Ecology

1.
Cesar, H. J. S., Burke, L. & Pet-Soede, L. The Economics of Worldwide Coral Reef Degradation. 23 (Cesar Environmental Economics Consulting: The Netherlands). https://www.icran.org/pdf/cesardegradationreport.pdf (2003).
2.
Tambutté, E. et al. Observations of the tissue-skeleton interface in the scleractinian coral Stylophora pistillata. Coral Reefs 26, 517–529 (2007).
ADS  Article  Google Scholar 

3.
Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl. Acad. Sci. 115, 1754–1759 (2018).
ADS  CAS  Article  Google Scholar 

4.
Mass, T. et al. Cloning and characterization of four novel coral acid-rich proteins that precipitate carbonates in vitro. Curr. Biol. 23, 1126–1131. https://doi.org/10.1016/j.cub.2013.05.007 (2013).
CAS  Article  PubMed  Google Scholar 

5.
Guo, W. Seawater temperature and buffering capacity modulate coral calcifying pH. Sci. Rep. 9, 1–13 (2019).
Article  Google Scholar 

6.
McCulloch, M. et al. Resilience of cold-water scleractinian corals to ocean acidification: boron isotopic systematics of pH and saturation state up-regulation. Geochim. Cosmochim. Acta 87, 21–34 (2012).
ADS  CAS  Article  Google Scholar 

7.
Guo, W. et al. Ocean acidification has impacted coral growth on the great barrier reef. Geophys. Res. Lett. https://doi.org/10.1029/2019gl086761 (2020).
Article  PubMed  PubMed Central  Google Scholar 

8.
Sevilgen, D. S. et al. Full in vivo characterization of carbonate chemistry at the site of calcification in corals. Sci. Adv. https://doi.org/10.1126/sciadv.aau7447 (2019).
Article  PubMed  PubMed Central  Google Scholar 

9.
Venn, A., Tambutté, E., Holcomb, M., Allemand, D. & Tambutté, S. Live tissue imaging shows reef corals elevate pH under their calcifying tissue relative to seawater. PLoS ONE 6, e20013 (2011).
ADS  CAS  Article  Google Scholar 

10.
Cai, W.-J. et al. Microelectrode characterization of coral daytime interior pH and carbonate chemistry. Nat. Commun. 7, 1–8 (2016).
Google Scholar 

11.
Holcomb, M. et al. Coral calcifying fluid pH dictates response to ocean acidification. Sci. Rep. 4, 5207. https://doi.org/10.1038/srep05207 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
DeCarlo, T. M., Holcomb, M. & McCulloch, M. T. Reviews and syntheses: revisiting the boron systematics of aragonite and their application to coral calcification. Biogeosciences 15, 2819–2834. https://doi.org/10.5194/bg-15-2819-2018 (2018).
ADS  CAS  Article  Google Scholar 

13.
McCulloch, M. T., D’Olivo, J. P., Falter, J., Holcomb, M. & Trotter, J. A. Coral calcification in a changing world and the interactive dynamics of pH and DIC upregulation. Nat. Commun. 8, 15686. https://doi.org/10.1038/ncomms15686 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Horvath, K. M. et al. Next-century ocean acidification and warming both reduce calcification rate, but only acidification alters skeletal morphology of reef-building coral Siderastrea siderea. Sci. Rep. 6, 29613. https://doi.org/10.1038/srep29613 (2016).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

15.
Tambutté, E. et al. Morphological plasticity of the coral skeleton under CO2-driven seawater acidification. Nat. Commun. 6, 7368. https://doi.org/10.1038/ncomms8368 (2015).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

16.
Stewart, J. A., Anagnostou, E. & Foster, G. L. An improved boron isotope pH proxy calibration for the deep-sea coral Desmophyllum dianthus through sub-sampling of fibrous aragonite. Chem. Geol. 447, 148–160. https://doi.org/10.1016/j.chemgeo.2016.10.029 (2016).
ADS  CAS  Article  Google Scholar 

17.
Allison, N., Finch, A. A. & EIMF. δ11B, Sr, Mg and B in a modern Porites coral: the relationship between calcification site pH and skeletal chemistry. Geochim. Cosmochim. Acta 74, 1790–1800 (2010).
ADS  CAS  Article  Google Scholar 

18.
Rollion-Bard, C. & Blamart, D. SIMS method and examples of applications in coral biomineralization. In Biomineralization Sourcebook: Characterization of Biominerals and Biomimetic Materials (eds DiMasi, E. & Gower, L. B.) 249–261 (CRC Press, Boca Raton, 2014).
Google Scholar 

19.
Trotter, J. et al. Quantifying the pH ‘vital effect’ in the temperate zooxanthellate coral Cladocora caespitosa: validation of the boron seawater pH proxy. Earth Planet. Sci. Lett. 303, 163–173. https://doi.org/10.1016/j.epsl.2011.01.030 (2011).
ADS  CAS  Article  Google Scholar 

20.
Krief, S. et al. Physiological and isotopic responses of scleractinian corals to ocean acidification. Geochim. Cosmochim. Acta 74, 4988–5001 (2010).
ADS  CAS  Article  Google Scholar 

21.
Hönisch, B. et al. Assessing scleractinian corals as recorders for paleo-pH: empirical calibration and vital effects. Geochim. Cosmochim. Acta 68, 3675–3685. https://doi.org/10.1016/j.gca.2004.03.002 (2004).
ADS  CAS  Article  Google Scholar 

22.
Tanaka, K. et al. Response of Acropora digitifera to ocean acidification: constraints from δ11B, Sr, Mg, and Ba compositions of aragonitic skeletons cultured under variable seawater pH. Coral Reefs 34, 1139–1149 (2015).
ADS  Article  Google Scholar 

23.
Reynaud, S., Hemming, N. G., Juillet-Leclerc, A. & Gattuso, J.-P. Effect of pCO2 and temperature on the boron isotopic composition of the zooxanthellate coral Acropora sp. Coral Reefs 23, 539–546 (2004).
Google Scholar 

24.
Anagnostou, E., Huang, K.-F., You, C.-F., Sikes, E. & Sherrell, R. Evaluation of boron isotope ratio as a pH proxy in the deep sea coral Desmophyllum dianthus: evidence of physiological pH adjustment. Earth Planet. Sci. Lett. 349, 251–260 (2012).
ADS  Article  Google Scholar 

25.
Jurikova, H. et al. Boron isotope composition of the cold-water coral Lophelia pertusa along the Norwegian margin: zooming into a potential pH-proxy by combining bulk and high-resolution approaches. Chem. Geol. 513, 143–152. https://doi.org/10.1016/j.chemgeo.2019.01.005 (2019).
ADS  CAS  Article  Google Scholar 

26.
Kasemann, S. A., Schmidt, D. N., Bijma, J. & Foster, G. L. In situ boron isotope analysis in marine carbonates and its application for foraminifera and palaeo-pH. Chem. Geol. https://doi.org/10.1016/j.chemgeo.2008.12.015 (2009).
Article  Google Scholar 

27.
Rollion-Bard, C., Chaussidon, M. & France-Lanord, C. pH control on oxygen isotopic composition of symbiotic corals. Earth Planet. Sci. Lett. 215, 275–288. https://doi.org/10.1016/S0012-821X(03)00391-1 (2003).
ADS  CAS  Article  Google Scholar 

28.
Standish, C. D. et al. The effect of matrix interferences on in situ boron isotope analysis by laser ablation multi-collector inductively coupled plasma mass spectrometry. Rapid Commun. Mass Spectrom. 33, 959–968 (2019).
ADS  CAS  Article  Google Scholar 

29.
Sadekov, A. et al. Accurate and precise microscale measurements of boron isotope ratios in calcium carbonates using laser ablation multicollector-ICPMS. J. Anal. At. Spectrom. 34, 550–560 (2019).
CAS  Article  Google Scholar 

30.
Fietzke, J. et al. Boron isotope ratio determination in carbonates via LA-MC-ICP-MS using soda-lime glass standards as reference material. J. Anal. At. Spectrom. 25, 1953–1957 (2010).
CAS  Article  Google Scholar 

31.
Oppelt, A., López, M. & Rocha, C. Biogeochemical analysis of the calcification patterns of cold-water corals Madrepora oculata and Lophelia pertusa along contact surfaces with calcified tubes of the symbiotic polychaete Eunice norvegica: evaluation of a ‘mucus’ calcification hypothesis. Deep Sea Res. I Oceanogr. Res. Pap. 127, 90–104. https://doi.org/10.1016/j.dsr.2017.08.006 (2017).
ADS  CAS  Article  Google Scholar 

32.
Fowell, S. et al. Historical trends in pH and carbonate biogeochemistry on the Belize Mesoamerican Barrier Reef System. Geophys. Res. Lett. 45, 3228–3237 (2018).
ADS  CAS  Article  Google Scholar 

33.
Runcorn, S. K. Corals as paleontological clocks. Sci. Am. 215, 26–33 (1966).
Article  Google Scholar 

34.
DeCarlo, T. M. & Cohen, A. L. Dissepiments, density bands and signatures of thermal stress in Porites skeletons. Coral Reefs 36, 749–761. https://doi.org/10.1007/s00338-017-1566-9 (2017).
ADS  Article  Google Scholar 

35.
Barnes, D. & Lough, J. On the nature and causes of density banding in massive coral skeletons. J. Exp. Mar. Biol. Ecol. 167, 91–108 (1993).
Article  Google Scholar 

36.
DeCarlo, T. M. et al. Coral Sr-U thermometry. Paleoceanography 31, 626–638 (2016).
ADS  Article  Google Scholar 

37.
Gagnon, A. C., Adkins, J. F., Fernandez, D. P. & Robinson, L. F. Sr/Ca and Mg/Ca vital effects correlated with skeletal architecture in a scleractinian deep-sea coral and the role of Rayleigh fractionation. Earth Planet. Sci. Lett. 261, 280–295. https://doi.org/10.1016/j.epsl.2007.07.013 (2007).
ADS  CAS  Article  Google Scholar 

38.
Blamart, D. et al. Correlation of boron isotopic composition with ultrastructure in the deep-sea coral Lophelia pertusa: implications for biomineralization and paleo-pH. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2007GC001686 (2007).
Article  Google Scholar 

39.
Jokiel, P. L. Coral reef calcification: carbonate, bicarbonate and proton flux under conditions of increasing ocean acidification. Proc. Biol. Sci. 280, 20130031–20130031. https://doi.org/10.1098/rspb.2013.0031 (2013).
CAS  Article  PubMed  PubMed Central  Google Scholar 

40.
Galli, G. & Solidoro, C. ATP supply may contribute to light-enhanced calcification in corals more than abiotic mechanisms. Front. Mar. Sci. https://doi.org/10.3389/fmars.2018.00068 (2018).
Article  Google Scholar 

41.
Barott, K. L., Venn, A. A., Perez, S. O., Tambutté, S. & Tresguerres, M. Coral host cells acidify symbiotic algal microenvironment to promote photosynthesis. Proc. Natl. Acad. Sci. 112, 607–612. https://doi.org/10.1073/pnas.1413483112 (2015).
ADS  CAS  Article  PubMed  Google Scholar 

42.
Bernardet, C., Tambutté, E., Techer, N., Tambutté, S. & Venn, A. Ion transporter gene expression is linked to the thermal sensitivity of calcification in the reef coral Stylophora pistillata. Sci. Rep. 9, 1–13 (2019).
Article  Google Scholar 

43.
Le Goff, C. et al. In vivo pH measurement at the site of calcification in an octocoral. Sci. Rep. 7, 11210. https://doi.org/10.1038/s41598-017-10348-4 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

44.
Zoccola, D. et al. Bicarbonate transporters in corals point towards a key step in the evolution of cnidarian calcification. Sci. Rep. 5, 9983. https://doi.org/10.1038/srep09983 (2015).
CAS  Article  PubMed  PubMed Central  Google Scholar 

45.
Furla, P., Galgani, I., Durand, I. & Allemand, D. Sources and mechanisms of inorganic carbon transport for coral calcification and photosynthesis. J. Exp. Biol. 203, 3445–3457 (2000).
CAS  PubMed  Google Scholar 

46.
DeLong, K. L., Maupin, C. R., Flannery, J. A., Quinn, T. M. & Shen, C.-C. Refining temperature reconstructions with the Atlantic coral Siderastrea siderea. Palaeogeogr. Palaeoclimatol. Palaeoecol. 462, 1–15. https://doi.org/10.1016/j.palaeo.2016.08.028 (2016).
Article  Google Scholar 

47.
Castillo, K. D., Ries, J. B. & Weiss, J. M. Declining coral skeletal extension for forereef colonies of Siderastrea siderea on the Mesoamerican Barrier Reef System Southern Belize. PLoS ONE 6, e14615 (2011).
ADS  CAS  Article  Google Scholar 

48.
Castillo, K. D., Ries, J. B., Weiss, J. M. & Lima, F. P. Decline of forereef corals in response to recent warming linked to history of thermal exposure. Nat. Clim. Change 2, 756–760. https://doi.org/10.1038/nclimate1577 (2012).
Article  Google Scholar 

49.
Foster, G. L. Seawater pH, pCO2 and CO32− variations in the Caribbean Sea over the last 130 kyr: a boron isotope and B/Ca study of planktic forminifera. Earth Planet. Sci. Lett. 271, 254–266. https://doi.org/10.1016/j.epsl.2008.04.015 (2008).
ADS  CAS  Article  Google Scholar 

50.
Foster, G. L. et al. Interlaboratory comparison of boron isotope analyses of boric acid, seawater and marine CaCO3 by MC-ICPMS and NTIMS. Chem. Geol. 358, 1–14. https://doi.org/10.1016/j.chemgeo.2013.08.027 (2013).
ADS  CAS  Article  Google Scholar 

51.
le Roux P. J. et al. In situ, multiplemultiplier, laser ablation ICP‐MS measurement of boron isotopic composition (δ11B) at the nanogram level. Chem. Geol. 203(1–2), 123–138. https://doi.org/10.1016/j.chemgeo.2003.09.006 (2004).
ADS  CAS  Article  Google Scholar 

52.
Inoue, M., Nohara, M., Okai, T., Suzuki, A. & Kawahata, H. Concentrations of trace elements in carbonate reference materials coral JCp-1 and Giant Clam JCt-1 by inductively coupled plasma-mass spectrometry. Geostand. Geoanal. Res. 28, 411–416. https://doi.org/10.1111/j.1751-908X.2004.tb00759.x (2004).
CAS  Article  Google Scholar 

53.
Thil, F. et al. Development of laser ablation multi-collector inductively coupled plasma mass spectrometry for boron isotopic measurement in marine biocarbonates: new improvements and application to a modern Porites coral. Rapid Commun. Mass Spectrom. 30, 359–371 (2016).
CAS  Article  Google Scholar 

54.
Hathorne, E. C. et al. Interlaboratory study for coral Sr/Ca and other element/Ca ratio measurements. Geochem. Geophys. Geosyst. 14, 3730–3750. https://doi.org/10.1002/ggge.20230 (2013).
ADS  CAS  Article  Google Scholar 

55.
Hijmans, R. & Van Etten, J. Geographic analysis and modeling with raster data. R Package Version 2, 1–25 (2012).
Google Scholar 

56.
R: A language and environment for statistical computing (R Foundation for Statistical Computing, Vienna, Austria, 2010).

57.
Foster, G. L., von Strandmann, P. & Rae, J. W. B. Boron and magnesium isotopic composition of seawater. Geochem. Geophys. Geosyst. https://doi.org/10.1029/2010gc003201 (2010).
Article  Google Scholar 

58.
Klochko, K., Kaufman, A. J., Yao, W. S., Byrne, R. H. & Tossell, J. A. Experimental measurement of boron isotope fractionation in seawater. Earth Planet. Sci. Lett. https://doi.org/10.1016/j.epsl.2006.05.034 (2006).
Article  Google Scholar 

59.
Holcomb, M., DeCarlo, T., Gaetani, G. & McCulloch, M. Factors affecting B/Ca ratios in synthetic aragonite. Chem. Geol. 437, 67–76 (2016).
ADS  CAS  Article  Google Scholar 

60.
Dickson, A. G. Thermodynamics of the dissociation of boric acid in synthetic seawater from 273.15 to 318.15 K. Deep Sea Res. Oceanogr. Res. Pap. 37, 755–766. https://doi.org/10.1016/0198-0149(90)90004-F (1990).
ADS  CAS  Article  Google Scholar 

61.
Lee, K. et al. The universal ratio of boron to chlorinity for the North Pacific and North Atlantic oceans. Geochim. Cosmochim. Acta 74, 1801–1811 (2010).
ADS  CAS  Article  Google Scholar 

62.
Zeebe, R. E. & Wolf-Gladrow, D. A. CO2in Seawater: Equilibrium, Kinetics, Isotopes in Seawater: Equilibrium, Kinetics, Isotopes Vol. 65 (Elsevier, Amsterdam, 2001).
Google Scholar 

63.
Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9, 671–675. https://doi.org/10.1038/nmeth.2089 (2012).
CAS  Article  PubMed  PubMed Central  Google Scholar  More

1.
Knutson, T. R. et al. Tropical cyclones and climate change. Nat. Geosci. 3(3), 157–163 (2010).
ADS  CAS  Article  Google Scholar 
2.
Lin, N. & Emanuel, K. Grey swan tropical cyclones. Nat. Clim. Change 6, 106–111 (2016).
ADS  Article  Google Scholar 

3.
de Vet, P. L. M. et al. Variations in storm-induced bed level dynamics across intertidal flats. Sci. Rep. 10, 12877 (2020).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Harris, L., Nel, R., Smale, M. & Schoeman, D. Swashed away? storm impacts on sandy beach macrofaunal communities. Estuar. Coast. Shelf Sci. 94(3), 210–221 (2011).
ADS  Article  Google Scholar 

5.
Machado, P. M., Costa, L. L., Suciu, M. C., Tavares, D. C. & Zalmon, I. R. Extreme storm wave influence on sandy beach macrofauna with distinct human pressures. Mar. Pollut. Bull. 107(1), 125–135 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Costa, L. L., Machado, P. M. & Zalmon, I. R. Do natural disturbances have significant effects on sandy beach macrofauna of Southeastern Brazil?. Zoologia (Curitiba) 36(1), e29814 (2019).
Google Scholar 

7.
Ghorai, D. & Sen, H. S. Role of climate change in increasing occurrences oceanic hazards as a potential threat to coastal ecology. Nat Hazards 75, 1223–1245 (2015).
Article  Google Scholar 

8.
Posey, M., Lindberg, W., Alphin, T. & Vose, F. Influence of storm disturbance on an offshore benthic community. Bull. Mar. Sci. 59(3), 523–529 (1996).
Google Scholar 

9.
Saloman, C. H. & Naughton, S. P. Effect of Hurricane Eloise on the benthic fauna of Panama City Beach, Florida, USA. Mar. Biol. 42(4), 357–363 (1977).
Article  Google Scholar 

10.
Abe, H. et al. Impact of the 2011 tsunami on the Manila clam Ruditapes philippinarum population and subsequent population recovery in Matsukawaura Lagoon, Fukushima, northeastern Japan. Region. Stud. Mar. Sci. 9, 97–105 (2017).
Article  Google Scholar 

11.
Dreyer, J., Bailey-Brock, J. H. & McCarthy, S. A. The immediate effects of Hurricane Iniki on intertidal fauna on the south shore of O ‘ahu. Mar. Environ. Res. 59(4), 367–380 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Izumi, S. & Masabumi, S. Behavioral characteristics of the juvenile Japanese surf clam Pseudocardium sachalinensis in response to sand erosion and deposition associated with oscillatory water flow. Fish. Sci. 64(3), 367–372 (1998).
Article  Google Scholar 

13.
Bricheno, L. M., Wolf, J. & Aldridge, J. Distribution of natural disturbance due to wave and tidal bed currents around the UK. Cont. Shelf Res. 109, 67–77 (2015).
ADS  Article  Google Scholar 

14.
Browning, T. N. et al. Widespread deposition in a coastal bay following three major 2017 hurricanes (Irma, Jose, and Maria). Sci. Rep. 9(1), 7101 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

15.
Hagerman, G. & Rieger, R. Dispersal of benthic Meiofauna by wave and current action in Bogue sound, North Carolina, USA. Mar. Ecol. 2(3), 245–270 (1981).
ADS  Article  Google Scholar 

16.
Corte, G. N. et al. Storm effects on intertidal invertebrates: Increased beta diversity of few individuals and species. PeerJ 5, e3360 (2017).
PubMed  PubMed Central  Article  Google Scholar 

17.
Murphy, A. E. et al. Quantifying the effects of commercial clam aquaculture on c and n cycling: An integrated ecosystem approach. Estuar. Coasts 39(6), 1–16 (2016).
Article  CAS  Google Scholar 

18.
Turra, A. et al. Population biology and secondary production of the harvested clam Tivela Mactroides (Born, 1778) (Bivalvia, Veneridae) in Southeastern Brazil. Mar. Ecol. 36, 2 (2015).
Article  Google Scholar 

19.
Thomas, S. et al. Does the size structure of venerid clam populations affect ecosystem functions on intertidal sandflats?. Estuar. Coasts 20, 20 (2020).
Google Scholar 

20.
Wong, W. H., Rabalais, N. N. & Turner, R. E. Abundance and ecological significance of the clam Rangia Cuneata (Sowerby, 1831) in the upper Barataria Estuary (Louisiana, USA). Hydrobiologia 651(1), 305–315 (2010).
CAS  Article  Google Scholar 

21.
Adkins, S. C., Marsden, I. D. & Pirker, J. G. Reproduction, growth and size of a burrowing intertidal clam exposed to varying environmental conditions in estuaries. Inverteb. Reprod. Dev. 60(3), 223–237 (2016).
CAS  Article  Google Scholar 

22.
Clements, J. C. & Hunt, H. L. Effects of CO2-driven sediment acidification on infaunal marine bivalves: A synthesis. Mar. Pollut. Bull. 117(1–2), 6–16 (2017).
CAS  PubMed  Article  Google Scholar 

23.
Clements, J. C., Woodard, K. D. & Hunt, H. L. Porewater acidification alters the burrowing behavior and post-settlement dispersal of juvenile soft-shell clams (Mya arenaria). J. Exp. Mar. Biol. Ecol. 477(Apr.), 103–111 (2016).
Article  Google Scholar 

24.
Ocaña, F. A., Pech, D., Simões, N. & Hernández-Ávila, I. Spatial assessment of the vulnerability of benthic communities to multiple stressors in the Yucatan Continental Shelf, Gulf of Mexico. Ocean Coast. Manag. 181, 104900 (2019).
Article  Google Scholar 

25.
Ortega, L., Celentano, E., Delgado, E. & Defeo, O. Climate change influences on abundance, individual size and body abnormalities in a sandy beach clam. Mar. Ecol. Progress Ser. 20, 545 (2016).
Google Scholar 

26.
Hinchey, E. K., Schaffner, L. C., Hoar, C. C., Vogt, B. W. & Batte, L. P. Responses of estuarine benthic invertebrates to sediment burial: The importance of mobility and adaptation. Hydrobiologia 556(1), 85–98 (2006).
Article  Google Scholar 

27.
Redjah, I. et al. The importance of turbulent kinetic energy on transport of juvenile clams (Mya arenaria). Aquaculture 307(1–2), 20–28 (2010).
Article  Google Scholar 

28.
Forêt, M., Tremblay, R., Neumeier, U. & Olivier, F. Temporal variation of secondary migrations potential: Concept of temporal windows in four commercial bivalve species. Aquat. Liv. Resour. 31, 19 (2018).
Article  Google Scholar 

29.
Hunt, H. L. & Chant, F. R. J. Modeling bedload transport of juvenile bivalves: Predicted changes in distribution and scale of postlarval dispersal. Estuar. Coasts 32(6), 1090–1102 (2009).
Article  Google Scholar 

30.
Carolyn, J. L., Conrad, A. P. & Vonda, J. C. Behaviour controls post-settlement dispersal by the juvenile bivalves Austrovenus stutchburyi and Macomona Liliana. J. Exp. Mar. Biol. Ecol. 306, 51–74 (2004).
Article  Google Scholar 

31.
St-Onge, P., Miron, G. & Moreau, G. Burrowing behaviour of the softshell clam (Mya arenaria) following erosion and transport. J. Exp. Mar. Biol. Ecol. 340(1), 103–111 (2007).
Article  Google Scholar 

32.
Bolam, S. G. Burial survival of benthic macrofauna following deposition of simulated dredged material. Environ. Monit. Assess. 181(1–4), 13–27 (2011).
PubMed  Article  PubMed Central  Google Scholar 

33.
Fiori, S. M. & Carcedo, M. C. Influence of grain size on burrowing and alongshore distribution of the yellow clam (Amarilladesma mactroides). J. Shellf. Res. 34(3), 785–789 (2015).
Article  Google Scholar 

34.
Lewis, N. S., Fox, E. W. & Dewitt, T. H. Estimating the distribution of harvested estuarine bivalves with natural-history-based habitat suitability models. Estuar. Coast. Shelf Sci. 219(Apr. 5), 453–472 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 

35.
Lundquist, C. J. et al. Spatial variability in recolonisation potential: Influence of organism behaviour and hydrodynamics on the distribution of macrofaunal colonists. Mar. Ecol. Prog. Ser. 324, 67–81 (2006).
ADS  Article  Google Scholar 

36.
Hunt, H. L. Transport of juvenile clams: Effects of species and sediment grain size. J. Exp. Mar. Biol. Ecol. 312(2), 271–284 (2004).
Article  Google Scholar 

37.
Lundquist, C. J., Pilditch, C. A. & Cummings, V. J. Behaviour controls post-settlement dispersal by the juvenile bivalves Austrovenus stutchburyi and Macomona liliana. J. Exp. Mar. Biol. Ecol. 306(1), 51–74 (2004).
Article  Google Scholar 

38.
Sakurai, I., Nakajima, K. & Yamashita, T. Effect of oscillatory water flow on burrowing behaviors of the Japanese surf clam Pseudocardium sachalinensis. Nippon Suisan Gakkaishi 64(3), 406–411 (1998).
Article  Google Scholar 

39.
Alejandro, A., Doris, O. & Pedro, T. Effect of transfer time, temperature, and size on burrowing capacity of juvenile clams, Mulinia edulis, from hatchery. World Aquacult. Soc. 50(4), 1–15 (2018).
Google Scholar 

40.
Zaklan, S. D. & Ydenberg, R. The body size–burial depth relationship in the infaunal clam Mya arenaria. J. Exp. Mar. Biol. Ecol. 215(1), 1–17 (1997).
Article  Google Scholar 

41.
Zwarts, L. & Wanink, J. Siphon size and burying depth in deposit-and suspension-feeding benthic bivalves. Mar. Biol. 100(2), 227–240 (1989).
Article  Google Scholar 

42.
Abarca, A., Oliva, D. & Toledo, P. Effect of transfer time, temperature, and size on burrowing capacity of juvenile clams, Mulinia edulis, from hatchery. J. World Aquacult. Soc. 50(4), 774–788 (2019).
Article  Google Scholar 

43.
Nunez, J. D., Laitano, M. V., Meretta, P. E. & Ocampo, E. H. Burrowing behavior of an infaunal clam species after siphon nipping. J. Exp. Mar. Biol. Ecol. 459(Oct. 4), 45–50 (2014).
Article  Google Scholar 

44.
Maurer, D., Keck, R. T., Tinsman, J. C. & Leathem, W. A. Vertical migration and mortality of benthos in dredged material—part I: Mollusca. Mar. Environ. Res. 4(4), 299–319 (1981).
Article  Google Scholar 

45.
Sakurai, I. & Seto, M. Behavioral characteristics of the juvenile Japanese surf clam Pseudocardium sachalinensis in response to sand erosion and deposition associated with oscillatory water flow. Fish. Sci. 64(3), 367–372 (1998).
CAS  Article  Google Scholar 

46.
Hutchison, Z. L., Hendrick, V. J., Burrows, M. T., Wilson, B. & Last, K. S. Buried alive: The behavioural response of the mussels, modiolus modiolus and mytilus edulis to sudden burial by sediment. PLoS One 11(3), e0151471 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Ma, D., Shi, W. & Yu, J. Burial effects of Tianjin nangang industrial zone dredging Materialon Macrobenthos. J. Zhejiang Ocean Univ. 20, 20 (2015) ((in Chinese)).
Google Scholar 

48.
Quinn, N., Atkinson, P. & Wells, N. Modelling of tide and surge elevations in the Solent and surrounding waters: The importance of tide–surge interactions. Estuar. Coast. Shelf Sci. 112(112), 162–172 (2012).
ADS  Article  Google Scholar 

49.
Houser and Chris. Alongshore variation in the morphology of coastal dunes: Implications for storm response. Geomorphology 199, 48–61 (2013).
ADS  Article  Google Scholar  More

1.
Stearns, S. C. The Evolution of Life Histories (Oxford University Press, Oxford, 1996).
Google Scholar 
2.
Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton University Press, Princeton, 2002).
Google Scholar 

3.
Kaspari, M. & Powers, J. S. Biogeochemistry and geographical ecology: Embracing all twenty-five elements required to build organisms. Am. Nat. 188, S62–S73 (2016).
PubMed  Article  PubMed Central  Google Scholar 

4.
Kozlowski, J. Why life histories are diverse. Polish J. Ecol. 54, 585–605 (2006).
Google Scholar 

5.
Ejsmond, M. J., Varpe, Ø., Czarnoleski, M. & Kozłowski, J. Seasonality in offspring value and trade-offs with growth explain capital breeding. Am. Nat. 186, E111–E125 (2015).
Article  Google Scholar 

6.
Filipiak, M. A better understanding of bee nutritional ecology is needed to optimize conservation strategies for wild bees-the application of ecological stoichiometry. Insects 9, 85 (2018).
PubMed Central  Article  Google Scholar 

7.
Filipiak, Z. M. & Filipiak, M. The scarcity of specific nutrients in wild bee larval food negatively influences certain life history traits. Biology (Basel). 9, 462 (2020).

8.
Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity (Princeton University Press, Princeton, 2012).
Google Scholar 

9.
Bärlocher, F. & Rennenberg, H. Food chains and nutrient cycles. In Ecological biochemistry (eds Krauss, G. J. & Nies, D. H.) 92–122 (Wiley, New York, 2014).
Google Scholar 

10.
DeAngelis, D. L. Dynamics of Nutrient Cycling and Food Webs (Springer Netherlands, Amsterdam, 1992).
Google Scholar 

11.
Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry (Academic Press, London, 2020).
Google Scholar 

12.
Jeyasingh, P. D., Cothran, R. D. & Tobler, M. Testing the ecological consequences of evolutionary change using elements. Ecol. Evol. 4, 528–538 (2014).
PubMed  PubMed Central  Article  Google Scholar 

13.
Jeyasingh, P. D., Goos, J. M., Thompson, S. K., Godwin, C. M. & Cotner, J. B. Ecological stoichiometry beyond redfield: An ionomic perspective on elemental homeostasis. Front. Microbiol. 8, 722 (2017).
PubMed  PubMed Central  Article  Google Scholar 

14.
González, A. L. et al. Ecological mechanisms and phylogeny shape invertebrate stoichiometry: A test using detritus-based communities across Central and South America. Funct. Ecol. 32, 2448–2463 (2018).
Article  Google Scholar 

15.
Peñuelas, J. et al. The bioelements, the elementome, and the biogeochemical niche. Ecology 100, e02652 (2019).
PubMed  Article  PubMed Central  Google Scholar 

16.
Fagan, W. F. & Denno, R. F. Stoichiometry of actual vs. potential predator-prey interactions: Insights into nitrogen limitation for arthropod predators. Ecol. Lett. 7, 876–883 (2004).
Article  Google Scholar 

17.
Kay, A. D. et al. Toward a stoichiometric framework for evolutionary biology. Oikos 109, 6–17 (2005).
Article  Google Scholar 

18.
Cherif, M. et al. An operational framework for the advancement of a molecule-to-biosphere stoichiometry theory. Front. Mar. Sci. 4, 1–16 (2017).
ADS  Article  Google Scholar 

19.
Welti, N. et al. Bridging food webs, ecosystem metabolism, and biogeochemistry using ecological stoichiometry theory. Front. Microbiol. 8, 1298 (2017).
PubMed  PubMed Central  Article  Google Scholar 

20.
Hessen, D. O., Elser, J. J., Sterner, R. W. & Urabe, J. Ecological stoichiometry: An elementary approach using basic principles. Limnol. Oceanogr. 58, 2219–2236 (2013).
ADS  CAS  Article  Google Scholar 

21.
Lemoine, N. P., Giery, S. T. & Burkepile, D. E. Differing nutritional constraints of consumers across ecosystems. Oecologia 174, 1367–1376 (2014).
ADS  PubMed  Article  PubMed Central  Google Scholar 

22.
Morehouse, N. I., Nakazawa, T., Booher, C. M., Jeyasingh, P. D. & Hall, M. D. Sex in a material world: Why the study of sexual reproduction and sex-specific traits should become more nutritionally-explicit. Oikos 119, 766–778 (2010).
Article  Google Scholar 

23.
Filipiak, M. Key pollen host plants provide balanced diets for wild bee larvae: A lesson for planting flower strips and hedgerows. J. Appl. Ecol. 56, 1410–1418 (2019).
CAS  Article  Google Scholar 

24.
Goos, J. M., Cothran, R. D. & Jeyasingh, P. D. Within-population variation in the chemistry of life: The stoichiometry of sexual dimorphism in multiple dimensions. Evol. Ecol. 31, 635–651 (2017).
Article  Google Scholar 

25.
Halvorson, H. M., Scott, J. T., Sanders, A. J. & Evans-White, M. A. A stream insect detritivore violates common assumptions of threshold elemental ratio bioenergetics models. Freshw. Sci. 34, 508–518 (2015).
Article  Google Scholar 

26.
Meunier, C. L. et al. From elements to function: Toward unifying ecological stoichiometry and trait-based ecology. Front. Environ. Sci. 5, 1–10 (2017).
Article  Google Scholar 

27.
Sperfeld, E., Wagner, N. D., Halvorson, H. M., Malishev, M. & Raubenheimer, D. Bridging ecological stoichiometry and nutritional geometry with homeostasis concepts and integrative models of organism nutrition. Funct. Ecol. 31, 286–296 (2017).
Article  Google Scholar 

28.
Filipiak, M. & Weiner, J. Plant–insect interactions: The role of ecological stoichiometry. Acta Agrobot. 70, 1–16 (2017).
Article  Google Scholar 

29.
Elser, J. J., Dobberfuhl, D. R., MacKay, N. A. & Schampel, J. H. Organism size, life history, and N: P stoichiometry: Toward a unified view of cellular and ecosystem processes. Bioscience 46, 674–684 (1996).
Article  Google Scholar 

30.
Polidori, C. et al. Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta). Org. Divers. Evol. https://doi.org/10.1007/s13127-020-00448-x (2020).
Article  Google Scholar 

31.
Bosch, J., Sgolastra, F. & Kemp, W. P. Life cycle ecophysiology of Osmia mason bees used as crop pollinators. In Bee Pollination in Agricultural Eco-systems (eds James, R. & Pitts-Singer, T. L.) 83–105 (Oxford Scholarship Online, Oxford, 2008).
Google Scholar 

32.
Giejdasz, K. & Wilkaniec, Z. Individual development of the red mason bee (Osmia rufa L., Megachilidae) under natural and laboratory conditions. J. Apic. Sci. 46, 51–57 (2002).
Google Scholar 

33.
Gruber, B., Eckel, K., Everaars, J. & Dormann, C. F. On managing the red mason bee (Osmia bicornis) in apple orchards. Apidologie 42, 564–576 (2011).
Article  Google Scholar 

34.
Kaspari, M. The seventh macronutrient: How sodium shortfall ramifies through populations, food webs and ecosystems. Ecol. Lett. 23, 1153–1168 (2020).
PubMed  Article  PubMed Central  Google Scholar 

35.
Rizzuto, M. et al. Patterns and potential drivers of intraspecific variability in the body C, N, and P composition of a terrestrial consumer, the snowshoe hare (Lepus americanus). Ecol. Evol. 9, 14453–14464 (2019).
PubMed  PubMed Central  Article  Google Scholar 

36.
Sitters, J. & Olde Venterink, H. The need for a novel integrative theory on feedbacks between herbivores, plants and soil nutrient cycling. Plant Soil 396, 421–426 (2015).
CAS  Article  Google Scholar 

37.
Sitters, J. et al. Nutrient availability controls the impact of mammalian herbivores on soil carbon and nitrogen pools in grasslands. Glob. Change Biol. 26, 2060–2071 (2020).
ADS  Article  Google Scholar 

38.
Sitters, J. et al. The stoichiometry of nutrient release by terrestrial herbivores and its ecosystem consequences. Front. Earth Sci. 5, 1–8 (2017).
Article  Google Scholar 

39.
González, A. L., Fariña, J. M., Kay, A. D., Pinto, R. & Marquet, P. A. Exploring patterns and mechanisms of interspecific and intraspecific variation in body elemental composition of desert consumers. Oikos 120, 1247–1255 (2011).
Article  Google Scholar 

40.
Seidelmann, K. Optimal progeny body size in a solitary bee, Osmia bicornis (Apoidea: Megachilidae). Ecol. Entomol. 39, 656–663 (2014).
Article  Google Scholar 

41.
Kim, J. Y. Female size and fitness in the leaf-cutter bee Megachile apicalis. Ecol. Entomol. 22, 275–282 (1997).
Article  Google Scholar 

42.
Markow, T. et al. Elemental stoichiometry of Drosophila and their hosts. Funct. Ecol. 13, 78–84 (1999).
Article  Google Scholar 

43.
Bergwitz, C. & Jüppner, H. Phosphate sensing. Adv. Chronic Kidney Dis. 18, 132–144 (2011).
PubMed  PubMed Central  Article  Google Scholar 

44.
Werner, A. & Kinne, R. K. H. Evolution of the Na-Pi cotransport systems. Am. J. Physiol. Regul. Integr. Comp. Physiol. 280, R301–R312 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Morgan, A. J., Kille, P. & Stürzenbaum, S. R. Microevolution and ecotoxicology of metals in invertebrates. Environ. Sci. Technol. 41, 1085–1096 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Bednarska, A. J., Świątek, Z. M. & Labecka, A. M. Effects of cadmium bioavailability in food on its distribution in different tissues in the ground beetle Pterostichus oblongopunctatus. Bull. Environ. Contam. Toxicol. 103, 421–427 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Świątek, Z. M. & Bednarska, A. J. Energy reserves and respiration rate in the earthworm Eisenia andrei after exposure to zinc in nanoparticle or ionic form. Environ. Sci. Pollut. Res. Int. 26, 24933–24945 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

48.
Cohen, A. C. Insect Diets: Science and Technology (CRC Press, Boca Raton, 2005).
Google Scholar 

49.
Seidelmann, K. Optimal resource allocation, maternal investment, and body size in a solitary bee, Osmia bicornis. Entomol. Exp. Appl. 166, 790–799 (2018).
Article  Google Scholar 

50.
Bosch, J. & Vicens, N. Relationship between body size, provisioning rate, longevity and reproductive success in females of the solitary bee Osmia cornuta. Behav. Ecol. Sociobiol. 60, 26–33 (2006).
Article  Google Scholar 

51.
Seidelmann, K., Ulbrich, K. & Mielenz, N. Conditional sex allocation in the Red Mason bee, Osmia rufa. Behav. Ecol. Sociobiol. 64, 337–347 (2010).
Article  Google Scholar 

52.
González, A. L., Dézerald, O., Marquet, P. A., Romero, G. Q. & Srivastava, D. S. The multidimensional stoichiometric niche. Front. Ecol. Evol. 5, 110 (2017).
Article  Google Scholar 

53.
Lemmen, K. D., Butler, O. M., Koffel, T., Rudman, S. M. & Symons, C. C. Stoichiometric traits vary widely within species: A meta-analysis of common garden experiments. Front. Ecol. Evol. 7, 1–15 (2019).
Article  Google Scholar 

54.
Prater, C., Wagner, N. D. & Frost, P. C. Interactive effects of genotype and food quality on consumer growth rate and elemental content. Ecology 98, 1399–1408 (2017).
PubMed  Article  PubMed Central  Google Scholar 

55.
Sherman, R. E., Chowdhury, P. R., Baker, K. D., Weider, L. J. & Jeyasingh, P. D. Genotype-specific relationships among phosphorus use, growth and abundance in Daphnia pulicaria. R. Soc. Open Sci. 4, 170770 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Zajitschek, F. & Connallon, T. Partitioning of resources: The evolutionary genetics of sexual conflict over resource acquisition and allocation. J. Evol. Biol. 30, 826–838 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

57.
Moe, S. J. et al. Recent advances in ecological stoichiometry: Insights for population and community ecology. Oikos 109, 29–39 (2005).
Article  Google Scholar 

58.
Peñuelas, J., Sardans, J., Ogaya, R. & Estiarte, M. Nutrient stoichiometric relations and biogeochemical niche in coexisting plant species: Effect of simulated climate change. Polish J. Ecol. 56, 613–622 (2008).
Google Scholar 

59.
Urbina, I. et al. Plant community composition affects the species biogeochemical niche. Ecosphere 8, e01801 (2017).
Article  Google Scholar 

60.
Jeyasingh, P. D., Goos, J. M., Lind, P. R., Roy Chowdhury, P. & Sherman, R. E. Phosphorus supply shifts the quotas of multiple elements in algae and Daphnia: Ionomic basis of stoichiometric constraints. Ecol. Lett. 23, 1064–1072 (2020).
PubMed  Article  PubMed Central  Google Scholar 

61.
Ruedenauer, F. A. et al. Best be (e) on low fat: Linking nutrient perception, regulation and fitness. Ecol. Lett. 23, 545–554 (2020).
PubMed  Article  PubMed Central  Google Scholar 

62.
Trinkl, M. et al. Floral species richness correlates with changes in the nutritional quality of larval diets in a stingless bee. Insects 11, E125 (2020).
PubMed  Article  PubMed Central  Google Scholar 

63.
Roswell, M., Dushoff, J. & Winfree, R. Male and female bees show large differences in floral preference. PLoS ONE 14, e0214909 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Vaudo, A. D. et al. Pollen protein: Lipid macronutrient ratios may guide broad patterns of bee species floral preferences. Insects 11, 132 (2020).
PubMed Central  Article  Google Scholar 

65.
Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
Google Scholar 

66.
Smilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data using CANOCO 5 (Cambridge University Press, Cambridge, 2014).
Google Scholar  More

1.
Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).
ADS  PubMed  PubMed Central  Article  Google Scholar 
2.
Peterson, M. L., Doak, D. F. & Morris, W. F. Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob. Change. Biol 25, 775–793 (2019).
ADS  Article  Google Scholar 

3.
Rodríguez-Rodríguez, E. J. et al. Niche models at inter- and intraspecific levels reveal hierarchical niche differentiation in midwife toads. Sci. Rep. 10, 10942 (2020).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

4.
Harvey, P. H. & Pagel, M. D. The Comparative Method in Evolutionary Biology Vol. 239 (Oxford University Press, Oxford, 1991).
Google Scholar 

5.
Banerjee, A. K., Mukherjee, A., Guo, W., Ng, W. L. & Huang, Y. Combining ecological niche modeling with genetic lineage information to predict potential distribution of Mikania micrantha Kunth in South and Southeast Asia under predicted climate change. Glob. Ecol. Conserv. 20, e00800 (2019).
Article  Google Scholar 

6.
Martínez-Freiría, F. et al. Climatic refugia boosted allopatric diversification in western Mediterranean vipers. J. Biogeogr. https://doi.org/10.1111/jbi.13861 (2020).
Article  Google Scholar 

7.
Groom, Q. J., Marsh, C. J., Gavish, Y. & Kunin, W. E. How to predict fine resolution occupancy from coarse occupancy data. Methods Ecol. Evol. 9, 2273–2284 (2018).
Article  Google Scholar 

8.
Li, Y. et al. Climate and topography explain range sizes of terrestrial vertebrates. Nat. Clim. Change 6, 498–502 (2016).
ADS  Article  Google Scholar 

9.
Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. Adapting the IUCN Red List criteria for invertebrates. Biol. Conserv. 144, 2432–2440 (2011).
Article  Google Scholar 

10.
Burbidge, A., Woinarski, J. & Harrison, P. The Action Plan for Australian Mammals 2012 (Csiro Publishing, Clayton, 2014).
Google Scholar 

11.
Jiménez-Alfaro, B., Draper, D. & Nogués-Bravo, D. Modeling the potential area of occupancy at fine resolution may reduce uncertainty in species range estimates. Biol. Conserv. 147, 190–196 (2012).
Article  Google Scholar 

12.
Kamino, L. H. Y., Siqueira, M., Sánchez-Tapia, A. & Stehmann, J. R. Reassessment of the extinction risk of endemic species in the Neotropics: how can modelling tools help us. Nat. Conserv. 10, 191–198 (2012).
Article  Google Scholar 

13.
Kluber, M. R., Olson, D. H. & Puettmann, K. J. Amphibian distributions in riparian and upslope areas and their habitat associations on managed forest landscapes in the Oregon Coast Range. For. Ecol. Manage 256, 529–535 (2008).
Article  Google Scholar 

14.
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Article  Google Scholar 

15.
Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
Article  Google Scholar 

16.
Steinfartz, S., Hwang, U. W., Tautz, D., Öz, M. & Veith, M. Molecular phylogeny of the salamandrid genus Neurergus: evidence for an intrageneric switch of reproductive biology. Amphib-Reptilia. 23, 419–431 (2002).
Article  Google Scholar 

17.
Goudarzi, F. et al. Geographic separation and genetic differentiation of populations are not coupled with niche differentiation in threatened Kaiser’s spotted newt (Neurergus kaiseri). Sci. Rep. 9, 6239 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

18.
IUCN SSC Amphibian Specialist Group. Neurergus kaiseri. The IUCN Red List of Threatened Species 2016: e.T59450A49436271. https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T59450A49436271.en. Downloaded on 29 November 2018.

19.
Vaissi, S. & Sharifi, M. Integrating multi-criteria decision analysis with a GIS-based siting procedure to select a protected area for the Kaiser’s mountain newt, Neurergus kaiseri (Caudata: Salamandridae). Glob. Ecol. Conserv. 20, e00738 (2019).
Article  Google Scholar 

20.
Rancilhac, L. et al. Phylogeny and species delimitation of Near Eastern Neurergus newts (Salamandridae) based on genome-wide RADseq data analysis. Mol. Phylogenet. Evol. 133, 189–197 (2019).
PubMed  Article  PubMed Central  Google Scholar 

21.
Pearman, P. B., D’Amen, M., Graham, C. H., Thuiller, W. & Zimmermann, N. E. Within-taxon niche structure: niche conservatism, divergence and predicted effects of climate change. Ecography 33, 990–1003 (2010).
Article  Google Scholar 

22.
Lecocq, T., Harpke, A., Rasmont, P. & Schweiger, O. Integrating intraspecific differentiation in species distribution models: Consequences on projections of current and future climatically suitable areas of species. Divers. Distrib. 25, 1088–1100 (2019).
Article  Google Scholar 

23.
Rodríguez-Rodríguez, E. J. et al. Climate change challenges IUCN conservation priorities: A test with western Mediterranean amphibians. SN Appl. Sci. 2, 216 (2020).
Article  Google Scholar 

24.
Joppa, L. N. et al. Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment. Conserv. Biol. 30, 362–370 (2016).
PubMed  Article  PubMed Central  Google Scholar 

25.
Denoël, M. & Ficetola, G. F. Landscape-level thresholds and newt conservation. Ecol. Appl. 17, 302–309 (2007).
PubMed  Article  PubMed Central  Google Scholar 

26.
Denoël, M. et al. A multi-scale approach to facultative paedomorphosis of European newts (Salamandridae) in the Montenegrin karst: distribution pattern, environmental variables, and conservation. Biol. Conserv. 142, 509–517 (2009).
Article  Google Scholar 

27.
Ildos, A. S. & Ancona, N. Analysis of amphibian habitat preferences in a farmland area (Po plain, northern Italy). Amphib-Reptilia. 15, 307–316 (1994).
Article  Google Scholar 

28.
Beebee, T. J. Discriminant analysis of amphibian habitat determinants in South-East England. Amphib-Reptilia. 6, 35–43 (1985).
Article  Google Scholar 

29.
Manzoor, S. A., Griffiths, G. & Lukac, M. Species distribution model transferability and model grain size—finer may not always be better. Sci. Rep. 8, 7168 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

30.
Chardon, N. I., Pironon, S., Peterson, M. L. & Doak, D. F. Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide-spread plant species. Ecography 43, 60–74 (2020).
Article  Google Scholar 

31.
Maguire, K. C., Shinneman, D. J., Potter, K. M. & Hipkins, V. D. Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change. Syst. Biol 67, 965–978 (2018).
PubMed  Article  PubMed Central  Google Scholar 

32.
Barria, A. M. et al. The importance of intraspecific variation for niche differentiation and species distribution models: The ecologically diverse frog pleurodema thaul as study case. Evol. Biol. 47, 206–219 (2020).
Article  Google Scholar 

33.
Austin, M. P. & Van Niel, K. P. Impact of landscape predictors on climate change modelling of species distributions: A case study with Eucalyptus fastigata in southern New South Wales, Australia. J. Biogeogr. 38, 9–19 (2011).
Article  Google Scholar 

34.
Fournier, A., Barbet-Massin, M., Rome, Q. & Courchamp, F. Predicting species distribution combining multi-scale drivers. Glob. Ecol. Conserv. 12, 215–226 (2017).
Article  Google Scholar 

35.
Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
Article  Google Scholar 

36.
Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).
Article  Google Scholar 

37.
Dinis, M. et al. Allopatric diversification and evolutionary melting pot in a North African Palearctic relict: the biogeographic history of Salamandra algira. Mol. Phylogenet. Evol. 130, 81–91 (2019).
PubMed  Article  PubMed Central  Google Scholar 

38.
Schulte, U. et al. Cryptic niche conservatism among evolutionary lineages of an invasive lizard. Glob. Ecol. Biogeogr 21, 198–211 (2012).
Article  Google Scholar 

39.
Breiner, F. T., Guisan, A., Nobis, M. P. & Bergamini, A. Including environmental niche information to improve IUCN Red List assessments. Divers. Distrib. 23, 484–495 (2017).
Article  Google Scholar 

40.
IUCN Standards and Petitions Committee. Guidelines for Using the IUCN Red List Categories and Criteria, ver. 14. The Standards and Petitions Committee. https://www.iucnredlist.org/documents/RedListGuidelines.pdf (accessed 22 March 2020). (2019).

41.
Hartley, S. & Kunin, W. E. Scale dependency of rarity, extinction risk, and conservation priority. Conserv. Biol. 17, 1559–1570 (2003).
Article  Google Scholar 

42.
Raeisi, E. & Stevanovic, Z. Groundwater Hydrology of Springs 498–515 (Elsevier, Amsterdam, 2010).
Google Scholar 

43.
Chen, J. et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7–27 (2015).
ADS  Article  Google Scholar 

44.
Sharifi, M., Farasat, H., Barani-Beiranvand, H., Vaissi, S. & Foroozanfar, E. Notes on the distribution and abundance of the endangered kaiser’s mountain newt, neurergus kaiseri (caudata: salamandridae), in southwestern Iran. Herpetol. Conserv. Biol 8, 724–731 (2013).
Google Scholar 

45.
Mobaraki, A. et al. A conservation reassessment of the Critically Endangered, Lorestan newt Neurergus kaiseri (Schmidt 1952) in Iran. Amphib. Reptile Conserv. 9, 16–25 (2014).
Google Scholar 

46.
Casula, P., Vignoli, L., Luiselli, L. & Lecis, R. Local abundance and observer’s identity affect visual detectability of Sardinian mountain newts. Herpetol. J. 27, 258–265 (2017).
Google Scholar 

47.
Joly, P., Morand, C. & Cohas, A. Habitat fragmentation and amphibian conservation: Building a tool for assessing landscape matrix connectivity. BC. R. Biol. 326, 132–139 (2003).
Google Scholar 

48.
Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?. Glob. Ecol. Biogeogr 12, 361–371 (2003).
Article  Google Scholar 

49.
Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species distribution modeling. R package version 1.0-12. The R Foundation for Statistical Computing, Vienna. http://cran.r-project.org (2015).

50.
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013).

51.
ESRI. Using ecological niche modeling. (2016).

52.
Blank, L. & Blaustein, L. Using ecological niche modeling to predict the distributions of two endangered amphibian species in aquatic breeding sites. Hydrobiologia 693, 157–167 (2012).
Article  Google Scholar 

53.
Bradie, J. & Leung, B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44, 1344–1361 (2017).
Article  Google Scholar 

54.
Cunningham, H. R., Rissler, L. J., Buckley, L. B. & Urban, M. C. Abiotic and biotic constraints across reptile and amphibian ranges. Ecography 39, 1–8 (2015).
Article  Google Scholar 

55.
Peterman, W. E. & Semlitsch, R. D. Fine-scale habitat associations of a terrestrial salamander: the role of environmental gradients and implications for population dynamics. PLoS ONE 8, e62184 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Vasconcelos, T. S., Rodríguez, M. Á. & Hawkins, B. A. Species distribution modelling as a macroecological tool: A case study using New World amphibians. Ecography 35, 539–548 (2012).
Article  Google Scholar 

57.
Keating, K. A., Gogan, P. J. P., Vore, J. M. & Irby, L. R. A simple solar radiation index for wildlife habitat studies. J. Wildl. Manage. 71, 1344–1348 (2007).
Article  Google Scholar 

58.
Jenness, J., Brost, B. & Beier, P. Land Facet Corridor Designer: Extension for ArcGIS. Jenness Enterprises. http://www.jennessent.com/arcgis/land_facets.htm. (2013).

59.
Marnell, F. Discriminant analysis of the terrestrial and aquatic habitat determinants of the smooth newt (Triturus vulgaris) and the common frog (Rana temporaria) in Ireland. J Zool 244, 1–6 (2001).
Article  Google Scholar 

60.
Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
Article  Google Scholar 

61.
Warren, D. L., Wright, A. N., Seifert, S. N. & Shaffer, H. B. Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 C alifornia vertebrate species of concern. Divers. Distrib. 20, 334–343 (2014).
Article  Google Scholar 

62.
Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
Article  Google Scholar 

63.
Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: Complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).
Article  Google Scholar 

64.
Morales, N. S., Fernández, I. C. & Baca-González, V. MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations? A systematic review. PeerJ 5, e3093 (2017).
PubMed  PubMed Central  Article  Google Scholar 

65.
Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Modell. 269, 9–17 (2013).
Article  Google Scholar 

66.
Moreno-Amat, E. et al. Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data. Ecol. Model. 312, 308–317 (2015).
Article  Google Scholar 

67.
Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
Article  Google Scholar 

68.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
Article  Google Scholar 

69.
Schoener, T. W. The Anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
Article  Google Scholar 

70.
Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).
PubMed  Article  PubMed Central  Google Scholar 

71.
Lee, C. K. F., Keith, D. A., Nicholson, E. & Murray, N. J. Redlistr: tools for the IUCN Red Lists of ecosystems and threatened species in R. Ecography 42, 1050–1055 (2019).
Article  Google Scholar  More

1.
Almeida, A. C. & Landsberg, J. J. Evaluating methods of estimating global radiation and vapor pressure deficit using a dense network of automatic weather stations in coastal Brazil. Agric. For. Meteorol. 118, 237–250 (2003).
ADS  Article  Google Scholar 
2.
Hashimoto, H. et al. Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data. Remote Sens. Environ. 112, 142–155 (2008).
ADS  Article  Google Scholar 

3.
Silva, L. C. R. & Lambers, H. Soil-plant-atmosphere interactions : structure, function, and predictive scaling for climate change mitigation. Plant Soil https://doi.org/10.1007/s11104-020-04427-1 (2020).
Article  Google Scholar 

4.
Maxwell, T. M. & Silva, L. C. R. A state factor model for ecosystem carbon: water relations. Trends Plant Sci. 25, 652–660 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Penuelas, J. & Sardans, J. Developing holistic models of the structure and function of the soil/plant/atmosphere continuum. Plant Soil https://doi.org/10.1007/s11104-020-04641-x (2020).
Article  PubMed  PubMed Central  Google Scholar 

6.
Seager, R. et al. Climatology, variability, and trends in the U.S. vapor pressure deficit, an important fire-related meteorological quantity. J. Appl. Meteorol. Climatol. 54, 1121–1141 (2015).
ADS  Article  Google Scholar 

7.
Retallack, G. J. Greenhouse crises of the past 300 million years. Bull. Geol. Soc. Am. 121, 1441–1455 (2009).
CAS  Article  Google Scholar 

8.
Barbour, M. M., Walcroft, A. S. & Farquhar, G. D. Seasonal variation in δ13C and δ18O of cellulose from growth rings of Pinus radiata. Plant. Cell Environ. 25, 1483–1499 (2002).
Article  Google Scholar 

9.
Breecker, D. O., Sharp, Z. D. & McFadden, L. D. Seasonal bias in the formation and stable isotopic composition of pedogenic carbonate in modern soils from central New Mexico, USA. Bull. Geol. Soc. Am. 121, 630–640 (2009).
CAS  Article  Google Scholar 

10.
Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537 (1989).
CAS  Article  Google Scholar 

11.
Cerling, T. E. Use of carbon isotopes in paleosols as an indicator of the P(CO2) of the paleoatmosphere. Global Biogeochem. Cycles 6, 307–314 (1992).
ADS  CAS  Article  Google Scholar 

12.
Scheidegger, Y., Saurer, M., Bahn, M. & Siegwolf, R. Linking stable oxygen and carbon isotopes with stomatal conductance and photosynthetic capacity: a conceptual model. Oecologia 125, 350–357 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Maxwell, T. M., Silva, L. C. R. & Horwath, W. R. Using multielement isotopic analysis to decipher drought impacts and adaptive management in ancient agricultural systems: Fig. 1. Proc. Natl. Acad. Sci. 111, E4807–E4808 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Barbour, M. M. & Farquhar, A. Relative humidity- and ABA-induced variation in carbon and oxygen isotope ratios of cotton leaves. Plant Cell Environ. https://doi.org/10.1046/j.1365-3040.2000.00575.x (2000).
Article  Google Scholar 

15.
Roden, J. S., Lin, G. & Ehleringer, J. R. A mechanistic model for interpretation of hydrogen and oxygen isotope ratios in tree-ring cellulose. Geochim. Cosmochim. Acta 64, 21–35 (2000).
ADS  CAS  Article  Google Scholar 

16.
Roden, J. S. & Farquhar, G. D. A controlled test of the dual-isotope approach for the interpretation of stable carbon and oxygen isotope ratio variation in tree rings. Tree Physiol. 32, 490–503 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Saurer, M., Aellen, K. & Siegwolf, R. Correlating δ13C and δ18O in cellulose of trees. Plant Cell Environ. 20, 1543–1550 (1997).
Article  Google Scholar 

18.
Johnstone, J. A., Roden, J. S. & Dawson, T. E. Oxygen and carbon stable isotopes in coast redwood tree rings respond to spring and summer climate signals. J. Geophys. Res. Biogeosciences 118, 1438–1450 (2013).
ADS  CAS  Article  Google Scholar 

19.
Sidorova, O. V. et al. Do centennial tree-ring and stable isotope trends of Larix gmelinii (Rupr.) Rupr. indicate increasing water shortage in the Siberian north?. Oecologia 161, 825–835 (2009).
ADS  PubMed  Article  PubMed Central  Google Scholar 

20.
Yakir, D. & Sternberg, L. D. S. L. The use of stable isotopes to study ecosystem gas exchange. Oecologia 123, 297–311 (2000).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
McCarroll, D. & Loader, N. J. Stable isotopes in tree rings. Quat. Sci. Rev. 23, 771–801 (2004).
ADS  Article  Google Scholar 

22.
Koch, P. L. Isotopic reconstruction of past continental environments. Annu. Rev. Earth Planet. Sci. 26, 573–613 (1998).
ADS  CAS  Article  Google Scholar 

23.
Hook, B. A., Halfar, J., Gedalof, Z., Bollmann, J. & Schulze, D. J. Stable isotope paleoclimatology of the earliest Eocene using kimberlite-hosted mummified wood from the Canadian Subarctic. Biogeosciences 12, 5899–5914 (2015).
ADS  Article  Google Scholar 

24.
Zhang, H. & Nobel, P. S. Dependency of cI/ca and leaf transpiration efficiency on the vapour pressure deficit. Funct. Plant Biol. 23, 561–568 (1996).
Article  Google Scholar 

25.
Silva, L. C. R., Pedroso, G., Doane, T. A., Mukome, F. N. D. & Horwath, W. R. Beyond the cellulose: oxygen isotope composition of plant lipids as a proxy for terrestrial water balance. Geochemical Perspect. Lett. https://doi.org/10.7185/geochemlet.1504 (2015).
Article  Google Scholar 

26.
Breecker, D. O., Sharp, Z. D. & McFadden, L. D. Atmospheric CO2 concentrations during ancient greenhouse climates were similar to those predicted for A.D. 2100. Proc. Natl. Acad. Sci. 107, 576–580 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Breecker, D. O., McFadden, L. D., Sharp, Z. D., Martinez, M. & Litvak, M. E. Deep autotrophic soil respiration in shrubland and woodland ecosystems in central New Mexico. Ecosystems 15, 83–96 (2012).
CAS  Article  Google Scholar 

28.
Abels, H. A. et al. Carbon isotope excursions in paleosol carbonate marking five early Eocene hyperthermals in the Bighorn Basin, Wyoming. Clim. Past Discuss. 11, 1857–1885 (2015).
Article  Google Scholar 

29.
Leary, R. J., Quade, J., DeCelles, P. G. & Reynolds, A. Evidence from paleosols for low to moderate elevation of the India-Asia suture zone during mid-Cenozoic time. Geology 45, 399–402 (2017).
ADS  Article  Google Scholar 

30.
Silva, L. C. R. et al. Expansion of gallery forests into central Brazilian savannas. Glob. Chang. Biol. 14, 2108–2118 (2008).
ADS  Article  Google Scholar 

31.
Oerter, E. J. & Amundson, R. Climate controls on spatial temporal variations in the formation of pedogenic carbonate in the western Great Basin of North Americ. Bull. Geol. Soc. Am. 128, 1095–1104 (2016).
Article  Google Scholar 

32.
Quade, J., Cerling, T. E. & Bowman, J. R. Systematic variations in the carbon and oxygen isotopic composition of pedogenic carbonate along elevation trasects in the southern Great Basin, United States. Geol. Soc. Am. Bull. 101, 464–475 (1989).
ADS  CAS  Article  Google Scholar 

33.
Zamanian, K., Pustovoytov, K. & Kuzyakov, Y. Pedogenic carbonates : forms and formation processes. Earth Sci. Rev. 157, 1–17 (2016).
ADS  CAS  Article  Google Scholar 

34.
Botsyun, S. et al. Revised paleoaltimetry data show low Tibetan Plateau elevation during the Eocene. Science 80, 363 (2019).
Google Scholar 

35.
Maxwell, T. M., Silva, L. C. R. & Horwath, W. R. Predictable oxygen isotope exchange between plant lipids and environmental water: implications for ecosystem water balance reconstruction. J. Geophys. Res. Biogeosciences https://doi.org/10.1029/2018JG004553 (2018).
Article  Google Scholar 

36.
Nyachoti, S., Jin, L., Tweedie, C. E. & Ma, L. Insight into factors controlling formation rates of pedogenic carbonates: a combined geochemical and isotopic approach in dryland soils of the US Southwest. Chem. Geol. https://doi.org/10.1016/j.chemgeo.2017.10.014 (2017).
Article  Google Scholar 

37.
Sanyal, P., Bhattacharya, S. K., Kumar, R., Ghosh, S. K. & Sangode, S. J. Mio-Pliocene monsoonal record from Himalayan foreland basin (Indian Siwalik) and its relation to vegetational change. Palaeogeogr. Palaeoclimatol. Palaeoecol. 205, 23–41 (2004).
Article  Google Scholar 

38.
Ufnar, D. F., Gröcke, D. R. & Beddows, P. A. Assessing pedogenic calcite stable-isotope values: Can positive linear covariant trends be used to quantify palaeo-evaporation rates?. Chem. Geol. 256, 46–51 (2008).
ADS  CAS  Article  Google Scholar 

39.
Jahren, A. H. & Sternberg, L. S. L. Annual patterns within tree rings of the Arctic middle Eocene (ca. 45 Ma): isotopic signatures of precipitation, relative humidity, and deciduousness. Geology 36, 99–102 (2008).
ADS  CAS  Article  Google Scholar 

40.
Retallack, G. J., Wynn, J. G. & Fremd, T. J. Glacial-interglacial-scale paleoclimatic change without large ice sheets in the Oligocene of central Oregon. Geology 32, 297–300 (2004).
ADS  Article  Google Scholar 

41.
Howell, T. A. & Dusek, D. Comparison of vapor-pressure-deficit calculation methods: Southern high plains. J. Irrig. Drain. Eng. 121, 191–198 (1995).
Article  Google Scholar 

42.
Castellvi, F., Perez, P. J., Villar, J. M. & Rose, J. I. Analysis of methods for estimating vapor pressure deficits and relative humidity. Agric. For. Meteorol. 82, 29–45 (1996).
ADS  Article  Google Scholar 

43.
Jahren, A. H. & Sternberg, L. S. L. Humidity estimate for the middle Eocene Arctic rain forest. Geology 31, 463–466 (2003).
ADS  Article  Google Scholar 

44.
Schubert, B. A. & Jahren, A. H. The effect of atmospheric CO2 concentration on carbon isotope fractionation in C3 land plants. Geochim. Cosmochim. Acta 96, 29–43 (2012).
ADS  CAS  Article  Google Scholar 

45.
Sheldon, N. D., Retallack, G. J. & Tanaka, S. Geochemical climofunctions from North American soils and application to paleosols across the eocene: oligocene boundary in oregon geochemical climofunctions from North American soils and application to paleosols across the eocene-oligocene boundary in Or. J. Geol. 110, 687–696 (2015).
ADS  Article  Google Scholar 

46.
Retallack, G. J., Bestland, E. & Fremd, T. Eocene and oligocene paleosols of central oregon. Geol. Soc. Am. Spec. Pap. 344, 1–192 (2000).
Google Scholar 

47.
White, P. D. & Schiebout, J. A. Paleogene paleosols of Big Bend National Park, Texas. Spec. Pap. Geol. Soc. Am. 369, 537–550 (2003).
Google Scholar 

48.
Fischer-Femal, B. J. & Bowen, G. J. Coupled carbon and oxygen isotope model for pedogenic carbonates. Geochim. Cosmochim. Acta https://doi.org/10.1016/j.gca.2020.10.022 (2020).
Article  Google Scholar 

49.
Cerling, T. E. & Quade, J. Stable carbon and oxygen isotopes in soil carbonates. Clim. Chang. Cont. Isot. Rec. 78, 78 (1993).
Google Scholar 

50.
Sarangi, V., Agrawal, S. & Sanyal, P. The disparity in the abundance of C4 plants estimated using the carbon isotopic composition of paleosol components. Palaeogeogr. Palaeoclimatol. Palaeoecol. 561, 110068 (2021).
Article  Google Scholar 

51.
Huang, C. M., Wang, C. S. & Tang, Y. Stable carbon and oxygen isotopes of pedogenic carbonates in Ustic Vertisols: Implications for paleoenvironmental change. Pedosphere 15, 539–544 (2005).
CAS  Google Scholar 

52.
Werner, C. et al. Progress and challenges in using stable isotopes to trace plant carbon and water relations across scales. Biogeosciences 9, 3083–3111 (2012).
ADS  CAS  Article  Google Scholar 

53.
Wynn, J. G. & Bird, M. I. C4-derived soil organic carbon decomposes faster than its C3 counterpart in mixed C3/C4 soils. Glob. Chang. Biol. 13, 2206–2217 (2007).
ADS  Article  Google Scholar 

54.
Garzione, C. N., Dettman, D. L. & Horton, B. K. Carbonate oxygen isotope paleoaltimetry: evaluating the effect of diagenesis on paleoelevation estimates for the Tibetan plateau. Palaeogeogr. Palaeoclimatol. Palaeoecol. 212, 119–140 (2004).
Article  Google Scholar 

55.
Rice, C. M. et al. A Devonian auriferous hot spring system, Rhynie, Scotland. J. Geol. Soc. Lond. 152, 229–250 (1995).
CAS  Article  Google Scholar 

56.
Bera, M. K., Sarkar, A., Tandon, S. K., Samanta, A. & Sanyal, P. Does burial diagenesis reset pristine isotopic compositions in paleosol carbonates?. Earth Planet. Sci. Lett. 300, 85–100 (2010).
ADS  CAS  Article  Google Scholar 

57.
Cernusak, L. A. et al. Environmental and physiological determinants of carbon isotope discrimination in terrestrial plants. New Phytol. 200, 950–965 (2013).
CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Vargas, A. I., Schaffer, B., Yuhong, L. & Lobo, S. Testing plant use of mobile vs immobile soil water sources using stable isotope experiments. New Phytol. https://doi.org/10.1111/nph.14616 (2017).
Article  PubMed  PubMed Central  Google Scholar 

59.
Flanagan, L. B. & Farquhar, G. D. Variation in the carbon and oxygen isotope composition of plant biomass and its relationship to water-use efficiency at the leaf- and ecosystem-scales in a northern Great Plains grassland. Plant Cell Environ. 37, 425–438 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

60.
Sheshshayee, M. S. et al. Oxygen isotope enrichment (Δ18O) as a measure of time-averaged transpiration rate. J. Exp. Bot. 56, 3033–3039 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

61.
Sternberg, L., Fernandes, P. & Ellsworth, V. Divergent biochemical fractionation, not convergent temperature , explains cellulose oxygen isotope enrichment across latitudes. 6, (2011).

62.
Retallack, G. J. Field and laboratory tests for recognition of Ediacaran paleosols. Gondwana Res. 36, 94–110 (2016).
Article  CAS  Google Scholar 

63.
Farquhar, G. D. & Cernusak, L. A. Ternary effects on the gas exchange of isotopologues of carbon dioxide. Plant Cell Environ. 35, 1221–1231 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Maxwell, T. M., Silva, L. C. R. & Horwath, W. R. Integrating effects of species composition and soil properties to predict shifts in montane forest carbon–water relations. Proc. Natl. Acad. Sci. 201718864 (2018). https://doi.org/10.1073/pnas.1718864115

65.
Locatelli, E. R. The exceptional preservation of plant fossils: a review of taphonomic pathways and biases in the fossil record. Paleontol. Soc. Pap. 20, 237–258 (2014).
Article  Google Scholar 

66.
Castruita-Esparza, L. U. et al. Coping with extreme events: growth and water-use efficiency of trees in Western Mexico during the driest and wettest periods of the past one hundred sixty years. J. Geophys. Res. Biogeosci. 124, 3419–3431 (2019).
Article  Google Scholar 

67.
Jahren, A. H. The arctic forest of the middle eocene. Annu. Rev. Earth Planet. Sci. 35, 509–540 (2007).
ADS  CAS  Article  Google Scholar 

68.
Falini, F. On the formation of coal deposits of lacustrine origin. Bull. Geol. Soc. Am. 76, 1317–1346 (1965).
Article  Google Scholar  More

1.
Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241 (2002).
Article  Google Scholar 
2.
Boakes, E. H., Wang, J. & Amos, W. An investigation of inbreeding depression and purging in captive pedigreed populations. Heredity (Edinb). 98, 172–182 (2007).
CAS  PubMed  Article  Google Scholar 

3.
Bozzuto, C., Biebach, I., Muff, S., Ives, A. R. & Keller, L. F. Inbreeding reduces long-term growth of Alpine ibex populations. Nat. Ecol. Evol. 3, 1359–1364 (2019).
PubMed  Article  Google Scholar 

4.
Saccheri, I., Kuussaari, M., Kankare, M., Vikman, P. & Hanski, I. Inbreeding and extinction in a butterfly metapopulation. Nature 392, 491–494 (1998).
ADS  CAS  Article  Google Scholar 

5.
Kardos, M., Taylor, H. R., Ellegren, H., Luikart, G. & Allendorf, F. W. Genomics advances the study of inbreeding depression in the wild. Evol. Appl. 9, 1205–1218 (2016).
PubMed  PubMed Central  Article  Google Scholar 

6.
Allendorf, F. W., Luikart, G. & Aitken, S. N. Conservation and the genetics of populations. (Wiley-Blackwell, 2013).

7.
Johnson, H. E., Mills, L. S., Wehausen, J. D., Stephenson, T. R. & Luikart, G. Translating effects of inbreeding depression on component vital rates to overall population growth in endangered bighorn sheep. Conserv. Biol. 25, 1240–1249 (2011).
PubMed  Article  Google Scholar 

8.
Frankham, R. Where are we in conservation genetics and where do we need to go?. Conserv. Genet. 11, 661–663 (2010).
Article  Google Scholar 

9.
Pierson, J. C. et al. Incorporating evolutionary processes into population viability models. Conserv. Biol. 29, 755–764 (2015).
PubMed  Article  Google Scholar 

10.
Huisman, J., Kruuk, L. E. B., Ellisa, P. A., Clutton-Brock, T. & Pemberton, J. M. Inbreeding depression across the lifespan in a wild mammal population. Proc. Natl. Acad. Sci. U. S. A. 113, 3585–3590 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

11.
Grueber, C. E., Laws, R. J., Nakagawa, S. & Jamieson, I. G. Inbreeding depression accumulation across life-history stages of the endangered takahe. Conserv. Biol. 24, 1617–1625 (2010).
PubMed  Article  Google Scholar 

12.
Harrisson, K. A. et al. Lifetime fitness costs of inbreeding and being inbred in a critically endangered bird. Curr. Biol. 29, 2711-2717.e4 (2019).
CAS  PubMed  Article  Google Scholar 

13.
Ralls, K., Ballou, J. D. & Templeton, A. Estimates of lethal equivalents and the cost of inbreeding in mammals. Conserv. Biol. 2, 185–192 (1988).
Article  Google Scholar 

14.
Hoeck, P. E. A., Wolak, M. E., Switzer, R. A., Kuehler, C. M. & Lieberman, A. A. Effects of inbreeding and parental incubation on captive breeding success in Hawaiian crows. Biol. Conserv. 184, 357–364 (2015).
Article  Google Scholar 

15.
Jimenez, J. A., Hughes, K. A., Alaks, G., Graham, L. & Lacy, R. C. An experimental study of inbreeding depression in a natural habitat. Science (80-. ). 266, 271–273 (1994).

16.
Van Oosterhout, C., Zijlstra, W. G., Van Heuven, M. K. & Brakefield, P. M. Inbreeding depression and genetic load in laboratory metapopulations of the butterfly Bicyclus anynana. Evolution (N. Y). 54, 218–225 (2000).

17.
Szulkin, M., Garant, D., Mccleery, R. H. & Sheldon, B. C. Inbreeding depression along a life-history continuum in the great tit. J. Evol. Biol. 20, 1531–1543 (2007).
CAS  PubMed  Article  Google Scholar 

18.
Wolak, M. E., Arcese, P., Keller, L. F., Nietlisbach, P. & Reid, J. M. Sex-specific additive genetic variances and correlations for fitness in a song sparrow (Melospiza melodia) population subject to natural immigration and inbreeding. Evolution (N. Y). 72, 2057–2075 (2018).

19.
Kennedy, E. S., Grueber, C. E., Duncan, R. P. & Jamieson, I. G. Severe inbreeding depression and no evidence of purging in an extremely inbred wild species-the chatham island black robin. Evolution (N. Y). 68, 987–995 (2014).

20.
Jamieson, I. G., Tracy, L. N., Fletcher, D. & Armstrong, D. P. Moderate inbreeding depression in a reintroduced population of North Island robins. Anim. Conserv. 10, 95–102 (2007).
Article  Google Scholar 

21.
Norén, K., Godoy, E., Dalén, L., Meijer, T. & Angerbjörn, A. Inbreeding depression in a critically endangered carnivore. Mol. Ecol. https://doi.org/10.1111/mec.13674 (2016).
Article  PubMed  Google Scholar 

22.
Sæther, B. E. & Bakke, Ø. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).
Article  Google Scholar 

23.
Beissinger, S. R. & McCullough, D. R. Population viability analysis. (University of Chicago Press, 2002).

24.
Lacy, R. C. Lessons from 30 years of population viability analysis of wildlife populations. Zoo Biol. 38, 67–77 (2019).
PubMed  Article  Google Scholar 

25.
Traill, L. W., Bradshaw, C. J. A. & Brook, B. W. Minimum viable population size: A meta-analysis of 30 years of published estimates. Biol. Conserv. 139, 159–166 (2007).
Article  Google Scholar 

26.
O’Grady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133, 42–51 (2006).
Article  Google Scholar 

27.
Lacy, R. C., Miller, P. S. & Traylor-Holzer, K. Vortex 10 user’s manual. (2017).

28.
Ballou, J. D. & Lacy, R. C. in Population management for survival and recovery (eds. Ballou, J. D., Gilpin, M. & Foose, T. J.) 76–111 (Columbia University Press, 1995).

29.
Armbruster, P. & Reed, D. H. Inbreeding depression in benign and stressful environments. Heredity (Edinb). 95, 235–242 (2005).
CAS  PubMed  Article  Google Scholar 

30.
Fox, C. W. & Reed, D. H. Inbreeding depression increases with environmental stress: an experimental study and meta-analysis. Evolution (N. Y). 65, 246–258 (2011).

31.
Baker, R. H. The avifauna of Micronesia, its origin, evolution and distribution. (University of Kansas Publications, 1951).

32.
Marshall, J. T. The endemic avifauna of Sapan, Tinian Guam and Palau. Condor 51, 200–221 (1949).
Article  Google Scholar 

33.
Wiles, G. J., Bart, J., Beck, R. E. & Aguon, C. F. Impacts of the brown tree snake: patterns of decline and species persistence in Guam’s avifauna. Conserv. Biol. 17, 1350–1360 (2003).
Article  Google Scholar 

34.
Savidge, J. A. Extinction of an island forest avifauna by an introduced snake. Ecology 68, 660–668 (1987).
Article  Google Scholar 

35.
Haig, S. M., Ballou, J. D. & Casna, N. J. Genetic identification of kin in Micronesian kingfishers. J. Hered. 86, 423–431 (1995).
Article  Google Scholar 

36.
Lacy, R. C., Ballou, J. D. & Pollak, J. P. PMx: Software package for demographic and genetic analysis and management of pedigreed populations. Methods Ecol. Evol. 3, 433–437 (2012).
Article  Google Scholar 

37.
Ferrie, G. Using molecular genetic and demographic tools to improve management of ex situ avian populations. (University of Central Florida, 2017). http://stars.library.ucf.edu/etd/5709

38.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Article  Google Scholar 

39.
Burnham, K. . & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. (Springer, 2002).

40.
Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour?. J. Anim. Ecol. 75, 1182–1189 (2006).
PubMed  Article  Google Scholar 

41.
Nietlisbach, P., Muff, S., Reid, J. M., Whitlock, M. C. & Keller, L. F. Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load. Evol. Appl. 12, 266–279 (2018).
PubMed  PubMed Central  Article  Google Scholar 

42.
Zou, G. A modified poisson regression approach to prospective studies with binary data. Am. J. Epidemiol. 159, 702–706 (2004).
PubMed  Article  Google Scholar 

43.
Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
Article  Google Scholar 

44.
R Development Core Team. R: A language and environment for statistical computing. (2019).

45.
Lacy, R. C. & Pollak, J. P. Vortex: A stochastic simulation of the extinction process. (2017).

46.
Hemmings, N. L., Slate, J. & Birkhead, T. R. Inbreeding causes early death in a passerine bird. Nat. Commun. 3, 1–4 (2012).
Article  CAS  Google Scholar 

47.
Tiira, K., Piironen, J. & Primmer, C. R. Evidence for reduced genetic variation in severely deformed juvenile salmonids. Can. J. Fish. Aquat. Sci. 63, 2700–2707 (2006).
Article  Google Scholar 

48.
Wang, J., Hill, W. G., Charlesworth, D. & Charlesworth, B. Dynamics of inbreeding depression due to deleterious mutations in small populations: mutation parameters and inbreeding rate. Genet. Res. 74, 165–178 (1999).
CAS  PubMed  Article  Google Scholar 

49.
Husband, B. C. & Schemske, D. W. Evolution of the magnitude and timing of inbreeding depression in plants. Evolution (N. Y). 50, 54–70 (1996).

50.
de Boer, R. A., Eens, M. & Müller, W. Sex-specific effects of inbreeding on reproductive senescence. Proc. R. Soc. B Biol. Sci. 285, (2018).

51.
Keller, L. F., Reid, J. M. & Arcese, P. Testing evolutionary models of senescence in a natural population: Age and inbreeding effects on fitness components in song sparrows. Proc. R. Soc. B Biol. Sci. 275, 597–604 (2008).
CAS  Article  Google Scholar 

52.
Partridge, L. & Mangel, M. Messages from mortality: The evolution of death rates in the old. Trends Ecol. Evol. 14, 438–442 (1999).
CAS  PubMed  Article  Google Scholar 

53.
Charlesworth, B. & Hughes, K. A. Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence. Proc. Natl. Acad. Sci. U. S. A. 93, 6140–6145 (1996).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Kristensen, T. N., Loeschcke, V. & Hoffmann, A. A. Linking inbreeding effects in captive populations with fitness in the wild: Release of replicated Drosophila melanogaster lines under different temperatures. Conserv. Biol. 22, 189–199 (2008).
PubMed  Article  Google Scholar 

55.
Ryman, N. & Laikre, L. Effects of supportive breeding on the genetically effective population size. Conserv. Biol. 5, 325–329 (1991).
Article  Google Scholar 

56.
Hedrick, P. W. & Garcia-Dorado, A. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31, 940–952 (2016).
PubMed  Article  Google Scholar 

57.
Kalinowski, S. T., Hedrick, P. W. & Miller, P. S. Inbreeding Depression in the Speke’s Gazelle Captive Breeding Program. Conserv. Biol. 14, 1375–1384 (2000).
Article  Google Scholar 

58.
Gilligan, D. M. & Frankham, R. Dynamics of individual adaptation processes. Conserv. Genet. 4, 189–197 (2003).
Article  Google Scholar 

59.
Christie, M. R., Marine, M. L., French, R. A. & Blouin, M. S. Genetic adaptation to captivity can occur in a single generation. Proc. Natl. Acad. Sci. U. S. A. 109, 238–242 (2012).
ADS  CAS  PubMed  Article  Google Scholar 

60.
Grueber, C. E., Waters, J. M. & Jamieson, I. G. The imprecision of heterozygosity-fitness correlations hinders the detection of inbreeding and inbreeding depression in a threatened species. Mol. Ecol. 20, 67–79 (2011).
PubMed  Article  Google Scholar 

61.
Milligan, M. C., Wells, S. L. & McNew, L. B. A population viability analysis for sharp-tailed grouse to inform reintroductions. J. Fish Wildl. Manag. 9, 565–581 (2018).
Article  Google Scholar 

62.
Research needs & implications for population management. Moßbrucker, A. M., Imron, M. A., Pudtatmoko, S., Pratje, P. & Sumardi. Modelling the fate of Sumatran elephants in Bukit Tigapuluh, Indonesia. J. For. Sci. 10, 5–18 (2016).
Google Scholar 

63.
Sharpe, M. & Berggren, P. Indian Ocean humpback dolphin in the Menai Bay off the south coast of Zanzibar, East Africa is Critically Endangered. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 2133–2146 (2019).
Article  Google Scholar 

64.
McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359–372 (2008).
CAS  PubMed  PubMed Central  Article  Google Scholar 

65.
Caballero, A., Bravo, I. & Wang, J. Inbreeding load and purging: Implications for the short-term survival and the conservation management of small populations. Heredity (Edinb). 118, 177–185 (2017).
CAS  PubMed  Article  Google Scholar 

66.
Liao, W. & Reed, D. H. Inbreeding-environment interactions increase extinction risk. Anim. Conserv. 12, 54–61 (2009).
CAS  Article  Google Scholar 

67.
Melbourne, B. A. & Hastings, A. Extinction risk depends strongly on factors contributing to stochasticity. Nature 454, 100–103 (2008).
ADS  CAS  PubMed  Article  Google Scholar  More

1.
International Network for Bamboo and Rattan. Annual Report. www.Inbar.int. (2005).
2.
Sharma, M. L. & Nirmala, C. Bamboo diversity of India: An Update. Conference Paper. 10th World Bamboo Congress, Korea (2015).

3.
Environment and Forest Department, Government of Mizoram. Bamboos of Mizoram (2010).

4.
Jha, L. K. Bamboo based agroforestry systems to reclaim degraded hilly tracts (jhum) land in North Eastern India: Study on uses, species diversity, distribution, and growth performance of Melocanna baccifera, Dendrocalamus hamiltonii, D. longispathus and Bambusa tulda in natural stands and in stands managed on a sustainable basis. Bamboo Science and Culture. J. Am. Bamboo Soc. 23(1), 1–28 (2010).
Google Scholar 

5.
Nigatu, A., Wondei, M., Alemu, A., Gebeyehu, D. & Workagegnehu, H. Productivity of highland bamboo (Yushania alpina) across different plantation niches in West Amhara, Ethiopia. Forest Sci. Tech. 16, 116–122 (2020).
Article  Google Scholar 

6.
Quiroga, R. A. R., Li, T., Lora, G. & Anderson L. E. A measurement of the carbon sequestration potential of Guadua angustifolia in the Carrasco National Park Bolivia. Development Research Working Paper Series 04/2013. Institute for Advanced Development Studies. Bolivia (2013).

7.
Nath, A. J., Lal, R. & Das, A. K. Managing woody bamboos for carbon farming and carbon trading. Glob. Ecol. Conserv. 3, 654–663 (2015).
Article  Google Scholar 

8.
Wu, W., Liu, Q., Zhu, Z. & Shen, Y. Managing bamboo for carbon sequestration, bamboo stem and bamboo shoots. Small Scale Forest. 14, 233–243 (2015).
Article  Google Scholar 

9.
Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllosatchys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344 (2010).
Article  Google Scholar 

10.
Singnar, P., Das, M. C., Sileshi, G. W., Brahma, B. & Nath, A. J. Allometric scalling, biomass accumulation and carbon stocks in different aged stands of thin-walled bamboos Schiostachyum dullooa, Pseudostachyum polymorphum and Melocanna baccifera. For. Ecol. Manag. 395, 81–91 (2017).
Article  Google Scholar 

11.
Directorate of Science and Technology. Climate profile of Mizoram. A publication by Mizoram State Climate Change Cell, 23 (2018).

12.
Soil Survey Staff. Soil taxonomy A basic system of soil classification for making and interpreting soil surveys. U. S. Department of Agriculture Handbook p 436 (1999).

13.
Houba, V., VanderLee, J., Novozamsky, I. & Wallinga, I. Soil and plant analysis. A series of Syllabi Part 5. Soil Analysis Procedures Fourth Edition Wageningen, Netherlands (1989).

14.
Banik, R. L. Silviculture and Field-Guide to Priority Bamboos of Bangladesh and South Asia. Government of the people’s Republic of Bangladesh. Forest Research Institute, Chittagong, 187. (2000).

15.
FAO. Guidelines on Destructive Measurement for Forest Biomass Estimation (FAO, Rome, 2012).
Google Scholar 

16.
IPCC Good Practice Guidance for LULUCF Sector. Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, 2003).
Google Scholar 

17.
Yuming, Y., Chaomao, H., Jiarong, X. & Fan, D. Techniques of cultivation and integrated development of sympodial bamboo species. In Sustainable Development of Bamboo and Rattan Sectors in Tropical China 48–66 (China Forestry Publishing House, Beijing, 2001).
Google Scholar 

18.
Embaye, K., Weih, M., Ledin, S. & Christersson, L. Biomass and nutrient distribution in a highland bamboo forest in southwest Ethiopia: Implications for management. For. Ecol. Manag. 204, 159–169 (2005).
Article  Google Scholar 

19.
Nath, A. J. & Das, A. K. Carbon pool and sequestration potential of village bamboos in the agroforestry system of northeastern India. Trop. Ecol. 53, 287–293 (2012).
CAS  Google Scholar 

20.
Amoah, M., Assan, F. & Dadzie, K. P. Aboveground biomass, carbon storage and fuel values of Bambusa vulgaris, Oxynanteria abbyssinica and Bambusa vulgaris Var. vitata plantations in the Bobiri forest reserve of Ghana. J. Sustain. For. 38, 1–24 (2019).
Article  Google Scholar 

21.
Xu, M., Ji, H. & Zhuang, S. Carbon stock of Moso bamboo (Phyllostachys pubescens) forests along a latitude gradient in the subtropical region of China. PLoS One 13, 2,e0193024 (2018).
Google Scholar 

22.
Majumdar, K., Choudhary, B. K. & Datta, B. K. Aboveground woody biomass carbon stocks potential in selected tropical forest patches of Tripura, Northeast India. Open J. Ecol. 6, 598–612 (2016).
Article  Google Scholar 

23.
Pathak, P. K., Kumar, H., Kumari, G. & Bilyaminu, H. Biomass production potential in different species of hemicelluloses from bamboo in central Utter Pradesh. Ecoscan 10, 41–43 (2016).
Google Scholar 

24.
Sohel, M. S. I., Alamgir, M., Akhter, S. & Rahman, M. Carbon storage in a bamboo (Bambusa vulgaris) plantation in the degraded tropical forest: Implications for policy development. Land Use Policy 49, 142–151 (2015).
Article  Google Scholar 

25.
Thokchom, A. & Yadava, P. S. Comparing aboveground  carbon sequestration between bamboo forest and Dipterocarpus forests of Manipur, Northeast India. Int. J. Ecol. Environ. Sci. 41, 33–42 (2015).
Google Scholar 

26.
Xu, L. et al. Structural development and carbon dynamics of Moso bamboo forests in Zhejiang Province, China. For. Ecol. Manag. 409, 479–488 (2017).
Article  Google Scholar 

27.
Nath, A. J., Das, G. & Das, A. K. Aboveground standing biomass and carbon storage in village bamboos in North East India. Biom. Bioeng. 33, 1188–1196 (2009).
Article  Google Scholar 

28.
Wang, Y. C. Estimates of biomass and carbon sequestration in Dendrocalamus latiflorus culms. J. For. Prod. Ind. 23(1), 13–22 (2004).
Google Scholar 

29.
Wang, J. et al. The structures, aboveground biomass, carbon storage of Phyllostachys pubescens stands in Huisun Experimental Forest Station and Shi-Zhuo. Q. For. Res. 31, 17–26 (2009).
CAS  Google Scholar 

30.
Sujarwo, W. Stand biomass and carbon storage of bamboo forest in Penglipuram traditional village, Bali (Indonesia). J. For. Res. 27, 913–917 (2016).
CAS  Article  Google Scholar 

31.
Nfornkah, B. N. et al. Culm allometry and carbon storage capacity of Bambusa vulgaris Schrad. ex J. C. WendL. in the tropical evergreen rain forest of Cameroon. J Sustain For. https://doi.org/10.1080/10549811.2020.1795688 (2020).
Article  Google Scholar  More

Monkey conflict data
We obtained social conflict data ofNorthern China Rhesus Monkeys from Hongshan Forest Zoo of Nanjing, China. Nanjing (31° 14′–32° 37′ N, 118° 22′–119° 14′ E) is located in the central region of the lower Yangtze River and southwest of Jiangsu Province. It is an important national gateway city for the development of the central and western regions in the Yangtze River Delta, with an area of 6587 km2 covering a population of more than 8 Million. Average annual temperature is about 15.4 °C. Annual precipitation is 1106 mm, 60% of which occurs from Jun to Sep.
There are about 90 monkeys in the Hongshan Zoo in 2017, about 35 adults, 20 sub-adults and 35 juveniles or new-borns. The round monkey park was located in the central part of the zoo, with an area of about 2000 m2. Although a thick and 3-m high glass wall has been built to prevent artificial feedings, visitors sometimes throw food into the monkey park, causing a social conflict due to the food competition. Usually the zookeeper feeds these monkeys twice a day at about 9:30 am and 3:30 pm respectively.
We established a monitoring camera web (Haikang DS-7104N-SN/P) covering the monkey park in September 2016 and video-recorded the whole population since then. We defined social conflicts of monkeys as aggressive or fighting behaviors between individuals, including chasing (one chases another until it escapes), wrestling (one grapples and wrestles with another, until one escapes or gives up), biting (one opens its mouth and bites or tries to bites another), scratching (One scratches or scrapes another using its hands), threating (One warns or threats another through calling or behavioural display), etc. The age of participants and the occurrence time were recorded for each aggression46. We considered a conflict ends if there is no continuation within 10 s after the aggression. Since these monkeys are inactive during the night, we only recorded their diurnal aggressive behaviors from 6:30 till 18:30 and then summed the fights as daily social conflicts. One-year round data were collected from Mar 2017 to Feb 2018.
Air Quality Index
We obtained Air Quality Index (AQI) data of Nanjing from the Data Centre of the Ministry of Environmental Protection of the People’s Republic of China (MEP, http://datacenter.mep.gov.cn/)17. Based on established criteria (GB3095-2012). AQI is calculated for six major air pollutants separately: particle matter  More