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

Cone characteristics: the entire set and individual variantsCones used in all the test variants did not differ from each other in terms of height (coefficient of variance in the Student t-test–F = 1.33 at p = 0.23), diameter (F = 1.77 at p = 0.08), or initial weight (F = 0.86 at p = 0.55). Analysis of variance revealed a significant difference for cone humidity (F = 2.52 at p ˂ 0.05).Cone parameters such as height, diameter and initial weight are factors that can determine the course of the extraction process. Therefore, the relationship between diameter and height for all cones used in the study was described using a linear regression equation ((y=0.2794x+8.3195)), which means that cone diameter increased by 0.28 mm per 1 mm of cone height, ((R=0.778 >0.104-{R}_{kr})).The initial weight of cones may be associated with their harvest time or storage conditions. A linear regression equation was also used to describe the relationship between the height and initial weight of the examined cones (y = 0.238x–3.918), which means that initial weight increased on average by 0.238 g per 1 mm of height, (R = 0.795  > 0.104).Table 2 shows means with standard deviations, the minimum and maximum values of the measured parameters, the range of variance, the coefficient of variation and the standard error for the entire set of studied cones and seeds. The Shapiro–Wilk test showed that the examined characteristics had a normal distribution.Table 2 Cone and seed parameters for the entire study set.Full size tableThe cones used in the study had a height of 21.4–44.1 mm and a diameter of 12.5–24.3 mm. The mean height of a cone was 33.8 (± 3.4) mm and the mean diameter was 17.8 (± 1.6) mm. The initial weight of cones ranged from 2.137 to 9.111 g, with a mean of 4.144 (± 1.019) g. The initial moisture content of cones was from 27.6 to 57.1%, with a mean of 40.4 (± 4.5)%. Analysis was performed for individual extraction variants. The mean values of cone height h, diameter d, initial weight m01, and moisture content W were calculated (Table 3).Table 3 Mean parameter values and standard deviations for the nine process variants.Full size tableThe HSD Tukey test revealed one homogeneous group for cone height encompassing all variants and two homogeneous groups for diameter. The first group consisted of all variants except 7, and the second group included all variants except 2. One homogeneous group was obtained for initial weight. Two homogeneous groups were found for moisture content, one consisting of all variants except 7, and the other one containing variants 1, 4, 5, 6, 7, 8, and 9.Seed extraction results for the studied stepsSeed extraction conditions and timeThe change in cone weight in each step of the extraction process depended on its duration, temperature and humidity conditions in the extraction cabinet, as well as on the initial moisture content of the cones.Humidity inside the drying chamber decreased to an average of 30% after 2 h of the process in each step as a result of increasing temperature. Over the subsequent 4 h of the process, after increasing the temperature, the humidity inside the chamber declined significantly, and then (over 2 and 4 h) it decreased further only slightly, stabilizing at approx. 5% for the 10 h variants, 6% for the 8 h variants, and 8% for 6 h variants on average.Moisture content changes in cones during the seed extraction processThe initial moisture content (u01) of the studied cones was much greater than 0.20 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), which means that special care must be taken during seed extraction, which should be conducted at a temperature of up to 50 °C8.The relatively high moisture content of the cones could be attributed to the absence of preliminary drying in airy storage places prior to seed extraction (which is typically the case in commercial practice) and the early date of cone harvest, at the beginning of the extraction season. The initial (u0x) and final (ukx) moisture content of cones used in each process variant is given with standard deviation in Table 4.Table 4 Initial and final moisture content of cones used in each process variant.Full size tableThe initial moisture content of cones (u0x) in most variants increased with each extraction step due to immersion. In most variants, the final moisture content (ukx) was the highest in the first extraction step and decreased or remained at the same level with each subsequent step.The mean initial moisture content for the three process variants with 10 h of drying was 0.411 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 10 h of drying, the mean moisture content decreased to 0.130 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). The mean initial moisture content in the fifth extraction step was 0.437 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.071 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 10 h reached on average 7% moisture content after extraction steps 4 and 5.The mean initial moisture content for the three process variants with 8 h of drying was 0.412 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 8 h of drying, the mean moisture content decreased to 0.128 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . The mean initial moisture content in the fifth extraction step was 0.440 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.064 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 8 h reached on average 7.1% moisture content after extraction step IV and 6.4% after step V.The mean initial moisture content for the three process variants with 6 h of drying was 0.389 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}). After 6 h of drying, the mean moisture content decreased to 0.129 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . The mean initial moisture content in the fifth extraction step was 0.415 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}), and the final moisture content in that step was 0.084 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{mathrm{d}.mathrm{w}.}^{-1}) . Cones dried for 6 h reached on average 8.9% moisture content after extraction step IV and 8.4% moisture content after step V, which means that their final moisture content was higher than that of cones dried for 8 h and 10 h.The cones with the longest immersion time (15 min) were characterized by the highest initial moisture content in each extraction step as compared to the other two variants (immersion of 5 min and 10 min) with the same drying time. The final moisture content in a given extraction step differed between cones with different immersion times. Cones with an immersion time of 15 min were characterized by the highest final moisture content in individual extraction steps, and those with 5 min immersion revealed the lowest final moisture content.The Tukey HSD test revealed homogeneous groups in terms of initial moisture content (u01, u02, u03, u04, u05) and final moisture content (uk1, uk2, uk3, uk4, uk5) in each step, as shown in Table 4. For instance, four homogeneous groups were found for the final moisture content after extraction step V (uk5): the first one consisted of all variants except for 7, 8, and 9, the second one included variants 1, 2, 3, and 7, the third one comprised of variants 7 and 8, while the fourth one was constituted by variant 9 alone.Using Eq. (1), changes in moisture content were described for each of the tested cones over all five steps of each variant. The equation included the initial and final values of moisture content and the b coefficient for individual cones. The average values of the b coefficient and standard deviations for each extraction step are presented in Table 5 for individual extraction variants.Table 5 Mean values of the b coefficient and standard deviations for the five steps of the studied process variants.Full size tableThe lowest value of the b coefficient was recorded for the first step of the 10h_15min variant (b1 = 0.34), while the highest value was obtained for the fifth step of the 8 h_15 min variant (b5 = 0.60). In the process variants involving 10 and 8 h of drying , the b coefficient increased with each extraction step until the third one; in the fourth step it slightly decreased and in the fifth step it remained constant. In the variants with 6 h of drying the b coefficient almost peaked in the second extraction step and remained at a similar level until the fifth step. In the first steps of the variants with 6 h of drying, the mean value of the b coefficient was 0.54 and did not differ significantly from the coefficients obtained during the other steps. It was noted that in the 8 h_15 min variant, the b coefficients increased over successive steps.Figures 2–3 show examples of curves of actual and model changes in moisture content and the rate of extraction for sample cones, one each for variants 10 h_15 min and 8 h_15 min.Figure 2Diagrams: (a) actual and predicted changes in cone moisture content, (b) extraction rate in five extraction steps for larch cone no. 32 in the 10 h_15 min variant throughout effective extraction.Full size imageFigure 3Diagrams: (a) actual and predicted changes in cone moisture content, (b) extraction rate in five extraction steps for larch cone no. 17 in the 8 h_15 min variant throughout effective extraction.Full size imageEquations for changes in moisture content and extraction rate in consecutive extraction steps are given below for the graphically for the cone shown in Fig. 2 (no. 32 in the 10 h_15 min variant):Step I: ({u}_{1}=0.264cdot {mathrm{e }}^{left(-0.38 cdot {tau }_{i}right)}+0.107) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.100cdot {mathrm{e }}^{(-0.38 cdot {tau }_{i})})Step II: ({u}_{2}=0.372cdot {mathrm{e }}^{left(-0.44 cdot {tau }_{i}right)}+0.095) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.164cdot {mathrm{e }}^{(-0.44 cdot {tau }_{i})})Step III: ({u}_{3}=0.397cdot {mathrm{e }}^{left(-0.49 cdot {tau }_{i}right)}+0.086) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.195cdot {mathrm{e }}^{(-0.49 cdot {tau }_{i})})Step IV: ({u}_{4}=0.536cdot {mathrm{e }}^{left(-0.44 cdot {tau }_{i}right)}+0.080) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.236cdot {mathrm{e }}^{(-0.44 cdot {tau }_{i})})Step V: ({u}_{5}=0.485cdot {mathrm{e }}^{left(-0.46 cdot {tau }_{i}right)}+0.076) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.223cdot {mathrm{e }}^{(-0.46 cdot {tau }_{i})})Equations for changes (Fig. 3) in moisture content and extraction rate in consecutive extraction steps are also given for this cone (no. 17 in the 8 h_15 min variant):Step I: ({u}_{1}=0.304cdot {mathrm{e }}^{left(-0.53 cdot {tau }_{i}right)}+0.113) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.161cdot {mathrm{e }}^{(-0.53 cdot {tau }_{i})})Step II: ({u}_{2}=0.292cdot {mathrm{e }}^{left(-0.55 cdot {tau }_{i}right)}+0.085) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.161cdot {mathrm{e }}^{(-0.55 cdot {tau }_{i})})Step III: ({u}_{3}=0.369cdot {mathrm{e }}^{left(-0.70 cdot {tau }_{i}right)}+0.077) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.258cdot {mathrm{e }}^{(-0.70 cdot {tau }_{i})})Step IV: ({u}_{4}=0.379cdot {mathrm{e }}^{left(-0.71 cdot {tau }_{i}right)}+0.059) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.269cdot {mathrm{e }}^{(-0.71 cdot {tau }_{i})})Step V: ({u}_{5}=0.428cdot {mathrm{e }}^{left(-0.77 cdot {tau }_{i}right)}+0.060) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.330cdot {mathrm{e }}^{(-0.77 cdot {tau }_{i})})Finally, equations for changes in moisture content and extraction rate in consecutive extraction steps are given for cone no. 5 in the 6 h_15 min variant:Step I: ({u}_{1}=0.308cdot {mathrm{e }}^{left(-0.58 cdot {tau }_{i}right)}+0.0904) ,(frac{d{u}_{1}}{d{tau }_{1}}=-0.179cdot {mathrm{e }}^{(-0.58 cdot {tau }_{i})})Step II: ({u}_{2}=0.346cdot {mathrm{e }}^{left(-0.63 cdot {tau }_{i}right)}+0.1070) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.218cdot {mathrm{e }}^{(-0.63 cdot {tau }_{i})})Step III: ({u}_{3}=0.368cdot {mathrm{e }}^{left(-0.63 cdot {tau }_{i}right)}+0.0837) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.232cdot {mathrm{e }}^{(-0.63 cdot {tau }_{i})})Step IV: ({u}_{4}=0.387cdot {mathrm{e }}^{left(-0.68 cdot {tau }_{i}right)}+0.0838) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.263cdot {mathrm{e }}^{(-0.68 cdot {tau }_{i})})Step V: ({u}_{5}=0.396cdot {mathrm{e }}^{left(-0.65 cdot {tau }_{i}right)}+0.0743) , (frac{d{u}_{1}}{d{tau }_{1}}=-0.257cdot {mathrm{e }}^{(-0.65 cdot {tau }_{i})})Figures 2a, 3a show the curves of actual changes in the moisture content of three sample cones subjected to different drying times (10 and 8 h) but the same immersion time (15 min); the curves were fitted to a model which is widely used in descriptions of drying at constant temperature (mostly for vegetables). The present study used variable temperature, which may have influenced the fit of the model, in addition to the input variables (drying and immersion times). The best fit was found for the cone subjected to the variant with 8 h of drying (Fig. 3), with a slight deviation in the first three extraction steps, and with a very good fit in the fourth and fifth steps. The lowest fit was found for the cone subjected to 6 h drying, which may be caused by insufficient drying time (the cone was exposed to 35 °C for 2 h, and to 50 °C for only 4 h).Figures 2b, 3b show diagrams for cone extraction rates at different drying times (10 h and 8 h) at the same immersion times (15 min). As can be seen, extraction rates decreased in the very beginning, which is characteristic of the so-called second period of solid drying (Pabis44).Seed extraction dynamicsTable 2 presents data on the number of scales and seeds for the studied cones. There were from 33 to 70 open scales per cone, with an average of 48 (± 6). From 1 to 76 seeds were extracted per cone, with an average of 36 (± 18). Finally, each cone contained from 5 to 97 seeds, with an average of 52 (± 19). The weight of the extracted seeds ranged from 0.001 g to 0.651 g, on average 0.193 (± 0.109) g.Cones obtained from different process variants did not differ in terms of the number of seeds extracted (F = 0.862 at p = 0.55) or their weight (F = 0.720 at p = 0.674). However, ANOVA did reveal significant differences in the number of scales (F = 3.561 at p ˂0.05) and the total number of seeds per cone (F = 2.93601 at p = 0.003645). Table 6 gives mean scale and seed numbers per larch cone (with standard deviations) for the various extraction variants and homogeneous groups.Table 6 Mean numbers of cone scales and seeds for each process variant.Full size tableOn average, 70% of the seeds were extracted from cones used in all nine study variants, with 30% of the seeds remaining in the cones. Table 7 shows the number of seeds extracted in individual variants and the number of seeds remaining in the cones, expressed as a percentage.Table 7 Number of seeds extracted from and remaining in the cones for each process variant.Full size tableThe greatest number of seeds was obtained in process variants 2–73%, closely followed by variants 3, 1, and 7 (72%), and 8 (70%). The lowest seed yield was obtained from variant 4 (65%).In all study variants, some of the seeds were obtained in the process of extraction in the chamber and some in the process of shaking in the drum (Table 7). The highest number of seeds in the chamber was obtained in variant 2 (69%), and the lowest in variant 9 (56%). On average, the largest quantity of seeds was obtained in the chamber in the 10 h variants, and the lowest quantity in the 6 h variants. Comparing different process variants of the same drying duration, the greatest number of seeds in the chamber were obtained in variants 2, 5, and 7 (and also in variant 8—only 1% fewer). The greatest quantity of seeds extracted by shaking in the drum was obtained in variant 9 (44%), and the lowest in variant 2 (31%). On average, 38% of seeds extracted in all variants were obtained by shaking in the drum.It can be seen that in each of the variants and their individual steps, the highest number of seeds was obtained after 6 h of the process. Figure 4a–c shows the percentage of seeds obtained during the effective extraction time, where the number of seeds extracted at a given step was added cumulatively to those from the previous steps.Figure 4Percentage seed yield dynamics for each step of a five-step extraction process: (a) 10 h of drying, (b) 8 h of drying, (c) 6 h of drying.Full size imageThe diagrams in Fig. 4 show the percentage of seeds obtained throughout the entire process. Each step consists of drying, shaking, immersion, and soaking, except for step V, which involved only drying and shaking without immersion or soaking. Analysis of seed yield over 10 h of drying (Fig. 4a) shows that on average 37% of all extracted seeds were obtained in the first step, 26% in the second step, approx. 20% in the third step, 11% in the fourth step, and about 6% in the fifth step.As regards the 8 h process (Fig. 4b), on average 30% of all extracted seeds were obtained in the in the first extraction step in the 8 h_5 min and 8 h_15 min variants, and as much as 53% in the 8 h_10 min variant. An average of 27% of all seeds were extracted in the second step, 15% in the third step, about 11% in the fourth step, and approx. 5% in the fifth step. The 8 h_10 min variant was characterized by the highest seed yield, beginning in the first step of the process (as compared to the 8 h_5 min and 8 h_15 min variants).As far as the variant with 6 h of drying is concerned (Fig. 4c), on average approx. 46% of all extracted seeds were obtained in the first step, 24% in the second step, 15% in the third step, approx. 11% in the fourth step, about 4% in the fifth step.When extracting seeds from larch cones, scale deflection and the number of obtained seeds are not assessed during the process, as is the case with pine and spruce cones due to the difficulties caused by the aforementioned morphology of larch cones (Tyszkiewicz, 1949). The presented diagrams show that a satisfactory seed yield (60%) was obtained in variants with 8 and 6 h of drying already after 10 h of effective extraction time.The seed yield coefficient, α (3), and the cone mass yield coefficient, β (4), for each extraction variant are presented in Table 8.Table 8 Seed yield coefficient and cone mass yield coefficient for each process variants.Full size tableThe seed yield coefficient was the highest for variants 2 (0.73) and 3 (0.72), and the lowest for variants 4 (0.65) and 6 and 9 (0.67). The cone mass yield coefficient was the highest for variant 5, and the lowest for variant 9.Seed viabilityTable 9 presents germination energy (E) and capacity (Z) for the control seeds as well as for seeds obtained from the various steps of the nine process variants, as well as their corresponding quality classes.Table 9 Germination energy and capacity for the control seeds as well as for seeds obtained from the various extraction process variants.Full size tableGermination energy and capacity for the control sample were 45% and 57%, respectively, meaning that naturally released seeds, not subjected to any thermal or mechanical treatments, were classified in quality class I18. Importantly, seeds obtained from all the studied process variants were also placed in the same class; their germination energy ranged from 30 to 59%, and their germination capacity from 35 to 61%. When analyzing each extraction step separately, no correlation was found between decreasing germination energy and successive steps. However, the average germination energy was 46% for seeds obtained in the first extraction step of all nine variants, 45% for those from the second and third steps, 41% for seeds from the fourth step and 40% for those from the fifth one. Thus, in each subsequent step the average germination energy of seeds was equal or lower than in the previous step, which is consistent with literature reports that prolonged drying may reduce the quality of seeds8. This is also corroborated by the fact that the highest germination energy and capacity was revealed by seeds from variants with 6 h of drying while the lowest germination indicators characterized seeds from the 10 h variants. Furthermore, seeds from variant 1 exhibited the lowest germination energy and capacity and seeds from variant 8–the highest.Another reason for the higher quality of seeds from variants with 6 h of drying may be the lower initial moisture content of the cones due to the longer time they were kept at room temperature immediately before the test (u01 = 0.391 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{d.mathrm{w}.}^{-1}) as compared to u01 = 0.411 ({mathrm{kg}}_{mathrm{water}}cdot {mathrm{kg}}_{d.mathrm{w}.}^{-1}) for seeds from variants with 8 and 10 h of drying). These results are in line with the study of Tyszkiewicz8, who noted that under the same temperature and humidity conditions, the quality of seeds from cones with a lower moisture content did not deteriorate, in contrast to the quality of seeds obtained from cones with a higher moisture content.The germination capacity of seeds calculated from the mean capacity of seeds obtained from the same extraction steps of all process variants was similar at 45% for each of the steps.In summary, in the study the authors investigated a five-step process of extracting seeds from larch cones involving immersion and heat treatment to maximize seed yield. It was found that the two-step process widely used in extractories is insufficient, while a four-step process does not lead to a significantly higher number of obtained seeds. Thus, a three-step process appears to be optimal. 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

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

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

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 

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