Philippa Kaur

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

Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl. Acad. Sci. 113, 11261–11265 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Salo, P., Korpimäki, E., Banks, P. B., Nordström, M. & Dickman, C. R. Alien predators are more dangerous than native predators to prey populations. Proc. R. Soc. B Biol. Sci. 274, 1237–1243 (2007).
Google Scholar 
Losos, J. B., Schoener, T. W. & Spiller, D. A. Predator-induced behaviour shifts and natural selection in field-experimental lizard populations. Nature 432, 505–508 (2004).ADS 
CAS 
PubMed 

Google Scholar 
Komine, H., Iwai, N. & Kaji, K. Rapid responses in morphology and performance of native frogs induced by predation pressure from invasive mongooses. Biol. Invasions 23, 1293–1305 (2021).
Google Scholar 
Nunes, A. L. et al. A global meta-analysis of the ecological impacts of alien species on native amphibians. Proc. R. Soc. B Biol. Sci. 286, 20182528 (2019).
Google Scholar 
Berthon, K. How do native species respond to invaders? Mechanistic and trait-based perspectives. Biol. Invasions 17, 2199–2211 (2015).
Google Scholar 
Strauss, S. Y., Lau, J. A. & Carroll, S. P. Evolutionary responses of natives to introduced species: What do introductions tell us about natural communities?. Ecol. Lett. 9, 354–371 (2006).
Google Scholar 
Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).PubMed 
PubMed Central 

Google Scholar 
Sih, A. et al. Predator–prey naïveté, antipredator behavior, and the ecology of predator invasions. Oikos 119, 610–621 (2010).
Google Scholar 
Gould, S. J. & Vrba, E. Exaptation—A missing term in the science of form. Paleobiology 8, 4–15 (1982).
Google Scholar 
Komine, H. et al. Rapid behavioural responses of native frogs caused by past predation pressure from invasive mongooses. J. Zool. 310, 126–134 (2020).
Google Scholar 
Hoare, J. M., Pledger, S., Nelson, N. J. & Daugherty, C. H. Avoiding aliens: Behavioural plasticity in habitat use enables large, nocturnal geckos to survive Pacific rat invasions. Biol. Conserv. 136, 510–519 (2007).
Google Scholar 
Trompeter, W. P. & Langkilde, T. Invader danger: Lizards faced with novel predators exhibit an altered behavioral response to stress. Horm. Behav. 60, 152–158 (2011).CAS 
PubMed 

Google Scholar 
Thawley, C. J., Goldy-Brown, M., McCormick, G. L., Graham, S. P. & Langkilde, T. Presence of an invasive species reverses latitudinal clines of multiple traits in a native species. Glob. Change Biol. 25, 620–628 (2019).ADS 

Google Scholar 
Melotto, A., Manenti, R. & Ficetola, G. F. Rapid adaptation to invasive predators overwhelms natural gradients of intraspecific variation. Nat. Commun. 11, 3608 (2020).ADS 
PubMed 
PubMed Central 

Google Scholar 
Langkilde, T. Invasive fire ants alter behavior and morphology of native lizards. Ecology 90, 208–217 (2009).PubMed 

Google Scholar 
Moore, R. D., Griffiths, R. A., O’Brien, C. M., Murphy, A. & Jay, D. Induced defences in an endangered amphibian in response to an introduced snake predator. Oecologia 141, 139–147 (2004).ADS 
PubMed 

Google Scholar 
Fritts, T. H. & Rodda, G. H. The role of introduced species in the degradation of island ecosystems: A case history of Guam. Annu. Rev. Ecol. Syst. 29, 113–140 (1998).
Google Scholar 
Caut, S., Angulo, E. & Courchamp, F. Dietary shift of an invasive predator: Rats, seabirds and sea turtles. J. Appl. Ecol. 45, 428–437 (2008).PubMed 

Google Scholar 
Bonnaud, E. et al. The diet of feral cats on islands: A review and a call for more studies. Biol. Invasions 13, 581–603 (2011).
Google Scholar 
Guiden, P. W., Bartel, S. L., Byer, N. W., Shipley, A. A. & Orrock, J. L. Predator–prey interactions in the Anthropocene: Reconciling multiple aspects of novelty. Trends Ecol. Evol. 34, 616–627 (2019).PubMed 

Google Scholar 
Savidge, J. A. Extinction of an island forest avifauna by an introduced snake. Ecology 68, 660–668 (1987).
Google Scholar 
Wu, N. C., Alton, L. A., Clemente, C. J., Kearney, M. R. & White, C. R. Morphology and burrowing energetics of semi-fossorial skinks (Liopholis spp.). J. Exp. Biol. 218, 2416–2426 (2015).PubMed 

Google Scholar 
Losos, J. B. The evolution of form and function: Morphology and locomotor performance in West Indian lizards. Evolution 44, 1189–1203 (1990).PubMed 

Google Scholar 
Irschick, D. J. et al. A comparative analysis of clinging ability among pad-bearing lizards. Biol. J. Linn. Soc. 59, 21–35 (1996).
Google Scholar 
Winchell, K. M., Maayan, I., Fredette, J. R. & Revell, L. J. Linking locomotor performance to morphological shifts in urban lizards. Proc. R. Soc. B Biol. Sci. 285, 20180229 (2018).
Google Scholar 
Tan, W. C., Vanhooydonck, B., Measey, J. & Herrel, A. Morphology, locomotor performance and habitat use in southern African agamids. Biol. J. Linn. Soc. 130, 166–177 (2020).
Google Scholar 
Snyder, R. C. The anatomy and function of the pelvic girdle and hindlimb in lizard locomotion. Am. J. Anat. 95, 1–45 (1954).CAS 
PubMed 

Google Scholar 
Losos, J. B., Schoener, T. W., Langerhans, R. B. & Spiller, D. A. Rapid temporal reversal in predator-driven natural selection. Science 314, 1111 (2006).ADS 
CAS 
PubMed 

Google Scholar 
Anson, J. R., Dickman, C. R., Boonstra, R. & Jessop, T. S. Stress triangle: Do introduced predators exert indirect costs on native predators and prey?. PLoS One 8, e60916 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Sheriff, M. J., Peacor, S. D., Hawlena, D. & Thaker, M. Non-consumptive predator effects on prey population size: A dearth of evidence. J. Anim. Ecol. 89, 1302–1316 (2020).PubMed 

Google Scholar 
Cabrera-Pérez, M. Á., Gallo-Barneto, R., Esteve, I., Patiño-Martínez, C. & López-Jurado, L. F. The management and control of the California kingsnake in Gran Canaria (Canary Islands): Project LIFE+ Lampropeltis. Aliens Invasive Species Bull. 32, 20–28 (2012).
Google Scholar 
Hubbs, B. Common Kingsnakes: A Natural History of Lampropeltis getula (Tricolor Books, 2009).
Google Scholar 
Pyron, R. A. & Burbrink, F. T. Systematics of the common kingsnake (Lampropeltis getula; Serpentes: Colubridae) and the burden of heritage in taxonomy. Zootaxa 2241, 22–32 (2009).
Google Scholar 
Monzón-Argüello, C. et al. Snakes on an island: Independent introductions have different potentials for invasion. Conserv. Genet. 16, 1225–1241 (2015).
Google Scholar 
Piquet, J. C. & López-Darias, M. Invasive snake causes massive reduction of all endemic herpetofauna on Gran Canaria. Proc. R. Soc. B Biol. Sci. 288, 20211939 (2021).
Google Scholar 
Martín-González, E. & Sánchez-Pinto, L. Nuevos hallazgos de vertebrados fósiles de Fuerteventura: Identificación de una especie de serpiente utilizando técnicas de micro-escáner. Boletín la Asoc. Amigos del Mus. la Nat. y el Hombre Tenerife 15, 76–85 (2013).
Google Scholar 
García-Talavera, F., Rage, J.-C. & Barahona, F. The first record of snakes on the Canary Islands: A vertebra from the Upper Miocene of Lanzarote. Amphibia-Reptilia 19, 419–425 (1998).
Google Scholar 
Martín, A. & Lorenzo, J. Aves del archipiélago Canario (Francisco Lemus Editor S.L., 2001).
Google Scholar 
Nogales, M. & Medina, F. M. Trophic ecology of feral cats (Felis silvestris f. catus) in the main environments of an oceanic archipelago (Canary Islands): An updated approach. Mamm. Biol. 74, 169–181 (2009).
Google Scholar 
Salvador, A. & Pleguezuelos, J. Reptiles españoles: Identificación, historia natural y distribución (Esfagnos, 2002).
Google Scholar 
Vernet, R., Castanet, J. & Baez, M. Comparative water fux and daily energy expenditure of lizards of the genus Gallotia (Lacertidae) from the Canary Islands. Amphibia-Reptilia 16, 55–66 (1995).
Google Scholar 
Brown, R. P. Microevolution and Ecophysiology of Canary Island Skinks (Chalcides) (Thesis from the University of Aberdeen, 1990).
Google Scholar 
Penado, A. et al. Where to “rock”? Choice of retreat sites by a gecko in a semi-arid habitat. Acta Herpetol. 10, 47–54 (2015).
Google Scholar 
Brown, R. P. Thermal biology of the gecko Tarentola boettgeri: Comparisons among populations from different elevation within Gran Canaria. Herpetologica 52, 396–405 (1996).
Google Scholar 
Wiseman, K. D., Greene, H. W., Koo, M. S. & Long, D. J. Feeding ecology of generalist predator, the California kingsnake (Lampropeltis californiae): Why rare prey matter. Herpetol. Conserv. Biol. 14, 1–30 (2019).
Google Scholar 
King, R. B. Predicted and observed maximum prey size—Snake size allometry. Funct. Ecol. 16, 766–772 (2002).
Google Scholar 
Crystal-Ornelas, R. & Lockwood, J. L. The ‘known unknowns’ of invasive species impact measurement. Biol. Invasions 22, 1513–1525 (2020).
Google Scholar 
del Arco Aguilar, M. J. & Rodríguez Delgado, O. Vegetation of the Canary Islands (Springer, 2018).
Google Scholar 
AEMET. Standard climate values. AEMET (2022). http://www.aemet.es/en/serviciosclimaticos/datosclimatologicos/valoresclimatologicos?k=mur#tab2 (Accessed 9th February 2021)GESPLAN. Action A.1: Desarrollo de protocolos para la sistematización de las labores de captura y la recolección de datos. Official report (2015).Atzori, A. et al. Advances in methodologies of sexing and marking less dimorphic gekkonid lizards: The study case of the Moorish gecko, Tarentola mauritanica. Amphibia-Reptilia 28, 449–454 (2007).
Google Scholar 
Stuart, Y. E. et al. Rapid evolution of a native species following invasion by a congener. Science 346, 463–466 (2014).ADS 
CAS 
PubMed 

Google Scholar 
Peig, J. & Green, A. J. New perspectives for estimating body condition from mass/length data: The scaled mass index as an alternative method. Oikos 118, 1883–1891 (2009).
Google Scholar 
Peig, J. & Green, A. J. The paradigm of body condition: A critical reappraisal of current methods based on mass and length. Funct. Ecol. 24, 1323–1332 (2010).
Google Scholar 
Molina-Borja, M. & Rodríguez-Domínguez, M. A. Evolution of biometric and life-history traits in lizards (Gallotia) from the Canary Islands. J. Zool. Syst. Evol. Res. 42, 44–53 (2004).
Google Scholar 
Suárez, C. R., Rodríguez-Domínguez, M. A. & Molina-Borja, M. Sexual dimorphism in morphological traits and scaling relationships in two populations of Gallotia stehlini (Fam. Lacertidae: Squamata) from Gran Canaria. Afr. J. Herpetol. 65, 1–20 (2016).
Google Scholar 
Tejangkura, T. Hybrid Zone Genetics and Within-Island Diversity of the Gecko Tarentola boettgeri (Liverpool John Moores University, 2012).
Google Scholar 
Rózsa, L., Reiczigel, J. & Majoros, G. Quantifying parasites in samples of hosts. J. Parasitol. 86, 228–232 (2000).PubMed 

Google Scholar 
Zapatero-Ramos, L. M., Gonzalez-Santiago, P. M., Solera-Puertas, M. A. & Carvajal-Gallardo, M. M. Estudio de nuevas especies de Pterigosomidae (Acari: Actinedida) sobre geckónidos de las Islas Canarias. Descripción de Geckobia canariensis n. sp. y Geckobia tinerfensis n. sp. Rev. Ibérica Parasitol. 49, 51–64 (1989).
Google Scholar 
Fain, A. & Bannert, B. Two new species of Ophionyssus Mégnin (Acari: Macronyssidae) parasitic on lizards of the genus Gallotia Boulenger (Reptilia: Lacertidae) from the Canary Islands. Int. J. Acarol. 26, 41–50 (2000).
Google Scholar 
Rosner, B. On the detection of many outliers. Technometrics 17, 221–227 (1975).MathSciNet 
MATH 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Google Scholar 
Hartig, F. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.1 (2021).Langsrud, Ø. ANOVA for unbalanced data: Use type II instead of type III sums of squares. Stat. Comput. 13, 163–167 (2003).MathSciNet 

Google Scholar 
Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means. R package version 1.5.5-1 (2021).Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300 (1995).MathSciNet 
MATH 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).
Google Scholar 
Halliday, T. R. & Verrell, P. A. Body size and age in amphibians and reptiles. J. Herpetol. 22, 253–265 (1988).
Google Scholar 
Lopez-Darias, M., Vanhooydonck, B., Cornette, R. & Herrel, A. Sex-specific differences in ecomorphological relationships in lizards of the genus Gallotia. Funct. Ecol. 29, 506–514 (2015).
Google Scholar 
Márquez, R. & Cejudo, D. Defensive behavior as an escape strategy in four species of Gallotia (Sauria, Lacertidae) from the Canary Islands. Copeia 2000, 601–605 (2000).
Google Scholar 
Moon, B. R., Penning, D. A., Segall, M. & Herrel, A. Feeding in snakes: Form, function, and evolution of the feeding system. In Feeding in Vertebrates: Evolution, Morphology, Behavior, Biomechanics (eds Bels, V. & Whishaw, I. Q.) 527–574 (Springer, 2019).
Google Scholar 
Castanet, J. & Baéz, M. Adaptation and evolution in Gallotia lizards from the Canary Islands: Age, growth, maturity and longevity. Amphibia-Reptilia 12, 81–102 (1991).
Google Scholar 
Zamora-Camacho, F. J., Reguera, S., Rubiño-Hispán, M. V. & Moreno-Rueda, G. Effects of limb length, body mass, gender, gravidity, and elevation on escape speed in the lizard Psammodromus algirus. Evol. Biol. 41, 509–517 (2014).
Google Scholar 
Glossip, D. & Losos, J. B. Ecological correlates of number of subdigital lamellae in anoles. Herpetologica 53, 192–199 (1997).
Google Scholar 
Crandell, K. E., Herrel, A., Sasa, M., Losos, J. B. & Autumn, K. Stick or grip? Co-evolution of adhesive toepads and claws in Anolis lizards. Zoology 117, 363–369 (2014).PubMed 

Google Scholar 
Landová, E., Jančúchová-Lásková, J., Musilová, V., Kadochová, Š & Frynta, D. Ontogenetic switch between alternative antipredatory strategies in the leopard gecko (Eublepharis macularius): Defensive threat versus escape. Behav. Ecol. Sociobiol. 67, 1113–1122 (2013).
Google Scholar 
Eifler, M. A., Marchand, R., Eifler, D. A. & Malela, K. Habitat use and activity patterns in the nocturnal gecko, Chondrodactylus turneri. Herpetologica 73, 43–47 (2017).
Google Scholar 
Hielen, B. Unterschiedliche Fortpflanzungsstrategien bei Geckos der Gattung Tarentola Gray, 1825. Salamandra 28, 179–194 (1993).
Google Scholar 
Magnhagen, C. Predation risk as a cost of reproduction. Trends Ecol. Evol. 6, 183–186 (1991).CAS 
PubMed 

Google Scholar 
Shine, R. ‘Costs’ of reproduction in reptiles. Oecologia 46, 92–100 (1980).ADS 
PubMed 

Google Scholar 
Moran, E. V. & Alexander, J. M. Evolutionary responses to global change: Lessons from invasive species. Ecol. Lett. 17, 637–649 (2014).PubMed 

Google Scholar 
Whittaker, R. J. & Fernández-Palacios, J. M. Island Biogeography: Ecology, Evolution, and Conservation (Oxford University Press, 2007).
Google Scholar 
Pinya, S., Tejada, S., Capó, X. & Sureda, A. Invasive predator snake induces oxidative stress responses in insular amphibian species. Sci. Total Environ. 566–567, 57–62 (2016).ADS 
PubMed 

Google Scholar 
Genovart, M. et al. The young, the weak and the sick: Evidence of natural selection by predation. PLoS One 5, e9774 (2010).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sand, H., Wikenros, C., Ahlqvist, P., Strømseth, T. H. & Wabakken, P. Comparing body condition of moose (Alces alces) selected by wolves (Canis lupus) and human hunters: Consequences for the extent of compensatory mortality. Can. J. Zool. 90, 403–412 (2012).
Google Scholar 
Vedder, O., Bouwhuis, S. & Sheldon, B. C. The contribution of an avian top predator to selection in prey species. J. Anim. Ecol. 83, 99–106 (2014).PubMed 

Google Scholar 
Lopez, L. K. & Duffy, M. A. Mechanisms by which predators mediate host–parasite interactions in aquatic systems. Trends Parasitol. 37, 890–906 (2021).CAS 
PubMed 

Google Scholar 
Garrido, M. & Pérez-Mellado, V. Human pressure, parasitism and body condition in an insular population of a Mediterranean lizard. Eur. J. Wildl. Res. 61, 617–621 (2015).
Google Scholar 
Amo, L., López, P. & Martín, J. Habitat deterioration affects body condition of lizards: A behavioral approach with Iberolacerta cyreni lizards inhabiting ski resorts. Biol. Conserv. 135, 77–85 (2007).
Google Scholar 
Kindinger, T. L. & Albins, M. A. Consumptive and non-consumptive effects of an invasive marine predator on native coral-reef herbivores. Biol. Invasions 19, 131–146 (2017).
Google Scholar 
Main, A. R. & Bull, C. M. The impact of tick parasites on the behaviour of the lizard Tiliqua rugosa. Oecologia 122, 574–581 (2000).ADS 
CAS 
PubMed 

Google Scholar 
Garrido, M. & Pérez-Mellado, V. Sprint speed is related to blood parasites, but not to ectoparasites, in an insular population of lacertid lizards. Can. J. Zool. 92, 67–72 (2014).
Google Scholar 
Wirsing, A. J., Heithaus, M. R., Brown, J. S., Kotler, B. P. & Schmitz, O. J. The context dependence of non-consumptive predator effects. Ecol. Lett. 24, 113–129 (2021).PubMed 

Google Scholar 
Civantos, E., López, P. & Martín, J. Non-lethal effects of predators on body growth and health state of juvenile lizards, Psammdromus algirus. Physiol. Behav. 100, 332–339 (2010).CAS 
PubMed 

Google Scholar 
Graham, S. P., Freidenfelds, N. A., McCormick, G. L. & Langkilde, T. The impacts of invaders: Basal and acute stress glucocorticoid profiles and immune function in native lizards threatened by invasive ants. Gen. Comp. Endocrinol. 176, 400–408 (2012).CAS 
PubMed 

Google Scholar 
Donihue, C. M. et al. Hurricane-induced selection on the morphology of an island lizard. Nature 560, 88–91 (2018).ADS 
CAS 
PubMed 

Google Scholar 
Donihue, C. M. et al. Hurricane effects on Neotropical lizards span geographic and phylogenetic scales. Proc. Natl. Acad. Sci. 117, 10429–10434 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goodman, B. A., Miles, D. B. & Schwarzkopf, L. Life on the rocks: Habitat use drives morphological and performance evolution in lizards. Ecology 89, 3462–3471 (2008).PubMed 

Google Scholar 
Hendry, A. P., Gotanda, K. M. & Svensson, E. I. Human influences on evolution, and the ecological and societal consequences. Philos. Trans. R. Soc. B Biol. Sci. 372, 20160028 (2017).
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

FAO. The State of the World’s Forests 2018—Forest Pathways to Sustainable Development (FAO, 2018).
Google Scholar 
Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333(6045), 988–993. https://doi.org/10.1126/science.1201609 (2011).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Luyssaert, S. et al. Tradeoffs in using European forests to meet climate objectives. Nature 562(7726), 259–262. https://doi.org/10.1038/s41586-018-0577-1 (2018).Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Büntgen, U. et al. Limited capacity of tree growth to mitigate the global greenhouse effect under predicted warming. Nat. Commun. https://doi.org/10.1038/s41467-019-10174-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Anderegg, W. R. L. et al. Climate-driven risks to the climate mitigation potential of forests. Science 368, 1327. https://doi.org/10.1126/science.aaz7005 (2020).Article 
CAS 

Google Scholar 
Jiang, M. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580(7802), 227–231. https://doi.org/10.1038/s41586-020-2128-9 (2020).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Naudts, K. et al. Europe’s forest management did not mitigate climate warming. Science 351(6273), 597–599. https://doi.org/10.1126/science.aad7270 (2016).Article 
ADS 
CAS 
PubMed 

Google Scholar 
Tong, X. et al. Forest management in southern China generates short term extensive carbon sequestration. Nat. Commun. https://doi.org/10.1038/s41467-019-13798-8 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Yu, K. et al. Effects of stand age on soil respiration in Pinus massoniana plantations in the hilly red soil region of Southern China. CATENA 178, 313–321. https://doi.org/10.1016/j.catena.2019.03.038 (2019).Article 
CAS 

Google Scholar 
Mei, G., Sun, Y. & Sajjad, S. Models for predicting the biomass of Cunninghamia lanceolata trees and stands in southeastern China. PLoS ONE 12, e0169747. https://doi.org/10.1371/journal.pone.0169747 (2017).Article 
CAS 

Google Scholar 
Wu, H. et al. Soil phosphorus bioavailability and recycling increasedwith stand age in Chinese fir plantations. Ecosystems 23, 973–988. https://doi.org/10.1007/s10021-019-00450-1 (2019).Article 

Google Scholar 
State Forestry Administration. General situation of forest resources in China. The 8th National Forest Inventory (State Forestry Administration, 2014).Wang, X. et al. Vegetation carbon storage and density of forest ecosystems in China. Chin. J. Appl. Ecol. 12(1), 13–16 (2001) (in Chinese with English Abstract).ADS 
CAS 

Google Scholar 
Kang, H. et al. Simulating the impact of climate change on the growth of Chinese fir plantations in Fujian province, China. NZ J. For. Sci. 47(1), 20. https://doi.org/10.1186/s40490-017-0102-6 (2017).Article 

Google Scholar 
Lu, Y. et al. A process-based approach to estimate Chinese fir (Cunninghamia lanceolata) distribution and productivity in southern China under climate change. Forests 6, 360–379. https://doi.org/10.3390/f6020360 (2015).Article 

Google Scholar 
Zhang, X. et al. Relative contributions of competition, stand structure, age, and climate factors to tree mortality of Chinese fir plantations: Long-term spacing trials in southern China. For. Ecol. Manag. 465, 118103. https://doi.org/10.1016/j.foreco.2020.118103 (2020).Article 

Google Scholar 
You, R. et al. Variation in wood physical properties and effects of climate for different geographic sources of Chinese fir in subtropical area of China. Sci. Rep. https://doi.org/10.1038/s41598-021-83500-w (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Djomo, A. N., Ibrahima, A., Saborowski, J. & Gravenhorst, G. Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. For. Ecol. Manag. 260(10), 1873–1885. https://doi.org/10.1016/j.foreco.2010.08.034 (2010).Article 

Google Scholar 
Peng, D. et al. Estimating the aboveground biomass for planted forests based on stand age and environmental variables. Remote Sens. 11(19), 2270. https://doi.org/10.3390/rs11192270 (2019).Article 
ADS 

Google Scholar 
Zhou, X. et al. Dynamic allometric scaling of tree biomass and size. Nat. Plants. 7(1), 42–49. https://doi.org/10.1038/s41477-020-00815-8 (2021).Article 
PubMed 

Google Scholar 
Li, L. Study on the tree volume table compilation of Chinese fir in Kaihua Forest Farm (Beijing Forestry University, 2011) http://cdmd.cnki.com.cn/Article/CDMD-10022-1011134655.htm (in Chinese).Wang, J. P. et al. Study on the effect of Chinese fir volume formula on estimating the volume of fir standing trees in different sites. Guizhou For. Technol. 19(1), 26–29 (1991) (in Chinese).
Google Scholar 
Zeng, W. S. et al. Compatible tree volume and aboveground biomass equations for Chinese fir plantation in Guizhou. J. Beijing For. Univ. 33(4), 1–6 (2011) (in Chinese).
Google Scholar 
Xia, Z. S. et al. Construction of tree volume equations for Chinese fir plantation in Guizhou Province, southwestern China. J. Beijing For. Univ. 34(1), 1–5 (2012) (in Chinese).
Google Scholar 
Lin, H. Study on biomass and carbon storage of main coniferous forest in Jiangle state-owned forestry farm. J. Fujian For. Sci. Technol. 45(1), 30–34. https://doi.org/10.13428/j.cnki.fjlk.2018.01.007 (2018) (in Chinese with English Abstract).Article 
ADS 

Google Scholar 
Cai, Z. A study on biomass models of Cunninghamia lanceolata plantation in Fujian. (Beijing Forestry University, 2014), http://cdmd.cnki.com.cn/Article/CDMD-10022-1014327550.htm (in Chinese).Chen, G. et al. Carbon storage in a chronosequence of Chinese fir plantations in southern China. For. Ecol. Manag. 300, 68–76. https://doi.org/10.1016/j.foreco.2012.07.046 (2013).Article 

Google Scholar 
Zhang, G. et al. Biomass Characteristics of dominant tree species (group) at Lingnan forest farm in Anhui province. Scientia Silvae Sinicae. 48(5), 136–140. https://doi.org/10.1007/s11783-011-0280-z (2012) (in Chinese with English abstract).Article 
ADS 
CAS 

Google Scholar 
Shi, W. et al. Biomass model and carbon storage of Chinese fir plantation in Dabieshan Mountains in Anhui. Resour. Environ. Yangtze Basin. 24(5), 758–764. https://doi.org/10.11870/cjlyzyyhj201505007 (2015) (in Chinese with English abstract).Article 

Google Scholar 
Li, H. & Zhao, P. Improving the accuracy of tree-level aboveground biomass equations with height classification at a large regional scale. For. Ecol. Manag. 289, 153–163. https://doi.org/10.1016/j.foreco.2012.10.002 (2013).Article 

Google Scholar 
Zeng, W. & Tang, S. A new general allometric biomass model. Nat. Precedings. https://doi.org/10.1038/npre.2011.6704.1 (2011).Article 

Google Scholar 
Schumacher, F. X. & Hall, F. D. S. Logarithmic expression of timber-tree volume. J. Agric. Res. 47(9), 719–734 (1933).
Google Scholar 
Honer, T. G. A new total cubic foot volume function. For. Chron. 41(4), 476–493. https://doi.org/10.5558/tfc41476-4 (1965).Article 

Google Scholar 
Burkhart, H. E. Cubic-foot volume of loblolly pine to any merchantable top limit. South. J. Appl. For. 2, 7–9. https://doi.org/10.1093/sjaf/1.2.7 (1977).Article 

Google Scholar 
Lee, D., Seo, Y. & Choi, J. Estimation and validation of stem volume equations for Pinus densiflora, Pinus koraiensis, and Larix kaempferi in South Korea. For. Sci. Technol. 13(2), 77–82. https://doi.org/10.1080/21580103.2017.1315963 (2017).Article 

Google Scholar 
Chen, B. H. & Chen, C. Y. A preliminary study on the biomass and productivity of Picea koraiensis forests in the dunes. Scientia Silvae Sinicae 4, 269–278 (1980) (in Chinese).
Google Scholar 
Niklas, K. J. Plant Allometry: The Scaling of Form and Process (University of Chicago Press, 1994).
Google Scholar 
Ketterings, Q. M. et al. Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For. Ecol. Manag. 146, 199–209. https://doi.org/10.1016/S0378-1127(00)00460-6 (2001).Article 

Google Scholar 
Chen, X. G. The biomass and allometric equation of a 20-years-old Cunninghamia lanceolata plantation. Prot. For. Sci. Technol. 4, 28–29, 40. https://doi.org/10.3969/j.issn.1005-5215.2007.04.010.(inChinese) (2007).Article 

Google Scholar 
Wang, X. P. et al. Climatic control of primary forest structure and DBH–height allometry in Northeast China. For. Ecol. Manag. 234, 264–274. https://doi.org/10.1016/j.foreco.2006.07.007 (2006).Article 

Google Scholar 
Peng, C. et al. Developing and evaluating tree height–diameter models at three geographic scales for black spruce in Ontario. N. J. Appl. For. 21(2), 83–92. https://doi.org/10.1093/njaf/21.2.83 (2004).Article 

Google Scholar 
López-Serrano, F. R. et al. Site and weather effects in allometries: A simple approach to climate change effect on pines. For. Ecol. Manag. 215(1–3), 251–270. https://doi.org/10.1016/j.foreco.2005.05.014 (2005).Article 

Google Scholar 
Zhang, C. et al. Developing aboveground biomass equations both compatible with tree volume equations and additive systems for single trees in Poplar plantations in Jiangsu Province, China. Forests 7, 32. https://doi.org/10.3390/f7020032 (2016).Article 

Google Scholar 
Liu, J. C. et al. Comparing non-destructive methods to estimate volume of three tree taxa in Beijing, China. Forests 10, 92. https://doi.org/10.3390/f10020092 (2019).Article 

Google Scholar 
Thangjam, U. et al. Developing tree volume equation for Parkia timoriana grown in home gardens and shifting cultivation areas of North-East India. For. Trees Livelihoods 28(12), 1–13. https://doi.org/10.1080/14728028.2019.1624200 (2019).Article 

Google Scholar 
Dutcă, I. et al. Does slope aspect affect the aboveground tree shape and volume allometry of European Beech (Fagus sylvatica L.) trees?. Forests 13, 1071. https://doi.org/10.3390/f13071071 (2022).Article 

Google Scholar 
Segura, M. & Kanninen, M. Allometric models for tree volume and total aboveground biomass in a tropical humid forest in Costa Rica. Biotropica 37(1), 2–8. https://doi.org/10.2307/30045500 (2005).Article 

Google Scholar 
Wang, X. W. et al. Additive tree biomass equations for Betula platyphylla Suk. plantations in Northeast China. Ann. For. Sci. 75, 60. https://doi.org/10.1007/s13595-018-0738-2 (2018).Article 

Google Scholar 
Niklas, K. J. & Enquist, B. J. Canonical rules for plant organ biomass partitioning and annual allocation. Am. J. Bot. 89(5), 812–819. https://doi.org/10.3732/ajb.89.5.812 (2002).Article 
PubMed 

Google Scholar 
Xiang, W. H. et al. General allometric equations and biomass allocation of Pinus massoniana trees on a regional scale in southern China. Ecol. Res. 26, 697–711. https://doi.org/10.1007/s11284-011-0829-0 (2011).Article 

Google Scholar 
Brown, S. Measuring carbon in forests: Current status and future challenges. Environ. Pollut. 116, 363–372. https://doi.org/10.1016/s0269-7491(01)00212-3 (2002).Article 
CAS 
PubMed 

Google Scholar 
Brassard, B. W. et al. Influence of environmental variability on root dynamics in northern forests. Crit. Rev. Plant Sci. 28, 179–197. https://doi.org/10.1080/07352680902776572 (2009).Article 

Google Scholar 
Montagu, K. D. et al. Developing general allometric relationship for regional estimates of carbon sequestration—An example using Eucalyptus pilularis from seven contrasting sites. For. Ecol. Manag. 204, 113–127. https://doi.org/10.1016/j.foreco.2004.09.003 (2005).Article 

Google Scholar 
Williams, R. J. et al. Allometry for estimating aboveground tree biomass in tropical and subtropical eucalypt woodlands: Towards general predictive equations. Aust. J. Bot. 53, 607–619. https://doi.org/10.1071/BT04149 (2005).Article 

Google Scholar 
Ouimet, R. et al. Estimation of coarse root biomass and nutrient content for sugar maple, jack pine, and black spruce using stem diameter at breast height. Can. J. For. Res. 38, 92–100. https://doi.org/10.1139/x07-134 (2008).Article 

Google Scholar 
Peichl, M. & Arain, M. A. Allometry and partitioning of above-and belowground tree biomass in an age-sequence of white pine forests. For. Ecol. Manag. 253, 68–80. https://doi.org/10.1016/j.foreco.2007.07.003 (2007).Article 

Google Scholar 
Bond-Lamberty, B. et al. Aboveground and below-ground biomass and sapwood area allometric equations for six boreal tree species of northern Manitoba. Can. J. For. Res. 32, 1441–1450. https://doi.org/10.1139/x02-063 (2002).Article 

Google Scholar 
King, J. S. et al. Biomass partitioning in red pine (Pinus resinosa) along a chronosequence in the Upper Peninsula of Michigan. Can. J. For. Res. 37(1), 93–102. https://doi.org/10.1139/x06-217 (2007).Article 

Google Scholar 
Ziania, D. & Mencuccini, M. Aboveground biomass relation-ships for beech (Fagus moesiaca Cz.) trees in Vermio Mountain, northern Greece, and generalised equations for Fagus sp. Ann. For. Sci. 60(5), 439–448. https://doi.org/10.1051/forest:2003036 (2003).Article 

Google Scholar 
Martin, J. G. et al. Aboveground biomass and nitrogen allocation of ten deciduous southern Appalachian tree species. Can. J. For. Res. 28(11), 1648–1659. https://doi.org/10.1139/x98-146 (1998).Article 

Google Scholar 
Wang, C. Biomass allometric equations for 10 co-occurring tree species in Chinese temperate forests. For. Ecol. Manag. 222, 9–16. https://doi.org/10.1016/j.foreco.2005.10.074 (2006).Article 

Google Scholar  More

All animal care and experimental protocols complied with local and federal laws and guidelines and were approved by the appropriate governing body in Berlin, Germany, the Landesamt fur Gesundheit und Soziales (LaGeSo G-0224/20).Experimental breeding and designThe all-female Amazon molly (Poecilia formosa) is a naturally clonal, live-bearing fish species that gives birth to broods of genetically identical offspring. Like all unisexual vertebrates, Amazon mollies are the result of inter-specific hybridization44,45. As such, this ‘frozen hybrid’ has a heterozygous genome from its ancestral P. mexicana mother and P. latipinna father alleviating concerns about reduced genetic variation and the resulting inbreeding depression often associated with artificially selected isogenic animals. Additionally, despite their clonal nature, the Amazon’s genome shows no evidence of increased mutation accumulation, genomic decay or transposable element activity suggesting the genomes of these animals are evolving in similar ways as sexual species46. They reproduce through gynogenesis where the meiotic process is disrupted so that the eggs contain a full maternal genome. The egg must be fused with a sperm from one of their ancestral species to stimulate embryogenesis, but this paternal DNA is not incorporated into the egg. This provides the opportunity to control when reproduction occurs by controlling the females’ access to male sperm donors.We placed adult females, as potential mothers of experimental fish, in individual (5-gallon) breeding tanks with two Atlantic molly (P. mexicana) males for one week to act as sperm donors. Amazon mollies give birth to broods of generally ~8-30 individuals. A brood is born at once (i.e. all individuals are born within minutes of each other) and birth generally happens early in the day close to dawn. These parental fish were lab-bred and themselves sisters, so of the same age and lineage, and were kept at similar social densities and under standardized environmental conditions throughout their lives to further minimize potential variation in maternal experience. Each breeding tank contained an artificial plant as refuge and was checked frequently each day for the presence of offspring, especially during the morning hours when births are most likely. Newborn mollies were always found in the morning and then singly netted by trained animal caretakers, into individual experimental tanks where their behavior was automatically recorded for the next 70 days (see below). Moving the fish from the maternal tank to the experimental tanks was done in a standardized manner (i.e. individual fish were netted and placed into small dishes of water and then placed in the tracking tanks to limit exposure to the air) by the same caretakers to minimize variation in experience among individual fish. Altogether, eight mothers provided offspring that completed the entire 10-week experiment (Supplementary Table 1).Experimental tanks (27 x 27 cm), made of white Perspex, consisted of four equally sized compartments, and were evenly lit from below using 6500K-LEDs. Environmental conditions were highly standardized across tanks: all tanks were on the same 11:13 (L:D) light schedule, water depth was maintained at 10 cm depth, temperature was maintained at 25 ± 1 °C by a room air conditioning system, and fish received a standardized amount of powdered flake fish food (TetraMin™) twice daily. Opaque blinds surrounded the tanks to further limit outside disturbances. All experimental tanks were connected to the same filtration system where water could mix in the sump tank, allowing chemical cues to be shared across all experimental fish. Previous work has shown exposure to just chemical cues of conspecifics is sufficient in preventing the developmental of pathological behavior that could be associated with development in complete isolation14. We initially placed a total of 40 newborn individuals into the tracking tanks. At the end of the 10-week experiment, we were able to achieve complete tracking data on 26 individuals; camera malfunctions prevented data collection on four individuals, two individuals jumped into neighboring tanks causing the loss of data of all four individuals as we could not verify their identity; four newborn individuals escaped through holes in the water outlet of the tanks; and four individuals died as newborns. All results in the manuscript are on these 26 animals, though including data from all 40 (e.g. patterns of individual variation on the first day post birth) did not change the results or their interpretation (see Supplementary Table 2).Behavioral trackingWe developed a custom recording system using Raspberry Pi computers, which are an upcoming low-cost, highly adaptable solution for many applications in the biological sciences25. Specifically, we created a local network of Raspberry Pi 3B + ’s, each connected to a Raspberry Pi camera positioned exactly above an experimental tank, commanded by a lab computer, and connected to the server on the institute network (Supplementary Fig. 1). We programmed the Raspberry Pi’s using pirecorder26 to take timestamped photos every 3 s across the daily light period, each day, for 10 weeks, and store them automatically in dedicated, automatically named folders on the server. Image settings and resolution were thereby optimized to minimize file size while assuring image quality. After the experimental period, we created videos of all the recorded images of each fish of each day. These videos were subsequently tracked with the Biotracker software27, using background subtraction, providing the x, y coordinates of each fish in each frame. We then processed the data, including scaling and converting the coordinates to mm, and, for each frame, computed fish’s swimming speed (cm/s) and distance from the tank walls (cm). We then summarized these variables both on an hourly and daily basis to compute fish’s median swimming speed, inter-quartile range of swimming speeds, activity (proportion of time spent moving >0.5 cm/s), and median border distance. To quantify fish’s body size over time, we randomly selected five photos per week of each compartment, making sure the fish was away from the compartment walls and did not show strong body curvature, and then used ImageJ software to measure total body length (mm) from the tip of the snout to the end of the body. By averaging the measurements of the five images, we acquired one body size measurement per week.Error checkingWe collected up to 924,000 photos on each individual throughout the experimental period resulting in a total of over 24 million data points collected on our experimental animals (N = 26 individuals). To ensure that our tracking software accurately captured the behavior of our fish, we checked for potential tracking errors in two ways. First, we estimated overall error rates. To do this, we selected at random a starting frame from within a day; then we manually checked each of the subsequent 200 frames and identified whether an error was made (fish was not properly located by BioTracker) or not (fish was properly located) by visual inspection of the videos. We estimated the error rate as the number of errors divided by the total number of checked frames. The overall median error rate over the entire observation period was estimated to be 7%. Error rates increased earlier in the observation period when the fish were smaller (Supplementary Note I). As such, as a second step, we manually went through and corrected all frames for the very first day of tracking (i.e. day 1 post-birth) for all fish (~13,200 frames per individual) as this is a critical time period for one of our research questions. This ensured that the resulting behavioral data were completely accurate for this day. This manual correction allowed us the additional opportunity to compare how well our automatically tracked (i.e. not manually corrected) data performed compared to the manually corrected data. We found that the automatically tracked data re-created near identical estimates of among- and within-individual variance components and most importantly the among-individual correlation between the automatically tracked and manually corrected data was over 0.98 for our behavioral variables (Supplementary Note I). This strongly suggests that any errors introduced by our automated tracking software have minimal influence of our behavioral variables at best and do not affect our interpretation of the results.Statistical analysesWe used linear mixed, or hierarchical, models to partition the behavioral variation across different times periods into its among- and within-individual components. Throughout we focused our analysis on the 26 individuals for which we had complete data for the entire 10-week observation period to ensure comparable variation over time and across models.Our first question of interest was to test when individual differences in behavior first appeared over the course of the experiment. We started by investigating behavior on the first day post birth (Fig. 1A, Supplementary Table 2) and then planned to proceed in a day-by-day fashion until significant repeatability in behavior was apparent (Supplementary Table 3). We used hourly median swimming speed (11 observations for each of 26 individuals) as our response variable and included ‘hour’ and ‘total length (TL)’ as fixed effects and ‘individual’ was included as our random effect of interest. Including TL as a covariate allowed us to test whether behavior was related to an offspring’s body size on its first day of life. We set the first hour of the day as 0 and mean-centered TL as this would allow the among- (and within-) individual variance components to be estimated at these values (i.e. the earliest possible moment from when we could record behavior in the fish). We estimated the adjusted repeatability of median swimming speed as the variance attributable to individual identity over the total variance not explained by the fixed effects. We additionally estimated both marginal and conditional R-squared values which estimate the variance explained by the fixed effects only and the variance explained by the fixed and random effects combined, respectively. As our individual experimental fish came from different mothers, we first explored a number of different variance structures including random intercepts and slopes for both individual ID and maternal ID. This allowed us to test whether maternal identity explained variation in individual behavior. However, the most supported model included random intercepts and slopes for individual ID and not for mother ID, indicating that our methods to reduce variation among mothers were successful (Table 1). We used median swimming speed as our behavioral variable of interest throughout the main manuscript, as this behavior was tightly correlated with most of our other behavioral variables (Supplementary Fig. 2); though results using the other behavioral variables yielded the same interpretation (i.e. that significant individuality in (any) behavior was present on the very first day post-birth; Supplementary Table 2).Our second research question was to investigate how individual behavioral variance changed over the course of the entire observation period (70 days). Again, we first explored several different variance structures to test the importance of maternal identity and/or individual identity on behavioral variation. We found support for the inclusion of random slopes at the individual level, but not maternal level (Table 1). This indicates that levels of among- (and within-) individual variation may differ throughout the observation period. To investigate patterns of change in the variance components, we ran a series of models where we centered the observation covariate on different days. Individual intercepts are estimated when all covariates are set to zero, so this allowed us to ‘slice’ the data to estimate the among- and within-individual variance at different time points over the ten weeks. We ran 11 models as we chose to center the data every 7 days (first model was centered on observation 1; 11th model was centered on observation 70). The predicted individual intercepts (best linear unbiased predictors) and estimated variance components from each model are plotted in Fig. 3.We also closely investigated any potential influence of body size and/or growth rate differences on behavioral expression and individual behavioral variation in this entire 10-week data set. First, we estimated the repeatability of both weekly total length and weekly growth rates to determine if individuals consistently differed in these traits. Then, we ran a series of models with median weekly swimming speed as the response variable and included either weekly total length, weekly growth rate, and/or overall growth rate (estimated over the entire 10 weeks), as our fixed effects of interest. Each model also included the random effects of individual intercepts and slopes. Finally, because body size varies both among individuals (some individuals are on average larger than others) and within individuals (as they grow), we also performed within-individual centering of total length. In this fifth model, we included each individual’s average total length and their weekly deviation from their average length as the two fixed effects of interest. Individual identity and slopes were included as random effects. For all models, we estimated the variance explained by the fixed effects (marginal R2) and the fixed and random effects together (conditional R2). These results are reported in Table 2.For our third and final research question, we tested whether early-life behavior predicted later-life behavior. To test this, we estimated the among-individual correlation (including ‘individual ID’ as our random effect) in behavior using multivariate mixed models where the daily median swimming speeds in each week were the response variables (7 observations per week per individual; 10 weeks total; Fig. 4A). Then to investigate how the strength of these correlations may change over development, we used a linear model to test whether the correlation strength was predicted by the interaction between the first week included in the correlation and distance to the next week in the correlation (1, 2, 3, 4 or 5 weeks away in time; Fig. 4B).All models were performed using Markov Chain Monte Carlo estimation with the MCMCglmm package38 in R v3.6.139. We set our models to run 510,000 iterations with a 10,000 burn-in and thinning every 200 iterations. To ensure proper model mixing and convergence, we initially ran 5 independent chains and inspected posterior trace plots of parameter estimates (Supplementary Note II). In a preliminary analysis we tested three different prior settings (Supplementary Note II); results did not change with prior settings so we chose parameter-expanded priors for all models reported here as these are generally considered to be more robust. An R Markdown file with all the results presented here is included in Supplementary Note II.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. 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