, 799–806. https://doi.org/10.1016/j.jas.2009.11.009 (2010).Article 

Google Scholar  More

Systematic reviewWe searched relevant publications through Web of Science (all databases), Google Scholar, and the China National Knowledge Infrastructure Database between 1945 and March 2021 with the following combinations of keywords: (drain* OR lower* water table OR standing water depth OR ground water table level drawdown OR decline OR drought OR dry*) with (peatland* OR mire* OR fen OR bog OR swamp OR marsh*) with (soil respiration OR heterotrophic respiration OR microbial respiration OR soil CO2 OR soil carbon decompos* OR soil carbon minerali* or peat subsidence). Using these search terms, we initially identified 2120 different publications. To reliably evaluate WT decline impacts on SR and peat subsidence-associated soil CO2 emissions, the following further criteria were applied:1) Only paired studies with pristine peatland (i.e., undrained, near-natural peatland without direct drainage history) as a control and pristine peatland with direct WT decline (due to drainage and land use or climate-induced drying) as a treatment were included by carefully checking the descriptions of field conditions from the publications. For the pristine peatlands, we included the peatland only if the peat soil had at least 30% dry organic matter, a peat depth of >40 cm1, and did not have any direct drainage history2. We acknowledge that few, if any, untouched and completely pristine peatlands currently exist, particularly in Europe.2) WT decline in peatlands referred to only the WT depth lowered by drainage or climate-induced drying and/or additional management practices related to C or N input (e.g., manure/N fertilizers); treatments in which WT decline was combined with manipulated warming, elevated CO2, N deposition, etc., were excluded, while individual treatments (i.e., peatlands affected by WT decline without additional warming, elevated CO2, N deposition treatments, etc.) were included, as the primary objective of this study was to evaluate the responses of peatland C decomposition to WT decline.3) Each individual study included SR or at least one of its components (HR and AR), and the measurement intervals were at least monthly. The in situ measurements of SR or its components (HR and AR) covered at least the growing or nongrowing season in temperate/boreal climate zones and the whole wet or dry season in (sub)tropical climate zones.4) Both in situ and soil core/microcosm/mesocosm measurements of SR or its components (HR and AR) were included. SR and its components were exclusively measured using the chamber method. The results of the latter group were used to test the results of the former.Finally, 386 paired in situ and 21 paired soil core incubation measurements of SR or its components (HR and AR) were extracted from 63 in situ studies and 9 soil core studies, respectively (see Supplementary Data A). Furthermore, to estimate HR emissions from global drained peatlands, the in situ measured paired peat subsidence rate (Rps, cm yr–1) and drainage duration (i.e., years since first drainage) and the proportion of peat subsidence rate attributed to oxidation (Po, %) and drainage duration, as well as the soil (0–30 cm) organic C and bulk density in pristine peatlands, were extracted from peer-reviewed publications. In drained boreal and temperate peatlands, most studies measured the total subsidence (in meter) during a certain drainage period, therefore the average Rps was calculated as the ratio of total subsidence and drainage years. It was assumed that the Rps was faster at the beginning and lower at the end of drainage duration, so the average subsidence rate is the rate for the middle year of the drainage duration41. The remaining studies directly showed the in situ measured Rps at the ith year of drainage. A similar procedure was applied for the Po in the ith year of drainage. In sum, 230 paired Rps–drainage duration observations and 49 paired Po–drainage duration observations, as well as 76 SOC and 63 BD in pristine peatlands, were taken from 80, 25, 58, and 44 studies, respectively (see Supplementary Data B).Data compilationTo systematically evaluate the impacts of WT decline on SR in pristine peatlands and clarify the underlying mechanisms, we obtained data related to SR and its components (HR and AR) together with environmental variables such as the mean annual temperature [MAT], mean annual precipitation [MAP], peat depth [PD], WT depth [WTD], soil water content [SWC], soil temperature [Ts], soil redox potential [Eh], soil air oxygen level [O2], soil bulk density [BD], soil pH [pH], soil organic carbon [SOC], soil total nitrogen [TN], soil total phosphorus [TP], soil ammonium [({{{{rm{NH}}}}}_{4}^{+})], soil nitrate [({{{{rm{NO}}}}}_{3}^{-})], soil dissolved organic carbon [DOC], microbial biomass carbon [MBC], microbial biomass nitrogen [MBN], dissolved total phosphorus [DTP], belowground biomass [BGB], iron [Fe3+, Fe2+] and sulfate [({{{{rm{SO}}}}}_{4}^{2-})] when possible. If available, other important information, such as geographic location (latitude, longitude), climate and WT decline driver and duration, intensity, peatland type, Rps, Po, nutrient type, inundated condition, microtopography, and plant functional types, was recorded. For WT decline intensity, net WT declines greater and less than 30 cm were defined as deep and shallow declines, respectively, according to the IPCC wetland report42. The abovementioned information about pristine peatlands and peatlands affected by WT decline is compiled in Supplementary Data A and B.We subsequently extracted the mean ((bar{X})), standard deviation (SD) and replicates (n) from different publications. If studies reported standard error (SE) rather than SD, then SD was calculated by SE (sqrt{n}). If studies reported only the median, maximum, minimum, and 25th and 75th percentiles, then the mean and SD were estimated following the mathematical equations recommended by ref. 60. If neither SD nor SE was reported, then the missing SD was estimated by multiplying the reported mean by the average coefficient of variation (CV) obtained from the remaining observations, resulting in both the mean and SD being reported61. The data were either obtained directly from tables and texts or extracted by digitizing graphs using Getdata Graph Digitizer software (version 2.26, Russia).The final database consisted of 250 paired SR, 101 paired HR and 35 paired AR in situ observations. Only 35 paired observations simultaneously reported SR, HR, and AR. Twenty-one paired SR soil core incubation measurements were also collected to test the results of the in situ measurements. The dataset mainly originated from Europe, North America, and Southeast Asia, and most studies ( >70%) were conducted in temperate and boreal peatlands in the Northern Hemisphere (Fig. 1a). Moreover, 230 paired Rps–drainage duration observations and 49 paired Po–drainage duration observations (Fig. 5a, b) and an additional 485 drainage year (Supplementary Fig. 9) observations classified by climate zone (i.e., boreal, temperate and tropical) and land use (i.e., agriculture, forestry, and grassland) were collected. A total of 76 SOC and 63 BD measurements from pristine peatlands categorized by climate zone (i.e., boreal, temperate, and tropical) were extracted to estimate Rps by oxidation and associated soil HR from global pristine peatlands due to drainage activities (Supplementary Fig. 10 and Supplementary Data B). In this study, we were unable to estimate climate drying-induced net CO2 emissions through soil HR, as the areas of pristine peatlands affected by climate drying currently remain unknown.Meta-analysisTo assess the relative changes in SR and its components (HR and AR), as well as environmental variables (e.g., SOC, BD, Ts, etc.) due to WT decline, the log-transformed response ratio (RR) was used:62$${{{mathrm{ln}}}}({{{rm{RR}}}})=,{{{mathrm{ln}}}}({X}_{{{{rm{t}}}}}/{X}_{{{{rm{c}}}}})$$
(1)
The results are presented as the percent change ((elnRR  – 1) × 100). The variance (v) of RR was estimated using the following equation:$$v=frac{{{{{rm{SD}}}}}_{{{{rm{t}}}}}^{2}}{{n}_{{{{rm{t}}}}},{X}_{{{{rm{t}}}}}^{2}}+frac{{{{{rm{SD}}}}}_{{{{rm{c}}}}}^{2}}{{n}_{{{{rm{c}}}}},{X}_{{{{rm{c}}}}}^{2}}$$
(2)
where Xt and Xc indicate the means of the treatment and control, SDt and SDc indicate the SDs of the treatment and control and nt and nc indicate the numbers of replicates in the treatment and control, respectively.However, in our study, approximately 60% of the WTD and Eh observations for the peatlands in pristine condition (control) and affected by WT decline (treatment) showed opposite signs; e.g., the pristine peatlands generally exhibited positive WTDs (higher than the peat surface) and negative Eh values, while those affected by WT decline exhibited negative WTDs (lower than the peat surface) and positive Eh values. Since it is impossible to calculate the logarithm of negative values, we introduced a new study index (net changes) for these two variables in our meta-analysis according to ref. 63:$$D={X}_{{{{rm{t}}}}}-{X}_{{{{rm{c}}}}}$$
(3)
where Xt and Xc indicate the paired annual mean WTD and Eh for the treatment and control, respectively, and D indicates the difference between the treatment and control.The SD and variance (v) of D were estimated using the following equation:$${{{rm{SD}}}}=sqrt{frac{({n}_{{{{rm{c}}}}}-1);{{{{rm{SD}}}}}_{{{{rm{c}}}}}^{2}+({n}_{{{{rm{t}}}}}-1);{{{{rm{SD}}}}}_{{{{rm{t}}}}}^{2}}{{n}_{{{{rm{c}}}}}+{n}_{{{{rm{t}}}}}-2}}$$
(4)
$$v=frac{{{{{rm{SD}}}}}_{{{{rm{t}}}}}^{2}}{{n}_{{{{rm{t}}}}}}+frac{{{{{rm{SD}}}}}_{{{{rm{c}}}}}^{2}}{{n}_{{{{rm{c}}}}}}$$
(5)
where SDt and SDc indicate the SD of the treatment and control and nt and nc indicate the number of replicates for the treatment and control, respectively.The weighted mean RR or D was calculated by individual RR or D with bias-corrected 95% confidence intervals (CIs) using the rma.mv function in the metafor package in R software (R core team, 2019)64, in which the variable “study” was regarded as a random effect to account for the dependence of observations derived from the same study. The impact of WT decline on a response variable was considered significant if the 95% CI did not overlap 065. Differences between subgroups (e.g., WT decline driver, climate zone, drainage duration) were considered significant if the 95% CIs did not overlap each other65. The frequency distribution of RR was calculated to test variability among individual studies using the Gaussian function (i.e., normal distribution)66.Estimation of peat subsidence rate by oxidation and associated HR rateDrainage has induced widespread peat subsidence and associated large CO2 release through soil HR and consequently reduced the sustainable utilization of drained peatlands and contributed to global warming11,12. In this study, we estimated the spatial patterns of Rps by oxidation and associated soil HR from global drained peatlands. Using the 230 paired Rps and drainage duration observations synthesized in this study, we first constructed empirical models between Rps and drainage duration for drained peatlands categorized by climate zone (boreal, temperate and tropical climate) and land use (i.e., agriculture, forestry and grassland) (Fig. 5a, b). The values of Rps for certain groups classified by climate zone and land use could be estimated by using the corresponding empirical models established in this study and reported drainage durations that were extracted from the literature. The empirical models categorized by climate zone and land use are listed below (Fig. 5a, b):$${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{Agr}}}}=13.95,{{{{rm{Dur}}}}}^{-0.58},,n=48,,{R}_{{{{rm{adj}}}}.}^{2}=0.85,,p; < ; 0.0001$$ (6) $${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{For}}}}=5.36,{{{{rm{Dur}}}}}^{-0.83},,n=21,,{R}_{{{{rm{adj}}}}.}^{2}=0.92,,p; < ; 0.0001$$ (7) $${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{Gra}}}}=5.55,{{{{rm{Dur}}}}}^{-0.36},,n=40,,{R}_{{{{rm{adj}}}}.}^{2}=0.61,,p; < ; 0.0001$$ (8) $${R}_{{{{rm{ps}}}}}{mbox{-}}{{{rm{Tro}}}}{mbox{-}}{{{rm{Agr}}}}{mbox{-}}{{{rm{For}}}}{mbox{-}}{{{rm{Gra}}}}=6.63,{{{{rm{Dur}}}}}^{-0.37},,n=121,,{R}_{{{{rm{adj}}}}.}^{2}=0.55,,p; < ; 0.0001$$ (9) where Rps indicates the peat subsidence rate (cm yr–1), Dur is the drainage duration, and the numbers indicate coefficients for the established empirical models. Bor, Tem, and Tro indicate boreal, temperate, and tropical climate zones, respectively. Agr, For, and Gra represent agriculture, forestry, and grassland land uses, respectively. We note that it was not possible to further distinguish these models between boreal and temperate climate zones and among agriculture, forestry, or grassland land use in tropical climates, as there is currently a lack of sufficient measurements, which warrants more research.However, the Rps is triggered by a combination of processes such as physical compaction by heavy equipment or livestock trampling and shrinkage through contraction of organic fibers when drying, consolidation by loss of water from pores in the peat and oxidation owing to the breakdown of peat organic matter10,11,12. Therefore, to reliably estimate the soil HR rate from Rps due to oxidation, the proportion of Rps attributed to oxidation (Po, in %) should be considered12. Using the 49 paired Po and drainage duration observations synthesized in this study, we then constructed empirical models between Po and drainage duration for drained peatlands that were also categorized by climate zone (boreal, temperate, and tropical climate) and land use (agriculture, forestry, and grassland) (Fig. 5c, d). Similarly, the Po values of certain groups classified by climate zone and land use could be estimated by using the corresponding empirical models established in this study and reported drainage durations that were extracted from the literature. The empirical models categorized by climate zone and land use are shown below (Fig. 5c, d):$$ {P}_{{{{rm{o}}}}}{mbox{-}}{{{rm{Tem}}}}{mbox{-}}{{{rm{Bor}}}}{mbox{-}}{{{rm{Agr}}}}{mbox{-}}{{{rm{For}}}}{mbox{-}}{{{rm{Gra}}}}=12.05,{{{mathrm{Ln}}}}({{{rm{Dur}}}})+2.15,,n=30,\ {R}_{{{{rm{adj}}}}.}^{2}=0.89,,p; < ;0.0001$$ (10) $$ {P}_{{{{rm{o}}}}}{mbox{-}}{{{rm{Tro}}}}{mbox{-}}{{{rm{Agr}}}}{mbox{-}}{{{rm{For}}}}{mbox{-}}{{{rm{Gra}}}}=14.36,{{{mathrm{Ln}}}}({{{rm{Dur}}}})+37.05,,n=19,\ {R}_{{{{rm{adj}}}}.}^{2}=0.81,,p; < ;0.0001$$ (11) where Po indicates the proportion of Rps attributable to oxidation, Dur is the drainage duration, and the numbers indicate coefficients for the established empirical models. The abbreviations Bor, Tem, Tro, Agr, For, and Gra have been described previously. We note that the different land uses shared the same models across temperate and boreal climates and tropical climate due to a lack of sufficient global observations. This will also induce some uncertainties in our analysis.Furthermore, the soil HR (FHR, Mt C yr−1) due to peat oxidation induced by drainage was estimated using the following equation according to ref. 11:$${F}_{{{{rm{HR}}}}}=sum {R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i}times {A}_{i,j}$$ (12) where SOC (g kg–1) and BD (g cm–3) indicate the soil (0–30 cm) organic C concentration and bulk density of pristine peatlands, respectively; A (×103 km2) indicates the drained peatland area; i indicates the climate zone (boreal, temperate or tropical); j indicates the land use (agriculture, forestry or grassland); and Rps (cm yr–1) and Po (%) are described in Eqs. (6–11). Datasets of the SOC concentration and BD and Rps due to oxidation were systematically reviewed and bootstrapped and categorized by climate zones and land uses (see Supplementary Fig. 10 and Supplementary Data B). Regarding the large uncertainties for areas of drained peatlands, we combined two previously published datasets (72, 61, 22, 37, 43, 26, 94, 109, and 39 × 103 km2 by ref. 18, and 37, 55, 4, 109, 63, 58, 96, 72, and 1 × 103 km2 by ref. 20. for agriculture-, forestry- and grassland-drained peatlands in boreal, temperate and tropical climate zones, respectively) and obtained their mean values with 95% CIs (for details, see bootstrapping procedure in Data analysis). Uncertainties (i.e., 95% CI) in total HR (δFHR) were propagated according to the Gaussian random error propagation principle as follows:$${{{rm{delta }}}}{F}_{{{{rm{HR}}}}}=sqrt{sum sqrt{begin{array}{c}{(delta {R}_{{{{rm{ps}}}},i,j})}^{2}times {({P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {P}_{{{{rm{o}}}},i,j})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {{{{rm{SOC}}}}}_{i})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{BD}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {{{{rm{BD}}}}}_{i})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {A}_{i,j})}^{2}+\ {(delta {A}_{i,j})}^{2}times {({R}_{{{{rm{ps}}}},i,j}times {P}_{{{{rm{o}}}},i,j}times {{{{rm{SOC}}}}}_{i}times {{{{rm{BD}}}}}_{i})}^{2}end{array}}}$$ (13) where δFHR, δRps, δPo, δSOC, δBD, and δA indicate the 95% CIs of total soil HR, Rps, Po, SOC, and BD and drained peatland area, respectively, and i and j indicate the climate zone (boreal, temperate, tropical) and land use (agriculture, forestry, or grassland), respectively.To further estimate the total SR (FSR, Mt C yr−1) and its uncertainty (δFSR) from global drained peatlands, the following equations were used:$${F}_{{{{rm{SR}}}}}=sum frac{{F}_{{{{rm{HR}}}},i,j}}{{C}_{{{{rm{HR}}}},i,j}}$$ (14) $$delta {F}_{{{{rm{SR}}}}}=sqrt{sum sqrt{{(frac{1}{{C}_{{{{rm{HR}}}},i,j}})}^{2}times delta {F}_{{{{rm{HR}}}},i,j}^{2}+{(-frac{{F}_{{{{rm{HR}}}},i,j}}{{C}_{{{{rm{HR}}}},i,j}^{2}})}^{2}times delta {C}_{{{{rm{HR}}}},i,j}^{2}}}$$ (15) where CHR (%) indicates the mean relative contribution of HR to SR from simultaneously measured SR, HR, and AR from our meta-analysis (see Supplementary Fig. 11 and Supplementary Data A) and i and j indicate the climate zone (boreal, temperate, tropical) and land use (agriculture, forestry, or grassland), respectively. FHR and δFHR are given in Eqs. (12, 13). We note that the CHR could be classified only by climate zone, as there is a lack of sufficient measurements of land use; that is, the different land uses under the same climate shared the same CHR value, which may induce uncertainties in estimating the total SR from global drained peatlands.Regarding the abovementioned lack of sufficient measurements for distinguishing between boreal and temperate drained peatlands, we also used another method to estimate the annual total HR and SR from global drained peatlands. Specifically, we obtained the mean values of Rps by oxidation across boreal and temperate drained peatlands for each land use (i.e., climate zones were classified as boreal+temperate or tropical) (Supplementary Fig. 14 and Supplementary Table 1). The estimation process was the same as that previously described. The different estimation methods were likely to provide results with greater convergence.Data analysisSignificant differences in observed variables were tested by performing nonparametric analysis. Specifically, tests with two independent samples (i.e., Mann–Whitney U test) were used for only two variables (i.e., to compare the contribution of HR to SR between pristine and drained peatlands), and tests with two or more independent samples (i.e., Kruskal–Wallis test and pairwise comparisons) were used if there were three or more variables (i.e., SOC, BD and Rps due to oxidation in the boreal, temperate and tropical climate zones or different land uses). Linear or nonlinear regression analysis was performed to examine the relationships between the responses of SR and its components with environmental variables or the peat subsidence rate with drainage duration.To reliably estimate the uncertainties in Rps by oxidation, SOC, BD, drained peatland area, and relative contribution of HR to SR, bootstrap resampling with 10000 iterations was conducted using the boot package, and 95% CIs were calculated using the “basic” type. The ggplot 2 package in R software (R core team, 2019) was used for statistical analysis. Data were expressed as the means with their 95% CIs, and significance of the regression analyses was indicated at the level of p  More

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 
Gardner, R.C., & Finlayson, C., (eds) Global Wetland Outlook: State of the World’s Wetlands and Their Services to People. (Ramsar Convention Secretariat, 2018).Tao, S. et al. Rapid loss of lakes on the Mongolian Plateau. Proc. Natl. Acad. Sci. 112(7), 2281–2286 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Davidson, N. Wetland losses and the status of wetland-dependent species. in The wetland book II: distribution, description, and conservation 369–381 (Springer, 2018).Xu, Y. et al. Loss of functional connectivity in migration networks induces population decline in migratory birds. Ecol. Appl. 29(7), e10960 (2019).
Google Scholar 
Wang, X. et al. Effects of anthropogenic landscapes on population maintenance of waterbirds. Conserv. Biol. https://doi.org/10.1111/cobi.13808 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Ma, Z., Cai, Y., Li, B. & Chen, J. Managing wetland habitats for waterbirds: An international perspective. Wetlands 30(1), 15–27 (2010).CAS 

Google Scholar 
Rönkä, M. T., Saari, C. L. V., Lehikoinen, E. A., Suomela, J., Häkkilä, K. (eds) Environmental changes and population trends of breeding waterfowl in northern Baltic Sea. in Annales Zoologici Fennici JSTOR (2005).Pöysä, H. & Paasivaara, A. Shifts in fine-scale distribution and breeding success of boreal waterbirds along gradients in ice-out timing and habitat structure. Freshw. Biol. 66(11), 2038–2050 (2021).
Google Scholar 
Borgo, J. S. & Conover, M. R. Visual and olfactory concealment of duck nests: Influence on nest site selection and success. Hum. Wildl. Interact. 10(1), 110–121 (2016).
Google Scholar 
Batbayar, N., Takekawa, J. Y., Natsagdorj, T., Spragens, K. A. & Xiao, X. Site selection and nest survival of the bar-headed goose (Anser indicus) on the Mongolian Plateau. Waterbirds Int. J. Waterbird Biol. 37(4), 381–393 (2014).
Google Scholar 
Syroechkovsky, E. V., Litvin K. E., Gurtovaya E. N. Nesting ecology of Bewick’s Swans on Vaygach Island, Russia. Waterbirds 221–6 (2002).Meijer, T. & Drent, R. Re-examination of the capital and income dichotomy in breeding birds. Ibis 141(3), 399–414 (1999).
Google Scholar 
Rothenbach, C. A. & Kelly, J. P. The parental dilemma under variable predation pressure: Adaptive variation in nest attendance by great egrets. Condor 114(1), 90–99 (2012).
Google Scholar 
Zhang, L., An, B., Shu, M. & Yang, X. Nest-site selection, reproductive ecology and shifts within core-use areas of black-necked cranes at the northern limit of the Tibetan Plateau. PeerJ 5(1), e2939 (2017).PubMed 
PubMed Central 

Google Scholar 
Newton, I. The Migration Ecology of Birds (Elsevier, 2010).
Google Scholar 
Donnelly, J. P. et al. Migration efficiency sustains connectivity across agroecological networks supporting sandhill crane migration. Ecosphere 12(6), e03543 (2021).
Google Scholar 
Barzen, J. A. & Serie, J. R. Nutrient reserve dynamics of breeding canvasbacks. Auk 107(1), 75–85 (1990).
Google Scholar 
Burnham, J. W. et al. Novel foraging by wintering Siberian cranes leucogeranus leucogeranus at China’s Poyang lake indicates broader changes in the ecosystem and raises new challenges for a critically endangered species. Bird Conserv. Int. 27, 204–223 (2017).
Google Scholar 
Krapu, G. L., Iverson, G. C., Reinecke, K. J. & Boise, C. M. Fat deposition and usage by arctic-nesting sandhill cranes during spring. Auk 102(2), 362–368 (1985).
Google Scholar 
Tøttrup, A. P. et al. Avian migrants adjust migration in response to environmental conditions en route. Biol. Lett. 4(6), 685–688 (2008).PubMed 
PubMed Central 

Google Scholar 
Sorte, F. A. L. et al. The role of atmospheric conditions in the seasonal dynamics of North American migration flyways. J. Biogeogr. 41(9), 1685–1696 (2014).
Google Scholar 
Marra, P. P., Francis, C. M., Mulvihill, R. S. & Moore, F. R. The influence of climate on the timing and rate of spring bird migration. Oecologia 142(2), 307–315 (2005).ADS 
PubMed 

Google Scholar 
Studds, C. E. & Marra, P. P. Rainfall-induced changes in food availability modify the spring departure programme of a migratory bird. Proc. R. Soc. B Biol. Sci. 278(1723), 3437–3443 (2011).
Google Scholar 
Saino, N. et al. Ecological conditions during winter predict arrival date at the breeding quarters in a trans-Saharan migratory bird. Ecol. Lett. 7(1), 21–25 (2004).
Google Scholar 
Barzen, JA. Chapter 15 – Ecological Implications of Habitat Use by Reintroduced and Remnant Whooping Crane Populations. In: Nyhus PJ, French JB, Converse SJ, Austin JE, Delap Jh (eds.), Whooping Cranes: Biology and Conservation. (Academic Press, New York, 2019).Norris, D. R., Marra, P. P., Kyser, T. K., Sherry, T. W. & Ratcliffe, L. M. Tropical winter habitat limits reproductive success on the temperate breeding grounds in a migratory bird. Proc. R. Soc. B Biol. Sci. 271(1534), 59–64 (2004).
Google Scholar 
Norris, D. R. & Taylor, C. M. Predicting the consequences of carry-over effects for migratory populations. Biol. Let. 2(1), 148–151 (2006).
Google Scholar 
Newton, I. Population Limitation in Birds (Academic press, 1998).
Google Scholar 
Altwegg, R. & Anderson, M. D. Rainfall in arid zones: Possible effects of climate change on the population ecology of blue cranes. Funct. Ecol. 23(5), 1014–1021 (2009).
Google Scholar 
Layton-Matthews, K., Hansen, B. B., Grøtan, V., Fuglei, E. & Loonen, M. J. J. E. Contrasting consequences of climate change for migratory geese: Predation, density dependence and carryover effects offset benefits of high-arctic warming. Glob. Chang. Biol. 26(2), 642–657 (2020).ADS 
PubMed 

Google Scholar 
Madsen, J. & Fox, A. D. Impacts of hunting disturbance on waterbirds-a review. Wildl. Biol. 1(4), 193–207 (1995).
Google Scholar 
Minias, P. Reproduction and survival in the city: Which fitness components drive urban colonization in a reed-nesting waterbird?. Curr. Zool. 62(2), 79–87 (2016).PubMed 
PubMed Central 

Google Scholar 
Cheng, Y., Fiedler, W., Wikelski, M. & Flack, A. “Closer-to-home” strategy benefits juvenile survival in a long-distance migratory bird. Ecol. Evol. 9, 8945–8952 (2019).PubMed 
PubMed Central 

Google Scholar 
Sargeant, A. B. & Raveling, D. G. Mortality during the breeding season. In Ecology and Management of Breeding Waterfowl (eds Batt, B. D. J. et al.) 396–422 (University of Minnesota Press, 1992).
Google Scholar 
IUCN. The IUCN Red List of Threatened Species. http://www.iucnredlist.org Downloaded on 22 November 2021. Version 2021–2 (2021).Mudrik, E. A. et al. Gene pool homogeneity of western and eastern populations of the white-naped crane antigone vipio in different flyways. Russ. J. Genet. 58(5), 566–575 (2022).CAS 

Google Scholar 
Wang, W., Fraser, J. D. & Chen, J. Distribution and long-term population trends of wintering waterbirds in Poyang lake China. Wetlands 39(S1), 125–135 (2019).
Google Scholar 
Wang, W., Fraser, J. D. & Chen, J. Wintering waterbirds in the middle and lower Yangtze river floodplain: Changes in abundance and distribution. Bird Conserv. Int. 27(2), 167–186 (2017).CAS 

Google Scholar 
Gilbert, M., Buuveibaatar, B., Fine, A. E., Jambal, L. & Strindberg, S. Declining breeding populations of white-naped cranes in eastern Mongolia, a ten-year update. Bird Conserv. Int. 26(04), 490–504 (2016).
Google Scholar 
Mirande, C.M., Harris J.T. Crane Conservation Strategy. in International Crane Foundation (Baraboo, Wisconsin, USA, 2019).Bouchard, F. et al. Paleolimnology of thermokarst lakes: A window into permafrost landscape evolution. Arct. Sci. 3, 91–117 (2016).
Google Scholar 
Fernández-Tizón, M., Emmenegger, T., Perner, J. & Hahn, S. Arthropod biomass increase in spring correlates with NDVI in grassland habitat. Sci. Nat. 107(5), 42 (2020).
Google Scholar 
Dong, J. et al. Mapping paddy rice planting area in northeastern Asia with landsat 8 images, phenology-based algorithm and google earth engine. Remote Sens. Environ. 185, 142–154 (2016).ADS 
PubMed 
PubMed Central 

Google Scholar 
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540(7633), 418–422 (2016).ADS 
CAS 
PubMed 

Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146(730), 1999–2049 (2020).ADS 

Google Scholar 
Bivand, R., Rundel, C. Rgeos: Interface to geometry engine-open source (GEOS). R package version 03–26. (2017).Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1(1), 3–14 (2010).
Google Scholar 
Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36(1), 27–46 (2013).
Google Scholar 
Barton, K., Barton, M. K. Package ‘MuMIn’. R package version. 1(6), (2019).Anderson, D. & Burnham, K. Model selection and multi-model inference. Second NY Springer-Verlag 2004(63), 10 (2020).
Google Scholar 
Signer, J., Fieberg, J. & Avgar, T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9(2), 880–890 (2019).PubMed 
PubMed Central 

Google Scholar 
Batbayar, N. et al. Combining tracking and remote sensing to identify critical year-round site, habitat use and migratory connectivity of a threatened waterbird species. Remote Sens. 13(20), 4049 (2021).ADS 

Google Scholar 
Lang, X. et al. Luan river upper reaches: The important stopover site of the white-naped crane (Grus vipio) western population. Biodivers. Sci. 28(10), 1213 (2020).
Google Scholar 
Jia, Y. et al. Shifting of the migration route of white-naped crane (Antigone vipio) due to wetland loss in China. Remote Sens. 13(15), 2984 (2021).ADS 
CAS 

Google Scholar 
Lloyd, C. T. et al. Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets. Big Earth Data 3(2), 108–139 (2019).PubMed 
PubMed Central 

Google Scholar 
Kéry, M. & Schaub, M. Bayesian Population Analysis Using WinBUGS: A Hierarchical Perspective (Academic Press, 2011).
Google Scholar 
Lê, S., Josse, J. & Husson, F. FactoMineR: An R package for multivariate analysis. J. Stat. Softw. 25(1), 1–18 (2008).
Google Scholar 
Hastie, T. J. & Tibshirani, R. J. Generalized Additive Model (Routledge, 2017).MATH 

Google Scholar 
Fair, J. M. & Jones, J. Guidelines to the Use of Wild Birds in Research (Ornithological council, 2010).
Google Scholar 
RStudio Team RStudio. Integrated Development Environment for R (Studio, PBC, 2022).
Google Scholar 
Higuchi, H. et al. Using a remote technology in conservation: Satellite tracking white-naped cranes in Russia and Asia. Conserv. Biol. 18(1), 136–147 (2004).
Google Scholar 
Weller, M. W. Wetland Birds: Habitat Resources and Conservation Implications (University Press, Cambridge, 1999).
Google Scholar 
Vandandorj, S., Gantsetseg, B. & Boldgiv, B. Spatial and temporal variability in vegetation cover of Mongolia and its implications. J. Arid Land 7(4), 450–461 (2015).
Google Scholar 
Bradter, U. T. E., Gombobaatar, S., Uuganbayar, C., Grazia, T. E. & Exo, K.-M. Reproductive performance and nest-site selection of white-naped cranes grus vipio in the Ulz river valley, north-eastern Mongolia. Bird Conserv. Int. 15(4), 313–326 (2005).
Google Scholar 
Bradter, U., Gombobaatar, S., Uuganbayar, C., Grazia, T. E. & Exo, K. M. Time budgets and habitat use of white-naped cranes grus vipio in the Ulz river valley, northeastern Mongolia during the breeding season. Bird Conserv. Int. 17(3), 259–271 (2007).
Google Scholar 
Zheng, Y. et al. Dynamic changes and driving factors of wetlands in inner Mongolia plateau China. PLoS ONE 14(8), e0221177 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Linscott, J. A. & Senner, N. R. Beyond refueling: Investigating the diversity of functions of migratory stopover events. Ornithol. Appl. 123, duaa074 (2021).
Google Scholar 
Xu, Y., Kieboom, M., van Lammeren, R. J. A., Si, Y. & de Boer, W. F. Indicators of site loss from a migration network: Anthropogenic factors influence waterfowl movement patterns at stopover sites. Global Ecol. Conserv. 25, e01435 (2021).
Google Scholar 
Piersma, T. et al. Simultaneous declines in summer survival of three shorebird species signals a flyway at risk. J. Appl. Ecol. 53(2), 479–490 (2016).
Google Scholar 
Wilson, S., Gil-Weir, K. C., Clark, R. G., Robertson, G. J. & Bidwell, M. T. Integrated population modeling to assess demographic variation and contributions to population growth for endangered whooping cranes. Biol. Conserv. 197, 1–7 (2016).
Google Scholar 
Wheeler, M. E., Barzen, J. A., Crimmins, S. M. & Deelen, T. R. V. Effects of territorial status and life history on sandhill crane (Antigone canadensis) population dynamics in south-central Wisconsin, USA. Can. J. Zool. 97(2), 112–120 (2019).
Google Scholar 
Gerber, B. D. & Kendall, W. L. Considering transient population dynamics in the conservation of slow life-history species: An application to the sandhill crane. Biol. Conserv. 200, 228–239 (2016).
Google Scholar 
Servanty, S., Converse, S. J. & Bailey, L. L. Demography of a reintroduced population: Moving toward management models for an endangered species, the whooping crane. Ecol. Appl. 24(5), 927–937 (2014).PubMed 

Google Scholar 
Xu, W. et al. Hidden loss of wetlands in China. Curr. Biol. 29(18), 3065–71.e2 (2019).CAS 
PubMed 

Google Scholar 
Niu, Z. et al. Mapping wetland changes in China between 1978 and 2008. Chin. Sci. Bull. 57(22), 2813–2823 (2012).
Google Scholar 
Austin, J., Morrison, K. & Harris, J. Cranes and Agriculture: A Global Guide for Sharing the Landscape (International Crane Foundation, Baraboo, 2018).
Google Scholar 
Lee, S. D., Jabłoński, P. G. & Higuchi, H. Winter foraging of threatened cranes in the demilitarized zone of Korea: Behavioral evidence for the conservation importance of unplowed rice fields. Biol. Conserv. 138(1), 286–289 (2007).
Google Scholar 
Nilsson, L., Bunnefeld, N., Persson, J., Žydelis, R. & Månsson, J. Conservation success or increased crop damage risk? The Natura 2000 network for a thriving migratory and protected bird. Biol. Conserv. 236, 1–7 (2019).
Google Scholar 
Mukherjee, A., Borad, C. K. & Parasharya, B. M. Breeding performance of the Indian sarus crane in the agricultural landscape of western India. Biol. Conserv. 105(2), 263–269 (2002).
Google Scholar 
Gopi Sundar, K. S. Are rice paddies suboptimal breeding habitat for sarus cranes in Uttar Pradesh, India?. Condor 111(4), 611–623 (2009).
Google Scholar 
Pekarsky, S., Schiffner, I., Markin, Y. & Nathan, R. Using movement ecology to evaluate the effectiveness of multiple human-wildlife conflict management practices. Biol. Conserv. 262, 109306 (2021).
Google Scholar 
Hemminger, K., König, H., Månsson, J., Bellingrath-Kimura, S.-D. & Nilsson, L. Winners and losers of land use change: A systematic review of interactions between the world’s crane species (Gruidae) and the agricultural sector. Ecol. Evol. 12(3), e8719 (2022).PubMed 
PubMed Central 

Google Scholar 
Amano, T. Conserving bird species in Japanese farmland: Past achievements and future challenges. Biol. Conserv. 142(9), 1913–1921 (2009).
Google Scholar 
Okuya, K. et al. Isolation and characterization of influenza A viruses from environmental water at an overwintering site of migratory birds in Japan. Adv. Virol. 160(12), 3037–3052 (2015).CAS 

Google Scholar 
Wille, M. & Barr, I. G. Resurgence of avian influenza virus. Science 376(6592), 459–460 (2022).ADS 
CAS 
PubMed 

Google Scholar  More

The Santa Barbara Coastal Long Term Ecological Research program has monitored benthic communities in five kelp forests seasonally since 2008 using fixed transect diver surveys, and moored sensors at each reef have recorded bottom temperatures every 15 min. Blob-associated positive bottom temperature anomalies began in winter 2014 and persisted through autumn 2016 (Fig. 1a)18. Peak temperature anomalies occurred during the summer and autumn of 2014 and 2015 (Fig. 1a), and the average temperature anomaly in autumn 2015 was +3.1 °C, equivalent to an average daily temperature of 19.6 °C. In 2014 and 2015, 91 and 69% of autumn days, respectively, were classified as heatwave days as defined by Hobday et al.20. Seasonal chlorophyll-a concentration, a proxy for phytoplankton abundance, was obtained from satellite imagery at each of the five reefs over the 14-year period. The average chlorophyll-a concentration was anomalously low throughout the warming period, and exceptionally low during the springs of 2014 and 2015 (Fig. 1a), when upwelling-driven nutrient enrichment typically supports dense phytoplankton blooms.Fig. 1: Average seasonal bottom temperature anomaly, chlorophyll-a concentration anomaly, and percent cover and species richness of sessile invertebrates across five sites.The Blob, an anomalous warming period from spring of 2014 to winter of 2016, is highlighted in gray, coincident with (a) positive temperature anomalies (°C; solid line), negative chlorophyll-a anomalies (mg/m3; dashed line), and declines in (b) invertebrate cover (solid line) and species richness (number of unique species/taxa/80 contact points; dashed line). Seasons are denoted by Sp (Spring), Su (Summer), A (Autumn) and W (Winter).Full size imageMean sessile invertebrate cover averaged across all sites declined 71% during the Blob, reaching a 14-year minimum of 7% in autumn of 2015 (Fig. 1b and Supplementary Fig. 1). Species richness declined 69% during the same period (Fig. 1b and Supplementary Fig. 1). The responses of invertebrates to warming were not consistent across time even though the duration and intensity of warming was similar in 2014 and 2015, suggesting that extended periods of elevated seawater temperature were not solely responsible for the most severe loss of invertebrates. For ectotherms, increases in ambient seawater temperature should be met with increases in metabolic rate and food requirements to sustain metabolism21. Because of their sedentary lifestyle, sessile invertebrates cannot actively forage for food or seek spatial refuge from thermal extremes, and limitations in their planktonic food supply can result in metabolic stress over extended periods22,23. Anomalously low chlorophyll-a concentrations during the Blob (Fig. 1a), particularly in the spring of 2015, indicated that food limitation was a likely driver of invertebrate decline. Results from piecewise structural equation modeling (Fig. 2) that incorporated biological interactions with space competitors (understory macroalgae), predators (sea urchins), and foundation species (giant kelp) showed that the severity of warming had both a direct and indirect effect on the sessile invertebrate community. The proportion of heatwave days was a direct negative predictor of sessile invertebrate cover (−0.11) and species richness (−0.21). The proportion of heatwave days was an even stronger negative predictor of chlorophyll-a concentration (−0.26), yielding negative indirect effects on invertebrate cover (−0.07) and species richness (−0.05) due to the positive influence of chlorophyll-a concentration on sessile invertebrate cover (+0.26) and richness (+0.20).Fig. 2: Piecewise structural equation modeling (SEM) for sessile invertebrate cover and species richness.Arrows indicate directionality of effects on (a) invertebrate cover and (b) species richness. Red arrows show negative relationships; black arrows show positive relationships. R2 values are conditional R2. Arrow widths are proportional to effect sizes as measured by standardized regression coefficients (shown next to arrows). ***p  More

Conception of the workflow to demonstrate the microbial associations from co-occurrence networks with microbial cultivationMicrobial co-occurrence networks are composed of nodes and edges, which usually represent microbes and statistically significant associations between microbes, respectively. We hypothesized that the microbial associations could be validated if the topological properties of networks are simplified, and if the microbes representing the nodes can be cultivated. To test this hypothesis, we designed a workflow as shown in Fig. 1. A total of 12,096 wells from 126 96-well plates were inoculated with droplets of series diluted environmental samples, wells from each 96-well plate represented the same combination of given culture condition, sample type (plants, roots, and sediments) and dilution rate (from 10–1 to 10–7). After being cultivated at 30 °C for 10 days, 69 effective (Supplementary Table S3) plates with  > 30% wells showing microbial growth were retained for downstream microbial community analysis. Microbial DNA in each well was extracted, bar-coded, and sequenced for the inference of co-occurrence networks. The wells of plates showing high abundances of target Zotus were targeted for microbial isolations. Lastly, the cultivated microbial isolates were matched to Zotus in the network and used for demonstration of microbial interactions.Figure 1Overview of experimental demonstration of microbial interactions in co-occurrence networks. For detailed description, please refer to the method section.Full size imagePrevalent Zotu pairs in the co-occurrence networksDepending on the microbial density in samples, the 96-well plates harbored different numbers of wells with microbial growth. We obtained 65 96-well plates (6,091 wells) that were effective with microbial growth and data analysis for co-occurrence network reconstruction. After quality control and denoise, we obtained 130 Gbp sequence data. A total of 14,377 Zotus were annotated (Supplementary Table S4). There were 217 ± 94 (average ± standard deviation) prevalent Zotus, i.e., these Zotus appeared at frequencies ≥ 30% of wells in a given 96-well plate.Next, we analyzed Zotus compositions and abundances in each well of the 65 plates. Accordingly, we reconstructed 65 independent microbial co-occurrence networks and further retrieved the robust (Spearman’s |ρ| > 0.6 and P  More

“This new study is inspired by some our earlier theoretical work applied to killer whales that suggests that age-related changes in relatedness are important for the evolution of menopause,” says Samuel Ellis, the first author of the study. “Reproduction can be thought of as a form of generalized harm as the birth of an offspring increases within-group competition for resources. Kinship dynamics — the ways in which local relatedness changes over an individual’s lifetime — are one way that menopause could be favored, because older females are more inclined to cease reproduction to not harm their group mates than younger females. Here we wanted to generalize this concept to both sexes, and to other species without menopause.” More

After massive parallel sequencing of the nucleic acids obtained from fur seal scats, a wide variety of invertebrate and vertebrate viral hosts assignations with low nucleotidic and amino-acidic identities were obtained, most of them corresponding to animal species not described before in Antarctica. These results make us reconsider the use of closed RefSeq databases for viral discovery, especially because the studied area was a remote geographical area where a high number of new viral species is expected to occur22.After repeating the analysis of the contigs obtained using BLASTn, a high number of miss-assignments was observed, corresponding almost entirely to contigs newly assigned as unclassified Eukaryotic Circular Rep-Encoding Single-Stranded DNA (CRESS-DNA) viral sequences. CRESS viruses have been detected ubiquitously in many different animals without any recognised role in the development of any disease23,24,25,26.These results are in accordance with the recent reporting of CRESS sequences also being ubiquitous in a wide variety of environments and at high proportions, including Antarctica, where they have been described to represent more than 50% of sequences obtained from glacier waters27.Viral-host distributionVirome studies in other Arctocephalus species from subantarctic and South American regions revealed a 5% of viral sequences with predominance of bacteriophages followed by viruses from the Parvoviridae family28. The methodology here applied provided an increase of 12–25% viral reads when probe-based Target Enrichment Sequencing (TES) was applied, that in comparison with Untargeted Viral Metagenomics (UVM) approaches conducted in these type of samples28 could be considered an optimal result.Most of the viral species detected in feces corresponded to unknown viruses, 83.59% from the total of sequences, followed by viruses that infect invertebrates, 8.75%, bacteriophages, 4.46%, and vertebrate viruses, 3.11% (Fig. 1).Figure 1Host distribution of viral assignations sequenced from fecal (A) and serum (B) samples collected from male A. gazella.Full size imageAs expected, when applying both targeted and untargeted sequencing methodologies, TES approach resulted in a recovery of many vertebrate viral assignations (Table 1) whereas untargeted sequencing enabled a better detection of viruses known to infect invertebrates (Table 2). To describe the complete A. gazella fecal virome, sequences obtained by both sequencing methodologies were considered all together, representing a total of 2.62 million reads.Table 1 Vertebrate viral assignations obtained from fecal samples sequencing from male A. gazella. Ranges of Genome coverage, nucleotide identity and aminoacidic identity are expressed in percentages.Full size tableTable 2 Invertebrate viral assignations obtained from fecal samples sequencing from male A. gazella. Colours represent the presence of each assignation in the processed pools. Ranges of Coverage, NT ID and AA ID are represented in percentages.Full size table
A. gazella virusesFur seal picorna-like virusFur seal picorna-like virus was firstly described in a fecal sample obtained from A. gazella in King George Island in the South Shetland Islands, Antarctica by Krumbholz and co-workers16.In this study, we report a total of 19 contigs resulting after assembling 2671 reads obtained from 4/4 fecal pools analysed being the most prevalent virus described in this study. One of the contigs covered 96.91% of the fur seal picorna-like virus genome and presented a nucleotide homology of 99.38% with the reference strain described in 2017. The other contigs coverage ranged from 19.75 to 21.22% with a 45.92 to 90.5% nucleotide identity with reference strain NC_035110. Four contigs matching the ORF2 polyprotein are represented in Fig. 2 where differences among them and with the reference strain are showed.Figure 2Nucleotide alignment of ORF2 sequences from the A. gazella picorna-like contigs compared to the ORF2 from RefSeq NC_0351110. In consensus strain, position 1 represents position 6523 from RefSeqs genome.Full size imagePicornaviruses are known to cause a wide variety of diseases in vertebrate hosts, especially mammals29, but the role of Fur seal picorna-like virus in pathogenesis development is still unknown30. Many picornaviruses are transmitted horizontally via fecal–oral or airborne routes29. The fact that these sequences were detected in all the fecal pools obtained from animals with no evidence of disease may that suggest the virus may have a stable endemic relationship within that seal population.Torque teno pinniped virusLambdatorquevirus is a genus within the Anelloviridae family. The genus comprises 8 species named Torque teno pinniped virus 2 to 9 isolated from different pinniped species: A. gazella (Torque teno pinniped virus 6 and 7)17, Phoca vitulina (Torque teno pinniped virus 2, 3, 4)31, Zalophus californianus (Torque teno pinniped virus 5)32 and Leptonychotes weddellii (Torque teno pinniped 8 and 9)33.One contig with a nuleotide similarity of 95.12% against Torque teno pinniped virus 7 was obtained from one of the fecal pools. This virus had been described in these animals inhabiting Livingston Island in 2016, using rolling circle amplification and subsequent Sanger sequencing from buccal swabs17. However, sequences obtained in this study belong to partial ORF2 which is not the optimal genome region for typing purposes or phylogenetic analysis.These members of the Anelloviridae represent the more abundant viruses found in human, animals and environmental samples although their etiological role in any disease has not been clearly identified being considered a persistent virus ubiquitous to several different tissues34,35No Torque teno virus sequences were detected in serum samples which agree with what was observed for Zalophus californianus anellovirus prevalently detected in different tissues, like lung and liver, but not in blood samples. Interestingly, other known anelloviruses are typically found in blood or plasma samples32.MamastrovirusTwo of the fecal pools analyzed presented Mamastrovirus sequences. The presence of these viruses in humans and other mammals is widely known, as well as their involvement in gastroenteritis development36. The four contigs obtained (comprising 1008 sequences) showed homologies against reference genomes, ranging from 45.70% to 59.37% when compared at nucleotide level and 36.69% to 46.69% when compared at aminoacidic level. Phylogenetic analysis of partial OFR2 regions of these contigs indicate its closer similarity with sequences from California Sea Lion astroviruses, a virus that was determined as to be the most prevalent in fecal samples from these animals (Z. californianus)37. This finding suggests that these sequences may belong to a yet unknown virus like Z. californianus astrovirus and may indicate that such virus is prevalent in the sampled area (detected in 2/4 fecal pools studied) and the second more abundant virus (1008 reads) in the studied fecal samples (Fig. 3).Figure 3Phylogenetic consensus tree based on partial ORF2 sequences from the Mamastrovirus contigs sequenced from A. gazella scats (in bold). Bootstrap resampling with 1000 replicates.Full size imageAdeno associated virus 2Two of the studied fecal pools presented 138 sequences, forming 3 contigs with nucleotide identities ranging from 46.91 to 48.04% (Table 1), that matched adeno associated viruses previously described in Z. californianus, humans and other mammals with and unknow etiologic role (Fig. 4). The detected sequences probably correspond to fur seal adeno associated viruses never described before. The detection of these viruses is quite common in other mammals suggesting they could cause persistent infections in their hosts, but no etiological role has been attributed to them38.Figure 4Phylogenetic consensus tree of the Adeno-associated virus contigs sequenced from A. gazella scats (in bold). Bootstrap resampling with 1000 replicates.Full size imageNorovirusA norovirus contig was obtained in one of the four pools analyzed. Noroviruses are the most relevant non-bacterial gastroenteritis etiological agents in humans39, with its presence widely described in other mammals40. The contig detected in the fecal samples, represented the 4.43% of the viral genome, was in the VP1 region and comprised 56 reads with an identity  > 99% to California sea lion norovirus described by Teng and collaborators in 201841 (Fig. 5). Results obtained suggest these sequences belong to a putative new norovirus specie.Figure 5Phylogenetic consensus tree of the Norovirus contig sequenced from A. gazella scats (in bold). Bootstrap resampling with 1000 replicates.Full size imageViruses in serum samplesAll the viral sequences obtained from serum samples (970 reads) matched to CRESS-DNA viral sequences from unknown hosts.The fact that no other viruses were identified in serum samples suggests the animals tested were not under active viremia at the time of sample collection or it was not detectable by the applied methodology.Diet related virusesSeveral virus sequences similar to viruses known to have invertebrate animals as hosts were detected in fecal pools, mainly by UVM although some also by TES. These viruses are probably present in fur seal feces because of dietary habits although, since scats were collected from the ground nearby the animals, environmental cross-contamination should not be ruled out.Sequences with high coverage or similarities to any described virus are showed in Table 2.The high prevalence of virus sequences from crustaceans in the feces analyzed is hardly surprising because A. gazella inhabiting the Antarctic peninsula and the Atlantic sector of the Southern Ocean feed mostly on Antarctic krill Euphasia superba during the summer months42,43,44,45,46,47,48. Sequences from cephalopod viruses were also detected, although were much scarcer than those from crustaceans. This also agrees with current knowledge about the diet of A. gazella in the Atlantic sector of the Southern Ocean, where octopuses and squids are regularly consumed, although in low numbers44,45,46. It is worth noting than not cephalopod beak was recovered from the scats analyzed here48. Among all invertebrate viruses identified, some sequences present low identities with genomes from available databases, probably because Antarctica wildlife has been scarcely explored, forcing bioinformatic analysis to match them with the most similar viruses from these databases.No fish viruses were found in this study. Hard skeletal remains of fishes are often recovered from the scats of A. gazella from the Atlantic sector of the Southern Ocean42,43,44,45,46,47 and occurred indeed in the samples analysed here48, but stable isotope analysis of blood and whiskers revealed a negligible contribution of fish to the assimilate diet of juvenile and subadult male A. gazella49, which likely explain the absence of fish viruses in the samples analized here. Additionaly, no data on the virome present in the fish species regularly consumed by A. gazella has been published to our knowledge, with information limited to the bacteriome32, so even in case fish viruses were sequenced, it might not be correctly assigned to a fish host. Nevertheless, the methodology applied in this study had been successfully applied to the identification of the virome of Atlantic fishes50. Furthermore, Li and coworkers.37 and Wille and coworkers.22 also observed viral sequences probably corresponding to fish when analyzing the fecal virome of the California sea lions and Antarctic penguins.On the other hand, sequences highly similar to Coelho and Khabarov viral polymerases (greater than 98% of aminoacid identity), previously described in chinstrap penguins (Pygoscelis antarcticus) by Wille and coworkers22, were found in this study. The consumption of penguins by A. gazella during the summer months has been reported widely51,52,53,54,55, penguins feathers were reported from the scats analyzed in this study48 and stable isotope analysis of blood and whiskers revealed penguins as the second most relevant prey from juvenile and subadult male A. gazella in the population studied here49. This evidence is consistent with the presence of virus from chinstrap penguins in the samples analysed here. All in all, the study of fecal virome constitutes a very promising tool to explore the consumers’ diet. More

Darwin C. The structure and distribution of coral reefs, 3rd edn. D. Appleton & Company: New York, NY, USA, 1889.Lajeunesse TC, Parkinson JE, Gabrielson PW, Jeong HJ, Reimer JD, Voolstra CR, et al. Systematic Revision of Symbiodiniaceae Highlights the Antiquity and Diversity of Coral Endosymbionts. Curr Biol. 2018;28:2570–80.e6.CAS 
PubMed 

Google Scholar 
Muscatine L, Porter JW. Reef corals: mutualistic symbioses adapted to nutrient-poor environments. Bioscience. 1977;27:454–60.
Google Scholar 
Rohwer F, Seguritan V, Azam F, Knowlton N. Diversity and distribution of coral-associated bacteria. Mar Ecol Prog Ser. 2002;243:1–10.
Google Scholar 
Rosenberg E, Koren O, Reshef L, Efrony R, Zilber-Rosenberg I. The role of microorganisms in coral health, disease and evolution. Nat Rev Microbiol. 2007;5:355–62.CAS 
PubMed 

Google Scholar 
Muscatine L. The role of symbiotic algae in carbon and energy flux in reef corals. Coral Reefs. 1990;25:75–87.
Google Scholar 
Falkowski PG, Dubinsky Z, Muscatine L, McCloskey L. Population control in symbiotic corals. Bioscience. 1993;43:606–11.
Google Scholar 
Baker DM, Freeman CJ, Wong JCY, Fogel ML, Knowlton N. Climate change promotes parasitism in a coral symbiosis. ISME J. 2018;12:921–30.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Perna G, Geißler L, et al. Heat stress reduces the contribution of diazotrophs to coral holobiont nitrogen cycling. ISME J. 2022;16:1110–8.PubMed 

Google Scholar 
Rädecker N, Pogoreutz C, Voolstra CR, Wiedenmann J, Wild C. Nitrogen cycling in corals: the key to understanding holobiont functioning? Trends Microbiol. 2015;23:490–7.PubMed 

Google Scholar 
Bourne DG, Webster NS. Coral Reef Bacterial Communities. In: Rosenberg E, DeLong EF, editors. The Prokaryotes. Springer: Berlin Heidelberg; 2013. pp. 163–87.Ainsworth DT, Krause L, Bridge T, Torda G, Raina J-B, Zakrzewski M, et al. The coral core microbiome identifies rare bacterial taxa as ubiquitous endosymbionts. ISME J. 2015;9:2261–74.CAS 

Google Scholar 
Pernice M, Raina J-B, Rädecker N, Cárdenas A, Pogoreutz C, Voolstra CR. Down to the bone: the role of overlooked endolithic microbiomes in reef coral health. ISME J. 2020;14:325–34.PubMed 

Google Scholar 
Pogoreutz C, Oakley CA, Rädecker N, Cárdenas A, Perna G, Xiang N, et al. Coral holobiont cues prime Endozoicomonas for a symbiotic lifestyle. ISME J. 2022;16:1883–95.CAS 
PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Voolstra CR, Rädecker N, Weis V. The coral holobiont highlights the dependence of cnidarian animal hosts on their associated microbes. In: Bosch TCG, Hadfield MG, editors. Cellular Dialogues in the Holobiont. Boca Raton: CRC Press; 2020. pp. 91–118.Babbin AR, Tamasi T, Dumit D, Weber L, Rodríguez MVI, Schwartz SL, et al. Discovery and quantification of anaerobic nitrogen metabolisms among oxygenated tropical Cuban stony corals. ISME J. 2021;15:1222–35.CAS 
PubMed 

Google Scholar 
Glaze TD, Erler DV, Siljanen HMP. Microbially facilitated nitrogen cycling in tropical corals. ISME J. 2022;16:68–77.CAS 
PubMed 

Google Scholar 
Lesser MP, Morrow KM, Pankey SM, Noonan SHC. Diazotroph diversity and nitrogen fixation in the coral Stylophora pistillata from the Great Barrier Reef. ISME J. 2018;12:813–24.CAS 
PubMed 

Google Scholar 
Cardini U, Bednarz VN, Naumann MS, van Hoytema N, Rix L, Foster RA, et al. Functional significance of dinitrogen fixation in sustaining coral productivity under oligotrophic conditions. Proc R Soc B. 2015;282:20152257.PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Wild C, Voolstra CR. Nitrogen fixation aligns with nifH abundance and expression in two coral trophic functional groups. Front Microbiol. 2017;8:1187.PubMed 
PubMed Central 

Google Scholar 
Pogoreutz C, Rädecker N, Cárdenas A, Gärdes A, Voolstra CR, Wild C. Sugar enrichment provides evidence for a role of nitrogen fixation in coral bleaching. Glob Chang Biol. 2017;23:3838–48.PubMed 

Google Scholar 
Bednarz VN, van de Water JA, Rabouille S, Maguer JF, Grover R, Ferrier‐Pagès C. Diazotrophic community and associated dinitrogen fixation within the temperate coral Oculina patagonica. Environ Microbiol. 2019;21:480–95.CAS 
PubMed 

Google Scholar 
Lema KA, Willis BL, Bourne DG. Corals form characteristic associations with symbiotic nitrogen-fixing bacteria. Appl Environ Microbiol. 2012;78:3136–44.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lema KA, Clode PL, Kilburn MR, Thornton R, Willis BL, Bourne DG. Imaging the uptake of nitrogen-fixing bacteria into larvae of the coral Acropora millepora. ISME J. 2016;10:1804–8.CAS 
PubMed 

Google Scholar 
Santos HF, Carmo FL, Duarte G, Dini-Andreote F, Castro CB, Rosado AS, et al. Climate change affects key nitrogen-fixing bacterial populations on coral reefs. ISME J. 2014;8:2272–9.PubMed 
PubMed Central 

Google Scholar 
Rädecker N, Pogoreutz C, Gegner HM, Cárdenas A, Roth F, Bougoure J, et al. Heat stress destabilizes symbiotic nutrient cycling in corals. Proc Natl Acad Sci USA. 2021;118:e2022653118.PubMed 
PubMed Central 

Google Scholar 
Braker G, Fesefeldt A, Witzel K-P. Development of PCR primer systems for amplification of nitrite reductase genes (nirK and nirS) to detect denitrifying bacteria in environmental samples. Appl Environ Microbiol. 1998;64:3769–75.CAS 
PubMed 
PubMed Central 

Google Scholar 
Tilstra A, El-Khaled YC, Roth F, Rädecker N, Pogoreutz C, Voolstra CR, et al. Denitrification aligns with N2 fixation in Red Sea corals. Sci Rep. 2019;9:1–9.Tilstra A, Roth F, El-Khaled YC, Pogoreutz C, Rädecker N, Voolstra CR, et al. Relative abundance of nitrogen cycling microbes in coral holobionts reflects environmental nitrate availability. R Soc Open Sci. 2021;8:201835.CAS 
PubMed 
PubMed Central 

Google Scholar 
Xiang N, Hassenrück C, Pogoreutz C, Rädecker N, Simancas-Giraldo SM, Voolstra CR, et al. Contrasting microbiome dynamics of putative denitrifying bacteria in two octocoral species exposed to dissolved organic carbon (DOC) and warming. Appl Environ Microbiol. 2022;88:e01886-21.El-Khaled YC, Roth F, Tilstra A, Rädecker N, Karcher DB, Kürten B, et al. In situ eutrophication stimulates dinitrogen fixation, denitrification, and productivity in Red Sea coral reefs. Mar Ecol Prog Ser. 2020;645:55–66.CAS 

Google Scholar 
Beauchamp EG, Trevors JT, Paul JW. Carbon sources for bacterial Denitrification. In: Stewart BA. Advances in Soil Science. Springer: New York, NY; 1989. pp. 113–42.Baker AC. Flexibility and Specificity in Coral-Algal Symbiosis: Diversity, Ecology, and Biogeography of Symbiodinium. Ann Rev Ecol Evol Syst. 2003;34:661–89.
Google Scholar 
Wang J-T, Chen Y-Y, Tew KS, Meng P-J, Chen CA. Physiological and Biochemical Performances of Menthol-Induced Aposymbiotic Corals. PLoS ONE. 2012;7:e46406.CAS 
PubMed 
PubMed Central 

Google Scholar 
Cui G, Liew YJ, Li Y, Kharbatia N, Zahran NI, Emwas A-H, et al. Host-dependent nitrogen recycling as a mechanism of symbiont control in Aiptasia. PLoS Genet. 2019;15:e1008189.CAS 
PubMed 
PubMed Central 

Google Scholar 
Rädecker N, Raina J-B, Pernice M, Perna G, Guagliardo P, Kilburn MR, et al. Using Aiptasia as a Model to Study Metabolic Interactions in Cnidarian-Symbiodinium Symbioses. Front Physiol. 2018;9:214.PubMed 
PubMed Central 

Google Scholar 
Voolstra CR. A journey into the wild of the cnidarian model systemAiptasiaand its symbionts. Mol Ecol. 2013;22:4366–8.PubMed 

Google Scholar 
Sunagawa S, Wilson EC, Thaler M, Smith ML, Caruso C, Pringle JR, et al. Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genom. 2009;10:258.
Google Scholar 
Xiang T, Hambleton EA, DeNofrio JC, Pringle JR, Grossman AR. Isolation of clonal axenic strains of the symbiotic dinoflagellate Symbiodinium and their growth and host specificity1. J Phycol. 2013;49:447–58.CAS 
PubMed 

Google Scholar 
Thornhill DJ, Lewis AM, Wham DC, Lajeunesse TC. Host‐specialist lineages dominate the adaptive radiation of reef coral endosymbionts. Evolution. 2014;68:352–67.CAS 
PubMed 

Google Scholar 
Bieri T, Onishi M, Xiang T, Grossman AR, Pringle JR. Relative Contributions of Various Cellular Mechanisms to Loss of Algae during Cnidarian Bleaching. PLoS ONE. 2016;11:e0152693.PubMed 
PubMed Central 

Google Scholar 
Baumgarten S, Simakov O, Esherick LY, Liew YJ, Lehnert EM, Michell CT, et al. The genome of Aiptasia, a sea anemone model for coral symbiosis. Proc Natl Acad Sci USA. 2015;112:11893–8.CAS 
PubMed 
PubMed Central 

Google Scholar 
Correa AMS, McDonald MD, Baker AC. Development of clade-specific Symbiodinium primers for quantitative PCR (qPCR) and their application to detecting clade D symbionts in Caribbean corals. Mar Biol. 2009;156:2403–11.CAS 

Google Scholar 
Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods. 2001;25:402–8.CAS 
PubMed 

Google Scholar 
Lee JA, Francis CA. DeepnirSamplicon sequencing of San Francisco Bay sediments enables prediction of geography and environmental conditions from denitrifying community composition. Environ Microbiol. 2017;19:4897–912.CAS 
PubMed 

Google Scholar 
Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6:279–84.CAS 
PubMed 

Google Scholar 
Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.CAS 
PubMed 
PubMed Central 

Google Scholar 
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBO J. 2011;17:10–2.
Google Scholar 
Boutet E, Lieberherr D, Tognolli M, Schneider M, Bairoch A. UniProtKB/Swiss-Prot: the manually annotated section of the UniProt KnowledgeBase. Methods Mol Biol. 2007;406:89–112.Abascal F, Zardoya R, Telford MJ. TranslatorX: multiple alignment of nucleotide sequences guided by amino acid translations. Nucleic Acids Res. 2010;38:7–13.
Google Scholar 
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.CAS 
PubMed 
PubMed Central 

Google Scholar 
Kearse M, Moir R, Wilson A, Stones-Havas S, Cheung M, Sturrock S, et al. Geneious Basic: An integrated and extendable desktop software platform for the organization and analysis of sequence data. Bioinformatics. 2012;28:1647–9.PubMed 
PubMed Central 

Google Scholar 
Fish JA, Chai B, Wang Q, Sun Y, Brown CT, Tiedje JM, et al. FunGene: the functional gene pipeline and repository. Front Microbiol. 2013;4:291.PubMed 
PubMed Central 

Google Scholar 
Wickham H. ggplot2. Wiley Interdiscip Rev Comput Stat. 2011;3:180–5.Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Commun Ecol Package. 2007;10:719.
Google Scholar 
McMurdie PJ, Holmes S. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLoS ONE. 2013;8:e61217.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:1–11.CAS 

Google Scholar 
Meunier V, Geissler L, Bonnet S, Rädecker N, Perna G, Grosso O, et al. Microbes support enhanced nitrogen requirements of coral holobionts in a high CO 2 environment. Mol Ecol. 2021;30:5888–99.CAS 
PubMed 

Google Scholar 
Geissler L, Meunier V, Rädecker N, Perna G, Rodolfo-Metalpa R, Houlbrèque F, et al. Highly Variable and Non-complex Diazotroph Communities in Corals From Ambient and High CO2 Environments. Front Mar Sci. 2021;8:754682.Thornhill DJ, Xiang Y, Pettay DT, Zhong M, Santos SR. Population genetic data of a model symbiotic cnidarian system reveal remarkable symbiotic specificity and vectored introductions across ocean basins. Mol Ecol. 2013;22:4499–515.CAS 
PubMed 

Google Scholar 
Röthig T, Costa RM, Simona F, Baumgarten S, Torres AF, Radhakrishnan A, et al. Distinct bacterial communities associated with the coral model Aiptasia in aposymbiotic and symbiotic states with Symbiodinium. Front Mar Sci. 2016;3:234.
Google Scholar 
Hartman LM, Blackall LL, van Oppen MJH. Antibiotics reduce bacterial load in Exaiptasia diaphana, but biofilms hinder its development as a gnotobiotic coral model. Access Microbiol. 2022;4:000314.CAS 
PubMed 
PubMed Central 

Google Scholar 
Lawson CA, Raina JB, Kahlke T, Seymour JR, Suggett DJ. Defining the core microbiome of the symbiotic dinoflagellate, Symbiodinium. Environ Microbiol Rep. 2018;10:7–11.CAS 
PubMed 

Google Scholar 
Matthews JL, Raina JB, Kahlke T, Seymour JR, van Oppen MJ, Suggett DJ. Symbiodiniaceae‐bacteria interactions: rethinking metabolite exchange in reef‐building corals as multi‐partner metabolic networks. Environ Microbiol. 2020;22:1675–87.PubMed 

Google Scholar 
Costa RM, Cárdenas A, Loussert-Fonta C, Toullec G, Meibom A, Voolstra CR. Surface Topography, Bacterial Carrying Capacity, and the Prospect of Microbiome Manipulation in the Sea Anemone Coral Model Aiptasia. Front Microbiol. 2021;12:637834.Pelve EA, Fontanez KM, DeLong EF. Bacterial succession on sinking particles in the ocean’s interior. Front Microbiol. 2017;8:2269.PubMed 
PubMed Central 

Google Scholar 
Welles L, Lopez-Vazquez CM, Hooijmans CM, Van Loosdrecht MCM, Brdjanovic D. Prevalence of ‘Candidatus Accumulibacter phosphatis’ type II under phosphate limiting conditions. AMB Express. 2016;6:1–12.Kaneko T. Complete Genomic Sequence of Nitrogen-fixing Symbiotic Bacterium Bradyrhizobium japonicum USDA110. DNA Res. 2002;9:189–97.PubMed 

Google Scholar 
Cziesielski MJ, Liew YJ, Cui G, Schmidt-Roach S, Campana S, Marondedze C, et al. Multi-omics analysis of thermal stress response in a zooxanthellate cnidarian reveals the importance of associating with thermotolerant symbionts. Proc R Soc B: Biol Sci. 2018;285:20172654.
Google Scholar 
Xiang T, Lehnert E, Jinkerson RE, Clowez S, Kim RG, Denofrio JC, et al. Symbiont population control by host-symbiont metabolic interaction in Symbiodiniaceae-cnidarian associations. Nat Commun. 2020;11:1–9.CAS 

Google Scholar  More